************************************************************************************************************************************************************************************************************************** Random Seed 12 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Test R^2 = 0.09508693464761053, Difference Score = 1.5086033062395885, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06453773690287523, Difference Score = 1.7620831759640412, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05982198354930035, Difference Score = 1.8356579405765978, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04689181937401821, Difference Score = 1.979172238667149, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=7, DecisionTreeRegressor__min_samples_split=10) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.6686671559491137, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.004757087965423845, Difference Score = 3.981515521870891, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) ********************************************************************************************************************************************************************************* Entered Generation: 2 Generation 2 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09554251528172586, Difference Score = 1.557508486950291, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08873946877703043, Difference Score = 1.708742554082063, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06453773690287523, Difference Score = 1.7620831759640412, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06251080585857172, Difference Score = 1.8234793861426963, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05982198354930035, Difference Score = 1.8356579405765978, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04883016096725312, Difference Score = 2.154376170656394, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04752708842051223, Difference Score = 2.2775730057414605, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=13, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.6686671559491137, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ******************************************************************************************************************************************************************** Entered Generation: 3 Generation 3 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09737663831222876, Difference Score = 1.3690084239533236, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09554251528172586, Difference Score = 1.557508486950291, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09526819809509879, Difference Score = 1.6601390708457995, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916467509393375, Difference Score = 1.761929753475856, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08948001409258854, Difference Score = 1.7897460690450047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05903240219812633, Difference Score = 2.255679844319983, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04946625653950021, Difference Score = 2.3099149599837796, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=13, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030288995738109947, Difference Score = 4.122252517025003, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=10)) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ************************************************************************************************************************************************************************* Entered Generation: 4 Generation 4 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09526819809509879, Difference Score = 1.6601390708457995, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916467509393375, Difference Score = 1.761929753475856, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08948001409258854, Difference Score = 1.7897460690450047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05916765295483706, Difference Score = 1.9031992517607748, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05903240219812633, Difference Score = 2.255679844319983, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030288995738109947, Difference Score = 4.122252517025003, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=10)) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ******************************************************************************************************************************************************************** Entered Generation: 5 Generation 5 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0994228591471158, Difference Score = 1.4032975612101621, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09526819809509879, Difference Score = 1.6601390708457995, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09362903045587412, Difference Score = 1.7058916812344151, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09267082097215573, Difference Score = 1.7267064628402096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09202857417526045, Difference Score = 1.7639695168495098, Pipeline: RandomForestRegressor(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08986334868817536, Difference Score = 1.7768580490378705, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08948001409258854, Difference Score = 1.7897460690450047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05903240219812633, Difference Score = 2.255679844319983, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030288995738109947, Difference Score = 4.122252517025003, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=10)) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ****************************************************************************************************************************************************************************** Entered Generation: 6 Generation 6 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09526819809509879, Difference Score = 1.6601390708457995, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09362903045587412, Difference Score = 1.7058916812344151, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09267082097215573, Difference Score = 1.7267064628402096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07397795135135787, Difference Score = 1.9272953583231227, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030288995738109947, Difference Score = 4.122252517025003, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=10)) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ***************************************************************************************************************************************************************************** Entered Generation: 7 Generation 7 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09526819809509879, Difference Score = 1.6601390708457995, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09362903045587412, Difference Score = 1.7058916812344151, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0931661123376819, Difference Score = 1.708447963707009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09267082097215573, Difference Score = 1.7267064628402096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08393345603243552, Difference Score = 1.8522769232594445, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07397795135135787, Difference Score = 1.9272953583231227, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06992959778708618, Difference Score = 1.9853700265472836, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ****************************************************************************************************************************************************************************** Entered Generation: 8 Generation 8 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08393345603243552, Difference Score = 1.8522769232594445, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07397795135135787, Difference Score = 1.9272953583231227, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06992959778708618, Difference Score = 1.9853700265472836, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.01147394954820391, Difference Score = 4.781109423380067, Pipeline: DecisionTreeRegressor(DominantEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ************************************************************************************************************************************************************ Entered Generation: 9 Generation 9 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08393345603243552, Difference Score = 1.8522769232594445, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0762268465995305, Difference Score = 1.9018000593654665, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07397795135135787, Difference Score = 1.9272953583231227, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06992959778708618, Difference Score = 1.9853700265472836, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.05001847925130487, Difference Score = 2.3198750926525387, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.011473949548204354, Difference Score = 4.781109423380344, Pipeline: DecisionTreeRegressor(VarianceThreshold(RecessiveEncoder(input_matrix), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) *********************************************************************************************************************************************************************** Entered Generation: 10 Generation 10 - Current Pareto Front: Test R^2 = 0.10726203052134375, Difference Score = 1.3482583226307812, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10561755764692471, Difference Score = 1.3933781083168093, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10467104989588627, Difference Score = 1.4017410505539747, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09286820228455661, Difference Score = 1.7444826213874358, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08403585717550643, Difference Score = 1.942414861276681, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06992959778708618, Difference Score = 1.9853700265472836, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.05001847925130487, Difference Score = 2.3198750926525387, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.011473949548204354, Difference Score = 4.781109423380344, Pipeline: DecisionTreeRegressor(VarianceThreshold(RecessiveEncoder(input_matrix), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.006398703527438543, Difference Score = 5.925168543373612, Pipeline: DecisionTreeRegressor(RecessiveEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=10) ***************************************************************************************************************************************************************************** Entered Generation: 11 Generation 11 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10355239278247252, Difference Score = 1.4659700144016512, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(HeterosisEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09396589348347983, Difference Score = 1.7508288757699466, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08776563118130876, Difference Score = 1.830609907869799, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08525784644768819, Difference Score = 1.8431830937996652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08403585717550643, Difference Score = 1.942414861276681, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06992959778708618, Difference Score = 1.9853700265472836, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06895924153908961, Difference Score = 2.028709730677496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.05001847925130487, Difference Score = 2.3198750926525387, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ***************************************************************************************************************************************************************** Entered Generation: 12 Generation 12 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10474189437281245, Difference Score = 1.4958945962806398, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10007649855400735, Difference Score = 1.5000641622329043, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09396589348347983, Difference Score = 1.7508288757699466, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07828638231885565, Difference Score = 2.0353277706999435, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07481677877558746, Difference Score = 2.0557758368397168, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.062136669499195274, Difference Score = 2.120378394896032, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05081469086245227, Difference Score = 2.3127715497436414, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.05001847925130487, Difference Score = 2.3198750926525387, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=20) Test R^2 = 0.046591060911427506, Difference Score = 3.135211431865081, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.0398595491028535, Difference Score = 3.668667155949132, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ************************************************************************************************************************************************************************ Entered Generation: 13 Generation 13 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09396589348347983, Difference Score = 1.7508288757699466, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07828638231885565, Difference Score = 2.0353277706999435, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07481677877558746, Difference Score = 2.0557758368397168, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07060107252382186, Difference Score = 2.0587816584474665, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.062136669499195274, Difference Score = 2.120378394896032, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.006740404721358062, Difference Score = 6.837696729046684, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(RecessiveEncoder(SelectPercentile(RecessiveEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), SelectPercentile__percentile=25)), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) ***************************************************************************************************************************************************************************************** Entered Generation: 14 Generation 14 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09396589348347983, Difference Score = 1.7508288757699466, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07828638231885565, Difference Score = 2.0353277706999435, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07481677877558746, Difference Score = 2.0557758368397168, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07060691566807509, Difference Score = 2.2283850691793194, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.006740404721358062, Difference Score = 6.837696729046684, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(RecessiveEncoder(SelectPercentile(RecessiveEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), SelectPercentile__percentile=25)), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) *********************************************************************************************************************************************************************** Entered Generation: 15 Generation 15 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10208700572695839, Difference Score = 1.5615294300760487, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09396589348347983, Difference Score = 1.7508288757699466, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09348426007164623, Difference Score = 1.7513773704815374, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07828638231885565, Difference Score = 2.0353277706999435, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07481677877558746, Difference Score = 2.0557758368397168, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07209147940923966, Difference Score = 2.2159955329942496, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07060691566807509, Difference Score = 2.2283850691793194, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.006740404721358062, Difference Score = 6.837696729046684, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(RecessiveEncoder(SelectPercentile(RecessiveEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), SelectPercentile__percentile=25)), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) ************************************************************************************************************************************************************************ Entered Generation: 16 Generation 16 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10208700572695839, Difference Score = 1.5615294300760487, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09396589348347983, Difference Score = 1.7508288757699466, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09348426007164623, Difference Score = 1.7513773704815374, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07828638231885565, Difference Score = 2.0353277706999435, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07481677877558746, Difference Score = 2.0557758368397168, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07209147940923966, Difference Score = 2.2159955329942496, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07060691566807509, Difference Score = 2.2283850691793194, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.006740404721358062, Difference Score = 6.837696729046684, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(RecessiveEncoder(SelectPercentile(RecessiveEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), SelectPercentile__percentile=25)), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) ************************************************************************************************************************************************************************** Entered Generation: 17 Generation 17 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10208700572695839, Difference Score = 1.5615294300760487, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10121585373486375, Difference Score = 1.5784700737911423, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09961353946734341, Difference Score = 1.5913031632861678, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09915084787924688, Difference Score = 1.6270291749109784, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08625473634345993, Difference Score = 1.9530432677395138, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07828638231885565, Difference Score = 2.0353277706999435, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07481677877558746, Difference Score = 2.0557758368397168, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07209147940923966, Difference Score = 2.2159955329942496, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07060691566807509, Difference Score = 2.2283850691793194, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05954910533513724, Difference Score = 2.3459362801458843, Pipeline: RandomForestRegressor(VarianceThreshold(DominantEncoder(OverDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.006740404721358062, Difference Score = 6.837696729046684, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(RecessiveEncoder(SelectPercentile(RecessiveEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), SelectPercentile__percentile=25)), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) ********************************************************************************************************************************************************************** Entered Generation: 18 Generation 18 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08625473634345993, Difference Score = 1.9530432677395138, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07922179237670934, Difference Score = 2.0015160362732227, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07837938968757818, Difference Score = 2.078185271627568, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05954910533513724, Difference Score = 2.3459362801458843, Pipeline: RandomForestRegressor(VarianceThreshold(DominantEncoder(OverDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.0143258372765106, Difference Score = 6.359153423349326, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.006740404721358062, Difference Score = 6.837696729046684, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(RecessiveEncoder(SelectPercentile(RecessiveEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), SelectPercentile__percentile=25)), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) ************************************************************************************************************************************************************************************ Entered Generation: 19 Generation 19 - Current Pareto Front: Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08625473634345993, Difference Score = 1.9530432677395138, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07922179237670934, Difference Score = 2.0015160362732227, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07837938968757818, Difference Score = 2.078185271627568, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05954910533513724, Difference Score = 2.3459362801458843, Pipeline: RandomForestRegressor(VarianceThreshold(DominantEncoder(OverDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ********************************************************************************************************************************************************************* Entered Generation: 20 Generation 20 - Current Pareto Front: Test R^2 = 0.10814754174386321, Difference Score = 1.3728301206910578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08798914462164098, Difference Score = 1.9711289908759186, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0849105508164979, Difference Score = 2.0100845708830675, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07837938968757818, Difference Score = 2.078185271627568, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06374741773865333, Difference Score = 2.259139331160239, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05954910533513724, Difference Score = 2.3459362801458843, Pipeline: RandomForestRegressor(VarianceThreshold(DominantEncoder(OverDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.030850911236687817, Difference Score = 4.311617007560114, Pipeline: RandomForestRegressor(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ******************************************************************************************************************************************************************************** Entered Generation: 21 Generation 21 - Current Pareto Front: Test R^2 = 0.10814754174386321, Difference Score = 1.3728301206910578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09015643166533005, Difference Score = 1.8923702056519751, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08798914462164098, Difference Score = 1.9711289908759186, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0849105508164979, Difference Score = 2.0100845708830675, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08166118442747616, Difference Score = 2.086880993996977, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06374741773865333, Difference Score = 2.259139331160239, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05959957200716792, Difference Score = 2.2782028876651417, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05954910533513724, Difference Score = 2.3459362801458843, Pipeline: RandomForestRegressor(VarianceThreshold(DominantEncoder(OverDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.051144137186138305, Difference Score = 4.683998074610716, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=14) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ******************************************************************************************************************************************************************************** Entered Generation: 22 Generation 22 - Current Pareto Front: Test R^2 = 0.10814754174386321, Difference Score = 1.3728301206910578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09539548170579781, Difference Score = 1.7551896118434824, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=1.0, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09015643166533005, Difference Score = 1.8923702056519751, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08798914462164098, Difference Score = 1.9711289908759186, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0849105508164979, Difference Score = 2.0100845708830675, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08166118442747616, Difference Score = 2.086880993996977, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06450062521726863, Difference Score = 2.2986781348945646, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05954910533513724, Difference Score = 2.3459362801458843, Pipeline: RandomForestRegressor(VarianceThreshold(DominantEncoder(OverDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.2), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.051144137186138305, Difference Score = 4.683998074610716, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=14) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ************************************************************************************************************************************************************************* Entered Generation: 23 Generation 23 - Current Pareto Front: Test R^2 = 0.10814754174386321, Difference Score = 1.3728301206910578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09539548170579781, Difference Score = 1.7551896118434824, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=1.0, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09420198271649971, Difference Score = 1.76095749502288, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09088529016533953, Difference Score = 1.8209844501908687, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09015643166533005, Difference Score = 1.8923702056519751, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08798914462164098, Difference Score = 1.9711289908759186, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0849105508164979, Difference Score = 2.0100845708830675, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08166118442747616, Difference Score = 2.086880993996977, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06450062521726863, Difference Score = 2.2986781348945646, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.062326685275460925, Difference Score = 2.4041495186502253, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.051144137186138305, Difference Score = 4.683998074610716, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=14) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ******************************************************************************************************************************************************************************* Entered Generation: 24 Generation 24 - Current Pareto Front: Test R^2 = 0.10814754174386321, Difference Score = 1.3728301206910578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09539548170579781, Difference Score = 1.7551896118434824, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=1.0, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09457959981489206, Difference Score = 1.7810154129513158, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09088529016533953, Difference Score = 1.8209844501908687, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09015643166533005, Difference Score = 1.8923702056519751, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08798914462164098, Difference Score = 1.9711289908759186, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0849105508164979, Difference Score = 2.0100845708830675, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08166118442747616, Difference Score = 2.086880993996977, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06450062521726863, Difference Score = 2.2986781348945646, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.062326685275460925, Difference Score = 2.4041495186502253, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.051144137186138305, Difference Score = 4.683998074610716, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=14) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ***************************************************************************************************************************************************************************************** Entered Generation: 25 Generation 25 - Current Pareto Front: Test R^2 = 0.10814754174386321, Difference Score = 1.3728301206910578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10738722934179235, Difference Score = 1.4458076751164448, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10638213579537281, Difference Score = 1.4472387804625528, Pipeline: RandomForestRegressor(RecessiveEncoder(RecessiveEncoder(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10601722539501424, Difference Score = 1.519481910074644, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.103484641207642, Difference Score = 1.6269066148254783, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10194548857369845, Difference Score = 1.6447398926688601, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09766370238703004, Difference Score = 1.6526689013043743, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09736690079075094, Difference Score = 1.683903802954888, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09606174695358771, Difference Score = 1.730831815507526, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09539548170579781, Difference Score = 1.7551896118434824, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=1.0, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09457959981489206, Difference Score = 1.7810154129513158, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09315005468676418, Difference Score = 1.7994548080742965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09240241170462371, Difference Score = 1.804073297946209, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09088529016533953, Difference Score = 1.8209844501908687, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09015643166533005, Difference Score = 1.8923702056519751, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08845805837194609, Difference Score = 1.9503164456044046, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08798914462164098, Difference Score = 1.9711289908759186, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0849105508164979, Difference Score = 2.0100845708830675, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08166118442747616, Difference Score = 2.086880993996977, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07389698552536828, Difference Score = 2.2367517894881863, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06584885667759965, Difference Score = 2.239177487085749, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06450062521726863, Difference Score = 2.2986781348945646, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.062326685275460925, Difference Score = 2.4041495186502253, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05776680353581831, Difference Score = 2.420328288426097, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0572222445242887, Difference Score = 2.42734554875591, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05536965437654817, Difference Score = 3.914528391315701, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) Test R^2 = 0.051144137186138305, Difference Score = 4.683998074610716, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=14) Test R^2 = 0.027333365951504818, Difference Score = 4.729079139230355, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016807707117026727, Difference Score = 7.211805758381822, Pipeline: LinearRegression(HeterosisEncoder(SelectPercentile(VarianceThreshold(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=55), VarianceThreshold__threshold=0.15), VarianceThreshold__threshold=0.3), SelectPercentile__percentile=65))) ************************************************************************************************************************************************************************************************************************** Random Seed 22 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Test R^2 = 0.08476605009453908, Difference Score = 1.468703346723247, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0840999201075221, Difference Score = 1.6619088206596222, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06385318873804413, Difference Score = 1.808953679327855, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04343778204658211, Difference Score = 2.0197897048824256, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Test R^2 = 0.03111748784503221, Difference Score = 2.114493363899188, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.028226470111847757, Difference Score = 2.8301700260198923, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Test R^2 = 0.004172139228820093, Difference Score = 3.4947729428175243, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) ******************************************************************************************************* Entered Generation: 2 Generation 2 - Current Pareto Front: Test R^2 = 0.08644299275715894, Difference Score = 1.5928371984910312, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0840999201075221, Difference Score = 1.6619088206596222, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07174225646941945, Difference Score = 1.8395932487354354, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06092204290437819, Difference Score = 2.0105453226507626, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04343778204658211, Difference Score = 2.0197897048824256, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Test R^2 = 0.03111748784503221, Difference Score = 2.114493363899188, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.028226470111847757, Difference Score = 2.8301700260198923, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Test R^2 = 0.02429826211913111, Difference Score = 2.956486267823374, Pipeline: LinearRegression(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.35)) Test R^2 = 0.007049379800147748, Difference Score = 3.326350147437229, Pipeline: LinearRegression(SelectPercentile(DominantEncoder(input_matrix), SelectPercentile__percentile=25)) Test R^2 = 0.004172139228820093, Difference Score = 3.4947729428175243, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.001411390992009709, Difference Score = 5.159268910437574, Pipeline: DecisionTreeRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) ************************************************************************************************** Entered Generation: 3 Generation 3 - Current Pareto Front: Test R^2 = 0.08644299275715894, Difference Score = 1.5928371984910312, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0840999201075221, Difference Score = 1.6619088206596222, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0742972500428577, Difference Score = 1.6727200665605932, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07174225646941945, Difference Score = 1.8395932487354354, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06092204290437819, Difference Score = 2.0105453226507626, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04343778204658211, Difference Score = 2.0197897048824256, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Test R^2 = 0.03111748784503221, Difference Score = 2.114493363899188, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.028270648172171176, Difference Score = 2.4820957604542815, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.25)) Test R^2 = 0.028226470111847757, Difference Score = 2.8301700260198923, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Test R^2 = 0.02429826211913111, Difference Score = 2.956486267823374, Pipeline: LinearRegression(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.35)) Test R^2 = 0.007049379800147748, Difference Score = 3.326350147437229, Pipeline: LinearRegression(SelectPercentile(DominantEncoder(input_matrix), SelectPercentile__percentile=25)) Test R^2 = 0.004172139228820093, Difference Score = 3.4947729428175243, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.0012935012232277998, Difference Score = 5.275567039717796, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) ******************************************************************************************* Entered Generation: 4 Generation 4 - Current Pareto Front: Test R^2 = 0.08783371292787678, Difference Score = 1.2943346227109827, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08644299275715894, Difference Score = 1.5928371984910312, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0840999201075221, Difference Score = 1.6619088206596222, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08138759448945632, Difference Score = 1.6984925425319075, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0793520147735357, Difference Score = 1.7029546482668338, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04876169297770183, Difference Score = 2.3188494418255425, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03126207712018303, Difference Score = 2.677695594484235, Pipeline: LinearRegression(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.35))) Test R^2 = 0.028226470111847757, Difference Score = 2.8301700260198923, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Test R^2 = 0.024442570265916008, Difference Score = 2.9393022442184313, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=35)) Test R^2 = 0.02429826211913111, Difference Score = 2.956486267823374, Pipeline: LinearRegression(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.35)) Test R^2 = 0.008575190528220089, Difference Score = 3.403059824281275, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=45)) Test R^2 = 0.004172139228820093, Difference Score = 3.4947729428175243, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.0012935012232277998, Difference Score = 5.275567039717796, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) *************************************************************************************** Entered Generation: 5 Generation 5 - Current Pareto Front: Test R^2 = 0.08783371292787678, Difference Score = 1.2943346227109827, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08754993836050384, Difference Score = 1.5230726545377653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08644299275715894, Difference Score = 1.5928371984910312, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0840999201075221, Difference Score = 1.6619088206596222, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08138759448945632, Difference Score = 1.6984925425319075, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0793520147735357, Difference Score = 1.7029546482668338, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07679010649450035, Difference Score = 1.7413919276848182, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04876169297770183, Difference Score = 2.3188494418255425, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) ********************************************************************************* Entered Generation: 6 Generation 6 - Current Pareto Front: Test R^2 = 0.08879663030974072, Difference Score = 1.3633407614116877, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08754993836050384, Difference Score = 1.5230726545377653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08644299275715894, Difference Score = 1.5928371984910312, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0840999201075221, Difference Score = 1.6619088206596222, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08138759448945632, Difference Score = 1.6984925425319075, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0793520147735357, Difference Score = 1.7029546482668338, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07885325098687856, Difference Score = 1.7433896550852093, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04876169297770183, Difference Score = 2.3188494418255425, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) ******************************************************************************* Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.09647076266863652, Difference Score = 1.3818478321253458, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.08869993640076734, Difference Score = 1.4931875264752534, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.08754993836050384, Difference Score = 1.5230726545377653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.0793520147735357, Difference Score = 1.7029546482668338, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.07885325098687856, Difference Score = 1.7433896550852093, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.07471530507864899, Difference Score = 1.8674419425159436, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.06450457535989196, Difference Score = 2.0257600193227003, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.04876169297770183, Difference Score = 2.3188494418255425, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 31%|███ | 795/2600 [03:20<05:38, 5.34pipeline/s] ************************************************************************************************ Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.09647076266863652, Difference Score = 1.3818478321253458, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.08869993640076734, Difference Score = 1.4931875264752534, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.08754993836050384, Difference Score = 1.5230726545377653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.07985075545142217, Difference Score = 1.8248141595422283, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.07471530507864899, Difference Score = 1.8674419425159436, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.06450457535989196, Difference Score = 2.0257600193227003, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.06324265848870192, Difference Score = 2.0916530971263505, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.05798264216333626, Difference Score = 2.129789011116274, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), SelectPercentile__percentile=55), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.04876169297770183, Difference Score = 2.3188494418255425, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] Test R^2 = -4.061900714735778e-05, Difference Score = 5.833062191289397, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)) Optimization Progress: 34%|███▍ | 895/2600 [03:51<07:28, 3.80pipeline/s] ************************************************************************************************** Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.09647076266863652, Difference Score = 1.3818478321253458, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.08945161716131778, Difference Score = 1.4625695552004334, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.08869993640076734, Difference Score = 1.4931875264752534, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.08836438718297812, Difference Score = 1.5377508040623595, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.07985075545142217, Difference Score = 1.8248141595422283, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.07471530507864899, Difference Score = 1.8674419425159436, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.06854366370065035, Difference Score = 2.0487612470123895, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=55), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.06324265848870192, Difference Score = 2.0916530971263505, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.0601952193566716, Difference Score = 2.17611675056258, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.055298836853523436, Difference Score = 2.226891633032121, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.04876169297770183, Difference Score = 2.3188494418255425, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.04622433933180181, Difference Score = 2.3824711644288294, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(SelectPercentile(OverDominanceEncoder(input_matrix), SelectPercentile__percentile=25))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] Test R^2 = -4.061900714735778e-05, Difference Score = 5.833062191289397, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)) Optimization Progress: 38%|███▊ | 995/2600 [04:21<09:09, 2.92pipeline/s] ************************************************************************************************** Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.09647076266863652, Difference Score = 1.3818478321253458, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.09095607810725814, Difference Score = 1.5525321984322922, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.07985075545142217, Difference Score = 1.8248141595422283, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.07471530507864899, Difference Score = 1.8674419425159436, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.0601952193566716, Difference Score = 2.17611675056258, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.055298836853523436, Difference Score = 2.226891633032121, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.054585572466996024, Difference Score = 2.4311362442343576, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] Test R^2 = -4.061900714735778e-05, Difference Score = 5.833062191289397, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)) Optimization Progress: 42%|████▏ | 1095/2600 [04:54<10:40, 2.35pipeline/s] ************************************************************************************************** Entered Generation: 11 Generation 11 - Current Pareto Front: Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.09647076266863652, Difference Score = 1.3818478321253458, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.09562388051875925, Difference Score = 1.434533405269131, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.09095607810725814, Difference Score = 1.5525321984322922, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.09054360431825592, Difference Score = 1.5920926097402983, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.07985075545142217, Difference Score = 1.8248141595422283, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.07471530507864899, Difference Score = 1.8674419425159436, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.0601952193566716, Difference Score = 2.17611675056258, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.055298836853523436, Difference Score = 2.226891633032121, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.054585572466996024, Difference Score = 2.4311362442343576, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] Test R^2 = -4.061900714735778e-05, Difference Score = 5.833062191289397, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)) Optimization Progress: 46%|████▌ | 1195/2600 [05:24<08:36, 2.72pipeline/s] ************************************************************************************************** Entered Generation: 12 Generation 12 - Current Pareto Front: Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.09647076266863652, Difference Score = 1.3818478321253458, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.09562388051875925, Difference Score = 1.434533405269131, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.09095607810725814, Difference Score = 1.5525321984322922, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.09054360431825592, Difference Score = 1.5920926097402983, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.07985075545142217, Difference Score = 1.8248141595422283, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.0794722993174225, Difference Score = 1.8471658755567488, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.07669024765075783, Difference Score = 1.88652527063367, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.07658533534828238, Difference Score = 1.912473415076192, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=70), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.0755010920507786, Difference Score = 1.9355121752887272, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.07469131103856008, Difference Score = 1.93839752212427, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.0601952193566716, Difference Score = 2.17611675056258, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.05811249648235095, Difference Score = 2.1850820208192565, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.05639956802382773, Difference Score = 2.4410110726715066, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 50%|████▉ | 1295/2600 [05:54<07:46, 2.80pipeline/s] ************************************************************************************************** Entered Generation: 13 Generation 13 - Current Pareto Front: Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.09095607810725814, Difference Score = 1.5525321984322922, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.09054360431825592, Difference Score = 1.5920926097402983, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.07985075545142217, Difference Score = 1.8248141595422283, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.0794722993174225, Difference Score = 1.8471658755567488, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.07675202657856484, Difference Score = 1.9262918893733907, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.0755010920507786, Difference Score = 1.9355121752887272, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.07469131103856008, Difference Score = 1.93839752212427, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), VarianceThreshold__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.05639956802382773, Difference Score = 2.4410110726715066, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.03450960283100901, Difference Score = 2.467780745273348, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 54%|█████▎ | 1395/2600 [06:23<05:01, 3.99pipeline/s] ************************************************************************************************** Entered Generation: 14 Generation 14 - Current Pareto Front: Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.07675202657856484, Difference Score = 1.9262918893733907, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.076348440933096, Difference Score = 2.0098799436914794, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 57%|█████▊ | 1495/2600 [06:45<02:42, 6.78pipeline/s] ************************************************************************************************** Entered Generation: 15 Generation 15 - Current Pareto Front: Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.08264195889496895, Difference Score = 1.7890191022228614, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.07675202657856484, Difference Score = 1.9262918893733907, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.076348440933096, Difference Score = 2.0098799436914794, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.07240378015827709, Difference Score = 2.0140804029706474, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 61%|██████▏ | 1595/2600 [07:00<02:30, 6.70pipeline/s] ************************************************************************************************** Entered Generation: 16 Generation 16 - Current Pareto Front: Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.08542760230684543, Difference Score = 1.705320623641538, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.08264195889496895, Difference Score = 1.7890191022228614, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 65%|██████▌ | 1694/2600 [07:14<01:33, 9.66pipeline/s] ************************************************************************************************** Entered Generation: 17 Generation 17 - Current Pareto Front: Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.08542760230684543, Difference Score = 1.705320623641538, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.08264195889496895, Difference Score = 1.7890191022228614, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 69%|██████▉ | 1794/2600 [07:27<01:31, 8.80pipeline/s] ************************************************************************************************** Entered Generation: 18 Generation 18 - Current Pareto Front: Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.08542760230684543, Difference Score = 1.705320623641538, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 73%|███████▎ | 1894/2600 [07:42<01:03, 11.15pipeline/s] ************************************************************************************************** Entered Generation: 19 Generation 19 - Current Pareto Front: Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.09788494097655343, Difference Score = 1.2972782428318346, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.06969535441044472, Difference Score = 2.1738212681367823, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.06915473214122525, Difference Score = 2.215626876913694, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 77%|███████▋ | 1994/2600 [07:57<01:12, 8.35pipeline/s] ************************************************************************************************** Entered Generation: 20 Generation 20 - Current Pareto Front: Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.09788494097655343, Difference Score = 1.2972782428318346, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.07188081749286335, Difference Score = 2.2307199910704263, Pipeline: RandomForestRegressor(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.05927958149268331, Difference Score = 2.647069265847003, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 81%|████████ | 2094/2600 [08:14<01:16, 6.58pipeline/s] ************************************************************************************************** Entered Generation: 21 Generation 21 - Current Pareto Front: Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.09865594492281304, Difference Score = 1.4459403766605698, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08682452011777042, Difference Score = 1.732936643830725, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08420560592404358, Difference Score = 1.8312082981619855, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.08171570044322474, Difference Score = 1.8812330061152653, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.07188081749286335, Difference Score = 2.2307199910704263, Pipeline: RandomForestRegressor(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.05927958149268331, Difference Score = 2.647069265847003, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 84%|████████▍ | 2194/2600 [08:35<00:48, 8.29pipeline/s] ************************************************************************************************** Entered Generation: 22 Generation 22 - Current Pareto Front: Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.09865594492281304, Difference Score = 1.4459403766605698, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08682452011777042, Difference Score = 1.732936643830725, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08420560592404358, Difference Score = 1.8312082981619855, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.08178801843825434, Difference Score = 1.8985906056536799, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.07188081749286335, Difference Score = 2.2307199910704263, Pipeline: RandomForestRegressor(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.05927958149268331, Difference Score = 2.647069265847003, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 88%|████████▊ | 2294/2600 [08:55<00:51, 5.95pipeline/s] ************************************************************************************************** Entered Generation: 23 Generation 23 - Current Pareto Front: Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.09865594492281304, Difference Score = 1.4459403766605698, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.0923635113462915, Difference Score = 1.6356568223013597, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08682452011777042, Difference Score = 1.732936643830725, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08420560592404358, Difference Score = 1.8312082981619855, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.08178801843825434, Difference Score = 1.8985906056536799, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.07188081749286335, Difference Score = 2.2307199910704263, Pipeline: RandomForestRegressor(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.05927958149268331, Difference Score = 2.647069265847003, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 92%|█████████▏| 2394/2600 [09:13<00:32, 6.29pipeline/s] ************************************************************************************************** Entered Generation: 24 Generation 24 - Current Pareto Front: Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.09865594492281304, Difference Score = 1.4459403766605698, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.0923635113462915, Difference Score = 1.6356568223013597, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08682452011777042, Difference Score = 1.732936643830725, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08420560592404358, Difference Score = 1.8312082981619855, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.08178801843825434, Difference Score = 1.8985906056536799, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.07188081749286335, Difference Score = 2.2307199910704263, Pipeline: RandomForestRegressor(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.05927958149268331, Difference Score = 2.647069265847003, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 96%|█████████▌| 2494/2600 [09:33<00:18, 5.61pipeline/s] ************************************************************************************************** Entered Generation: 25 Generation 25 - Current Pareto Front: Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.10079843066891314, Difference Score = 1.4336153535178837, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09972536273354649, Difference Score = 1.4339216427147259, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09865594492281304, Difference Score = 1.4459403766605698, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09662431392417525, Difference Score = 1.487151572465405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09444352488123198, Difference Score = 1.5254840742936773, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09352089880933445, Difference Score = 1.549781433390869, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09327582346357821, Difference Score = 1.6077667117347354, Pipeline: RandomForestRegressor(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.0923635113462915, Difference Score = 1.6356568223013597, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.09019009320556959, Difference Score = 1.665991853949382, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08742478528403252, Difference Score = 1.7027298671003146, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08682452011777042, Difference Score = 1.732936643830725, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08593752514845343, Difference Score = 1.733340236031228, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08485732686449932, Difference Score = 1.7524363760952617, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08476114279719638, Difference Score = 1.7775736039961396, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.0843451848484954, Difference Score = 1.8024174598405096, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08420560592404358, Difference Score = 1.8312082981619855, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.08178801843825434, Difference Score = 1.8985906056536799, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.07852778493571955, Difference Score = 2.029994262472428, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.07188081749286335, Difference Score = 2.2307199910704263, Pipeline: RandomForestRegressor(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), SelectPercentile__percentile=65), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.05927958149268331, Difference Score = 2.647069265847003, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.05897543354717327, Difference Score = 2.674137418233395, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.058802279797493306, Difference Score = 2.67542425505022, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.058654549912836695, Difference Score = 2.6789538535251896, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(RecessiveEncoder(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35))))), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.040793032137869556, Difference Score = 3.0000727199511914, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=35)), SelectPercentile__percentile=95), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.03382748612038711, Difference Score = 3.796257357390801, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=30)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.009621863917081974, Difference Score = 4.729251744640857, Pipeline: DecisionTreeRegressor(VarianceThreshold(FeatureEncodingFrequencySelector(DominantEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.15), VarianceThreshold__threshold=0.1), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=11, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.007829658668662587, Difference Score = 5.689987974485924, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=20) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.00042500148831081663, Difference Score = 12.898175361268027, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(RecessiveEncoder(DominantEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] Test R^2 = 0.0004250014883105946, Difference Score = 12.898175361287844, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(SelectPercentile(RecessiveEncoder(DominantEncoder(input_matrix)), SelectPercentile__percentile=60), VarianceThreshold__threshold=0.05), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 100%|█████████▉| 2594/2600 [09:53<00:01, 4.82pipeline/s] ************************************************************************************************************************************************************************************************************************** Random Seed 32 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.09210008438021378, Difference Score = 1.4877523181161354, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.08723436244255411, Difference Score = 1.6862002796632791, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.06499291357946213, Difference Score = 1.755142596420958, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.06142667833850579, Difference Score = 1.8295345133323813, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] Test R^2 = 0.007220623100438139, Difference Score = 5.438183664414742, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:24, 3.84pipeline/s] ************************************************************************************************** Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.09210008438021378, Difference Score = 1.4877523181161354, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.08723436244255411, Difference Score = 1.6862002796632791, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.06499291357946213, Difference Score = 1.755142596420958, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.06142667833850579, Difference Score = 1.8295345133323813, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.054033857480647596, Difference Score = 1.9978778105841666, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.007220623100438139, Difference Score = 5.438183664414742, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.0008560999781056511, Difference Score = 5.503608239119241, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10))) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] Test R^2 = 0.0006643009895442864, Difference Score = 8.135260933573605, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=80), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 11%|█▏ | 294/2600 [01:11<06:33, 5.86pipeline/s] ************************************************************************************************** Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.09562754565374232, Difference Score = 1.3387589330415028, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.09350465937944297, Difference Score = 1.4858850676107656, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.09210008438021378, Difference Score = 1.4877523181161354, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.08769180922710662, Difference Score = 1.751253409007259, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.07669369609218513, Difference Score = 1.8843652622135292, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.054033857480647596, Difference Score = 1.9978778105841666, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] Test R^2 = 0.0006643009895442864, Difference Score = 8.135260933573605, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=80), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 15%|█▌ | 392/2600 [01:31<08:10, 4.50pipeline/s] ************************************************************************************************** Entered Generation: 4 Generation 4 - Current Pareto Front: Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.09562754565374232, Difference Score = 1.3387589330415028, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.09350465937944297, Difference Score = 1.4858850676107656, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.09253242673743656, Difference Score = 1.523720330159738, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.08769180922710662, Difference Score = 1.751253409007259, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.08681293422551761, Difference Score = 1.7591973579001023, Pipeline: RandomForestRegressor(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.07669369609218513, Difference Score = 1.8843652622135292, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.054033857480647596, Difference Score = 1.9978778105841666, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 19%|█▉ | 492/2600 [01:56<08:42, 4.03pipeline/s] ************************************************************************************************** Entered Generation: 5 Generation 5 - Current Pareto Front: Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.09562754565374232, Difference Score = 1.3387589330415028, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.09350465937944297, Difference Score = 1.4858850676107656, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.09253242673743656, Difference Score = 1.523720330159738, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.08769180922710662, Difference Score = 1.751253409007259, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.08681293422551761, Difference Score = 1.7591973579001023, Pipeline: RandomForestRegressor(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.08536030836782549, Difference Score = 1.7593254303688723, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.054033857480647596, Difference Score = 1.9978778105841666, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 23%|██▎ | 592/2600 [02:22<15:02, 2.22pipeline/s] ************************************************************************************************** Entered Generation: 6 Generation 6 - Current Pareto Front: Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.09562754565374232, Difference Score = 1.3387589330415028, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.09350465937944297, Difference Score = 1.4858850676107656, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.09253242673743656, Difference Score = 1.523720330159738, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.054033857480647596, Difference Score = 1.9978778105841666, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 27%|██▋ | 691/2600 [02:44<08:57, 3.55pipeline/s] ************************************************************************************************** Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.09659327767604464, Difference Score = 1.3526531350989461, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.09350465937944297, Difference Score = 1.4858850676107656, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.09253242673743656, Difference Score = 1.523720330159738, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.06461364116380774, Difference Score = 2.051121085172145, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.128017636194358, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 30%|███ | 790/2600 [03:01<03:52, 7.77pipeline/s] ************************************************************************************************** Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.0980489371575074, Difference Score = 1.4989279549149315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.09506635245764594, Difference Score = 1.5346375498852647, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.06461364116380774, Difference Score = 2.051121085172145, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.05364803226303694, Difference Score = 2.0870841944779825, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.1280176361945555, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 34%|███▍ | 890/2600 [03:22<05:35, 5.10pipeline/s] ************************************************************************************************** Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.0980489371575074, Difference Score = 1.4989279549149315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.09506635245764594, Difference Score = 1.5346375498852647, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.09098247654396985, Difference Score = 1.6789042331336756, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.07592064558782052, Difference Score = 2.0622081721291927, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.06855958638426474, Difference Score = 2.067955187765008, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.06064686036074485, Difference Score = 2.2222830802710325, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.1280176361945555, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 38%|███▊ | 989/2600 [03:46<02:11, 12.29pipeline/s] ************************************************************************************************** Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.0980489371575074, Difference Score = 1.4989279549149315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.09506635245764594, Difference Score = 1.5346375498852647, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.0918862044647788, Difference Score = 1.6360817565310153, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.09140584016996456, Difference Score = 1.6946107623801188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.08908147097867725, Difference Score = 1.7233037416027885, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.07592064558782052, Difference Score = 2.0622081721291927, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.06855958638426474, Difference Score = 2.067955187765008, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.06064686036074485, Difference Score = 2.2222830802710325, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.1280176361945555, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 42%|████▏ | 1089/2600 [04:15<06:59, 3.60pipeline/s] ************************************************************************************************** Entered Generation: 11 Generation 11 - Current Pareto Front: Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.0980489371575074, Difference Score = 1.4989279549149315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.09506635245764594, Difference Score = 1.5346375498852647, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.0918862044647788, Difference Score = 1.6360817565310153, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.09140584016996456, Difference Score = 1.6946107623801188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.0895300460776729, Difference Score = 1.7275874197785488, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.08627263156860698, Difference Score = 1.93416488021482, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.07592064558782052, Difference Score = 2.0622081721291927, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.06855958638426474, Difference Score = 2.067955187765008, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.06064686036074485, Difference Score = 2.2222830802710325, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.1280176361945555, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.010900782990624913, Difference Score = 9.496540761409728, Pipeline: LinearRegression(DominantEncoder(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15), FeatureEncodingFrequencySelector__threshold=0.2))) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 46%|████▌ | 1189/2600 [04:40<04:01, 5.84pipeline/s] ************************************************************************************************** Entered Generation: 12 Generation 12 - Current Pareto Front: Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.09993720927773908, Difference Score = 1.2804415188209193, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.0980489371575074, Difference Score = 1.4989279549149315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.0954641980440305, Difference Score = 1.499888027116829, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.09506635245764594, Difference Score = 1.5346375498852647, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.0918862044647788, Difference Score = 1.6360817565310153, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.09140584016996456, Difference Score = 1.6946107623801188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.0895300460776729, Difference Score = 1.7275874197785488, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.0886355114216042, Difference Score = 1.873030328074147, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.08627263156860698, Difference Score = 1.93416488021482, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.07889751997408778, Difference Score = 1.9544516081444538, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.07592064558782052, Difference Score = 2.0622081721291927, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.06855958638426474, Difference Score = 2.067955187765008, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.06064686036074485, Difference Score = 2.2222830802710325, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.05334227094994626, Difference Score = 2.392287869827966, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.042414151152083246, Difference Score = 5.1280176361945555, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.016680688244268604, Difference Score = 6.2656138007921856, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=5)) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.010900782990624913, Difference Score = 9.496540761409728, Pipeline: LinearRegression(DominantEncoder(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15), FeatureEncodingFrequencySelector__threshold=0.2))) Optimization Progress: 50%|████▉ | 1289/2600 [05:05<05:02, 4.34pipeline/s] Test R^2 = 0.0008340834999293056, Difference Score = 10.662144244594675, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0), FeatureEncodingFrequencySelector__threshold=0.2)) ************************************************************************************************************************************************************************************************************************** Random Seed 62 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.09738728471585412, Difference Score = 1.181272834086654, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.09654239889269478, Difference Score = 1.396229115045433, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.09564630065295421, Difference Score = 1.6523410982841997, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.09467804103602617, Difference Score = 1.719983240253893, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.06978777503526035, Difference Score = 1.789407911724818, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.06941935548611855, Difference Score = 1.806778670869506, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.06074765510794555, Difference Score = 1.8506230676276054, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.049073156687152286, Difference Score = 2.3275438781746263, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.041867983262232844, Difference Score = 4.513308105995711, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.0384435961536157, Difference Score = 6.094110390888435, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.022343981031847315, Difference Score = 8.104373492628625, Pipeline: LinearRegression(RecessiveEncoder(input_matrix)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.02234198410306132, Difference Score = 8.144478451599475, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=90)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] Test R^2 = 0.0020648712992005214, Difference Score = 9.129236268421298, Pipeline: LinearRegression(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.35)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:30, 3.81pipeline/s] ************************************************************************************************** Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.09961982751397525, Difference Score = 1.259651465119338, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.09900004644192739, Difference Score = 1.4650404422286232, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.09564630065295421, Difference Score = 1.6523410982841997, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.09521210481600739, Difference Score = 1.726574316235373, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.09177589522544294, Difference Score = 1.7553458216715154, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.06978777503526035, Difference Score = 1.789407911724818, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.06941935548611855, Difference Score = 1.806778670869506, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.06257208487996768, Difference Score = 1.9483472599898617, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.05399101600954659, Difference Score = 2.2087014993801013, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.05388774578708655, Difference Score = 2.4768525272548505, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.041867983262232844, Difference Score = 4.513308105995711, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.03860773219536895, Difference Score = 6.558352817651308, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85), VarianceThreshold__threshold=0.25)) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.022343981031847315, Difference Score = 8.104373492628625, Pipeline: LinearRegression(RecessiveEncoder(input_matrix)) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.02234198410306132, Difference Score = 8.144478451599475, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=90)) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] Test R^2 = 0.0020648712992005214, Difference Score = 9.129236268421298, Pipeline: LinearRegression(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.35)) Optimization Progress: 11%|█▏ | 296/2600 [01:09<12:58, 2.96pipeline/s] ************************************************************************************************** Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.10222571839537864, Difference Score = 1.2421557672361723, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.10067684189217385, Difference Score = 1.3130286263967703, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.09979294133847505, Difference Score = 1.341882446924974, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.09900004644192739, Difference Score = 1.4650404422286232, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.09892701828786421, Difference Score = 1.5370697826786297, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.09720543061986842, Difference Score = 1.713007282463459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.09560811013671577, Difference Score = 1.7825555041793135, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.0898678728873179, Difference Score = 1.7895968477815754, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.08769763394192887, Difference Score = 1.895688380766927, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.06257208487996768, Difference Score = 1.9483472599898617, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] Test R^2 = 0.020698507339009686, Difference Score = 11.690597385288044, Pipeline: LinearRegression(RecessiveEncoder(VarianceThreshold(RecessiveEncoder(input_matrix), VarianceThreshold__threshold=0.05))) Optimization Progress: 15%|█▌ | 396/2600 [01:35<06:39, 5.52pipeline/s] ************************************************************************************************** Entered Generation: 4 Generation 4 - Current Pareto Front: Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.10600271834512998, Difference Score = 1.3841356570650742, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.10567256716715534, Difference Score = 1.3896351572905061, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.1004659208260682, Difference Score = 1.601946036503763, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.09720543061986842, Difference Score = 1.713007282463459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.0963268648315787, Difference Score = 1.7785207827759768, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.09560811013671577, Difference Score = 1.7825555041793135, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.08769763394192887, Difference Score = 1.895688380766927, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.06257208487996768, Difference Score = 1.9483472599898617, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.06147830039808777, Difference Score = 2.232237327834249, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.020698507339009686, Difference Score = 11.690597385288044, Pipeline: LinearRegression(RecessiveEncoder(VarianceThreshold(RecessiveEncoder(input_matrix), VarianceThreshold__threshold=0.05))) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] Test R^2 = 0.0014854806901034578, Difference Score = 13.883961444700988, Pipeline: LinearRegression(RecessiveEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=5))) Optimization Progress: 19%|█▉ | 496/2600 [02:06<09:20, 3.75pipeline/s] ************************************************************************************************** Entered Generation: 5 Generation 5 - Current Pareto Front: Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.10600271834512998, Difference Score = 1.3841356570650742, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.10567256716715534, Difference Score = 1.3896351572905061, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.10348663080178522, Difference Score = 1.5078562518801966, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.1004659208260682, Difference Score = 1.601946036503763, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.09820802319487287, Difference Score = 1.6826079731424606, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.09720543061986842, Difference Score = 1.713007282463459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.0963268648315787, Difference Score = 1.7785207827759768, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.09560811013671577, Difference Score = 1.7825555041793135, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.09459739399968736, Difference Score = 1.7898092544483937, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.08916268953689499, Difference Score = 1.9460014950713482, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.06394296112019315, Difference Score = 2.241741659576322, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.020698507339009686, Difference Score = 11.690597385288044, Pipeline: LinearRegression(RecessiveEncoder(VarianceThreshold(RecessiveEncoder(input_matrix), VarianceThreshold__threshold=0.05))) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] Test R^2 = 0.0014854806901034578, Difference Score = 13.883961444700988, Pipeline: LinearRegression(RecessiveEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=5))) Optimization Progress: 23%|██▎ | 596/2600 [02:37<11:18, 2.95pipeline/s] ************************************************************************************************** Entered Generation: 6 Generation 6 - Current Pareto Front: Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.10612297213261723, Difference Score = 1.505261903627666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.10348663080178522, Difference Score = 1.5078562518801966, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.1004659208260682, Difference Score = 1.601946036503763, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.10029154951367492, Difference Score = 1.6118885708496986, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.09820802319487287, Difference Score = 1.6826079731424606, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.09720543061986842, Difference Score = 1.713007282463459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.0963268648315787, Difference Score = 1.7785207827759768, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.09560811013671577, Difference Score = 1.7825555041793135, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.09459739399968736, Difference Score = 1.7898092544483937, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.09070963775896201, Difference Score = 1.9082944407327993, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.08916268953689499, Difference Score = 1.9460014950713482, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.08277082365286492, Difference Score = 2.0214103652210578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.06394296112019315, Difference Score = 2.241741659576322, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.020698507339009686, Difference Score = 11.690597385288044, Pipeline: LinearRegression(RecessiveEncoder(VarianceThreshold(RecessiveEncoder(input_matrix), VarianceThreshold__threshold=0.05))) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] Test R^2 = 0.0014854806901034578, Difference Score = 13.883961444700988, Pipeline: LinearRegression(RecessiveEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=5))) Optimization Progress: 27%|██▋ | 696/2600 [03:01<12:47, 2.48pipeline/s] ************************************************************************************************** Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.10866765614129126, Difference Score = 1.4658779298505955, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.10612297213261723, Difference Score = 1.505261903627666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.10348663080178522, Difference Score = 1.5078562518801966, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.1004659208260682, Difference Score = 1.601946036503763, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.10029154951367492, Difference Score = 1.6118885708496986, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.09820802319487287, Difference Score = 1.6826079731424606, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.09720543061986842, Difference Score = 1.713007282463459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.0963268648315787, Difference Score = 1.7785207827759768, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.09560811013671577, Difference Score = 1.7825555041793135, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.09459739399968736, Difference Score = 1.7898092544483937, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.09070963775896201, Difference Score = 1.9082944407327993, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.08916268953689499, Difference Score = 1.9460014950713482, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.08277082365286492, Difference Score = 2.0214103652210578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.06394296112019315, Difference Score = 2.241741659576322, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] Test R^2 = 0.022541693220270442, Difference Score = 16.384147324340386, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 31%|███ | 795/2600 [03:30<08:08, 3.69pipeline/s] ************************************************************************************************** Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.1099986561246854, Difference Score = 1.500197615591127, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.10612297213261723, Difference Score = 1.505261903627666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.10348663080178522, Difference Score = 1.5078562518801966, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.1004659208260682, Difference Score = 1.601946036503763, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.10029154951367492, Difference Score = 1.6118885708496986, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.0)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.09820802319487287, Difference Score = 1.6826079731424606, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.09806078060303902, Difference Score = 1.7363241532044422, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.09802865851757303, Difference Score = 1.78951511034839, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.09459739399968736, Difference Score = 1.7898092544483937, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.09070963775896201, Difference Score = 1.9082944407327993, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.08916268953689499, Difference Score = 1.9460014950713482, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.08277082365286492, Difference Score = 2.0214103652210578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.06394296112019315, Difference Score = 2.241741659576322, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] Test R^2 = 0.022541693220270442, Difference Score = 16.384147324340386, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 34%|███▍ | 895/2600 [04:02<09:19, 3.04pipeline/s] ************************************************************************************************** Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.1099986561246854, Difference Score = 1.500197615591127, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.10612297213261723, Difference Score = 1.505261903627666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.10348663080178522, Difference Score = 1.5078562518801966, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.1031710531510035, Difference Score = 1.5713672743577198, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.10119350225428081, Difference Score = 1.6361069298150615, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.10094424323931961, Difference Score = 1.6367194418500182, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.09820802319487287, Difference Score = 1.6826079731424606, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.09806078060303902, Difference Score = 1.7363241532044422, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.09802865851757303, Difference Score = 1.78951511034839, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:30<06:38, 4.03pipeline/s] Test R^2 = 0.09459739399968736, Difference Score = 1.7898092544483937, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.09418308286319588, Difference Score = 1.8457298790238494, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.09070963775896201, Difference Score = 1.9082944407327993, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.08916268953689499, Difference Score = 1.9460014950713482, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.08277082365286492, Difference Score = 2.0214103652210578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.07387109817241011, Difference Score = 2.167529907719285, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=70), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.06394296112019315, Difference Score = 2.241741659576322, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.04650756856845639, Difference Score = 3.2566812231020608, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] Test R^2 = 0.022541693220270442, Difference Score = 16.384147324340386, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 38%|███▊ | 995/2600 [04:31<06:38, 4.03pipeline/s] ************************************************************************************************** Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.11089317653918296, Difference Score = 1.5183057083859592, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.10364407926672237, Difference Score = 1.5266237559816693, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.10346309629597794, Difference Score = 1.5798734770504717, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.10268213348947808, Difference Score = 1.610114474276643, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.10119350225428081, Difference Score = 1.6361069298150615, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.10094424323931961, Difference Score = 1.6367194418500182, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.10075800724113193, Difference Score = 1.686694059594994, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09806078060303902, Difference Score = 1.7363241532044422, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09802865851757303, Difference Score = 1.78951511034839, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09459739399968736, Difference Score = 1.7898092544483937, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09453529285659423, Difference Score = 1.8011360539504622, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09418308286319588, Difference Score = 1.8457298790238494, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09070963775896201, Difference Score = 1.9082944407327993, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.09003809248343819, Difference Score = 1.9364208950131945, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.08916268953689499, Difference Score = 1.9460014950713482, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.08277082365286492, Difference Score = 2.0214103652210578, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.07474545836163682, Difference Score = 2.0842701845594274, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(RecessiveEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.07387109817241011, Difference Score = 2.167529907719285, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=70), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.07318264966690158, Difference Score = 2.197547052213633, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.06394296112019315, Difference Score = 2.241741659576322, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.0586498596414482, Difference Score = 2.5383092793678994, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.04895535188519151, Difference Score = 3.2781999071992747, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(OverDominanceEncoder(UnderDominanceEncoder(input_matrix))), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.04324869361128869, Difference Score = 5.66851756393423, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=65)) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.042455381765201694, Difference Score = 8.978515398363395, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3)) Optimization Progress: 42%|████▏ | 1095/2600 [05:00<06:04, 4.13pipeline/s] Test R^2 = 0.022541693220270442, Difference Score = 16.384147324340386, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=80)) ************************************************************************************************************************************************************************************************************************** Random Seed 72 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.09242350068431548, Difference Score = 1.4838594323209346, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.08862846630598797, Difference Score = 1.660117961149599, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.0860923268076782, Difference Score = 1.6686149741836271, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.06377414079395016, Difference Score = 1.7275361428739575, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.06285696010631892, Difference Score = 1.8100833460371284, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.04389304943201355, Difference Score = 2.2540353029050983, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.03969661879986963, Difference Score = 3.814327473322622, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] Test R^2 = 0.007108022796219804, Difference Score = 5.200520677954834, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 8%|▊ | 198/2600 [00:52<12:53, 3.11pipeline/s] ************************************************************************************************** Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.08862846630598797, Difference Score = 1.660117961149599, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.0860923268076782, Difference Score = 1.6686149741836271, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.06428549317825205, Difference Score = 1.6935398469390148, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.06377414079395016, Difference Score = 1.7275361428739575, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.06285696010631892, Difference Score = 1.8100833460371284, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.060396424416515604, Difference Score = 1.9082578187191872, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.04389304943201355, Difference Score = 2.2540353029050983, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.03969661879986963, Difference Score = 3.814327473322622, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.029802186924932972, Difference Score = 3.857288590458921, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = 0.007108022796219804, Difference Score = 5.200520677954834, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:27<09:23, 4.09pipeline/s] ************************************************************************************************** Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.08862846630598797, Difference Score = 1.660117961149599, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.08755356692954164, Difference Score = 1.6742114328811593, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.0857587184805153, Difference Score = 1.7324806681259362, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.06285696010631892, Difference Score = 1.8100833460371284, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.06181246924251249, Difference Score = 1.918688120704686, Pipeline: RandomForestRegressor(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.051532749238110354, Difference Score = 2.1539536175625345, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.04389304943201355, Difference Score = 2.2540353029050983, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.03969661879986963, Difference Score = 3.814327473322622, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.029802186924932972, Difference Score = 3.857288590458921, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = 0.007108022796219804, Difference Score = 5.200520677955045, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=90), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:59<18:10, 2.02pipeline/s] ************************************************************************************************** Entered Generation: 4 Generation 4 - Current Pareto Front: Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.0949219925568241, Difference Score = 1.422110217072822, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.08176073842408138, Difference Score = 1.827611912289205, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.06181246924251249, Difference Score = 1.918688120704686, Pipeline: RandomForestRegressor(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.03969661879986963, Difference Score = 3.814327473322622, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.029802186924932972, Difference Score = 3.857288590458921, Pipeline: LinearRegression(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.2)) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.012660930834563211, Difference Score = 5.114672013327233, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=35), SelectPercentile__percentile=35)) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = 0.007108022796219804, Difference Score = 5.200520677955045, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=90), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:27<07:24, 4.73pipeline/s] ************************************************************************************************** Entered Generation: 5 Generation 5 - Current Pareto Front: Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.0949219925568241, Difference Score = 1.422110217072822, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.08176073842408138, Difference Score = 1.827611912289205, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.8891865925076385, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=19) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = 0.0159884839622374, Difference Score = 5.56537648029759, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=35), SelectPercentile__percentile=50)) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:49<06:07, 5.45pipeline/s] ************************************************************************************************** Entered Generation: 6 Generation 6 - Current Pareto Front: Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.0949219925568241, Difference Score = 1.422110217072822, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.08495551313126048, Difference Score = 1.7668531411986967, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.08176073842408138, Difference Score = 1.827611912289205, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.8891865925076385, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=19) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.033867240519234176, Difference Score = 4.586009729124821, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = 0.0159884839622374, Difference Score = 5.56537648029759, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=35), SelectPercentile__percentile=50)) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:22<09:22, 3.39pipeline/s] ************************************************************************************************** Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.0949219925568241, Difference Score = 1.422110217072822, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.08595792104744449, Difference Score = 1.757448323660665, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.08495551313126048, Difference Score = 1.7668531411986967, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.08356156695573946, Difference Score = 1.7683788073984938, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.08176073842408138, Difference Score = 1.827611912289205, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.8891865925076385, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=19) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.033867240519234176, Difference Score = 4.586009729124821, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = 0.0159884839622374, Difference Score = 5.56537648029759, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=35), SelectPercentile__percentile=50)) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 796/2600 [03:59<07:59, 3.77pipeline/s] ************************************************************************************************** Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.09004048243328955, Difference Score = 1.625293316297429, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.08595792104744449, Difference Score = 1.757448323660665, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.08495551313126048, Difference Score = 1.7668531411986967, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.08369651826617563, Difference Score = 1.8294257740266904, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.8891865925076385, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=19) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.033867240519234176, Difference Score = 4.586009729124821, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = 0.0159884839622374, Difference Score = 5.56537648029759, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(RecessiveEncoder(input_matrix), SelectPercentile__percentile=35), SelectPercentile__percentile=50)) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 896/2600 [04:40<10:00, 2.84pipeline/s] ************************************************************************************************** Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.09004048243328955, Difference Score = 1.625293316297429, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.05942983237845845, Difference Score = 2.197878119609803, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.033867240519234176, Difference Score = 4.586009729124821, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 996/2600 [05:19<08:57, 2.98pipeline/s] ************************************************************************************************** Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.09422519333870316, Difference Score = 1.559036512987213, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.09004048243328955, Difference Score = 1.625293316297429, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.05942983237845845, Difference Score = 2.197878119609803, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.033867240519234176, Difference Score = 4.586009729124821, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1096/2600 [06:03<08:23, 2.99pipeline/s] ************************************************************************************************** Entered Generation: 11 Generation 11 - Current Pareto Front: Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.09422519333870316, Difference Score = 1.559036512987213, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.09141119169545897, Difference Score = 1.640972936951491, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.05942983237845845, Difference Score = 2.197878119609803, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.03432845169897891, Difference Score = 4.363237770562402, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.0338672405192344, Difference Score = 4.586009729124708, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.1), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.033867240519234176, Difference Score = 4.586009729124821, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1196/2600 [06:57<11:04, 2.11pipeline/s] ************************************************************************************************** Entered Generation: 12 Generation 12 - Current Pareto Front: Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.09422519333870316, Difference Score = 1.559036512987213, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.09221628977650986, Difference Score = 1.6155966555872159, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.09141119169545897, Difference Score = 1.640972936951491, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.061819582925945915, Difference Score = 2.130221472177796, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.05942983237845845, Difference Score = 2.197878119609803, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1296/2600 [07:49<07:22, 2.94pipeline/s] ************************************************************************************************** Entered Generation: 13 Generation 13 - Current Pareto Front: Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.09621177423463134, Difference Score = 1.4250582756780652, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.09422519333870316, Difference Score = 1.559036512987213, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.09404373007902833, Difference Score = 1.5647217087741834, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.061819582925945915, Difference Score = 2.130221472177796, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.05942983237845845, Difference Score = 2.197878119609803, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1396/2600 [08:43<06:19, 3.17pipeline/s] ************************************************************************************************** Entered Generation: 14 Generation 14 - Current Pareto Front: Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.09621177423463134, Difference Score = 1.4250582756780652, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.09422519333870316, Difference Score = 1.559036512987213, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.09404373007902833, Difference Score = 1.5647217087741834, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.0931685924126201, Difference Score = 1.6108088016927908, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.061819582925945915, Difference Score = 2.130221472177796, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1496/2600 [09:35<03:35, 5.13pipeline/s] ************************************************************************************************** Entered Generation: 15 Generation 15 - Current Pareto Front: Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09621177423463134, Difference Score = 1.4250582756780652, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09422519333870316, Difference Score = 1.559036512987213, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09404373007902833, Difference Score = 1.5647217087741834, Pipeline: RandomForestRegressor(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.061819582925945915, Difference Score = 2.130221472177796, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1596/2600 [10:31<06:22, 2.62pipeline/s] ************************************************************************************************** Entered Generation: 16 Generation 16 - Current Pareto Front: Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09621177423463134, Difference Score = 1.4250582756780652, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09514753807451326, Difference Score = 1.4792666215754877, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.08142559140659034, Difference Score = 1.8529503801376426, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1696/2600 [11:26<08:33, 1.76pipeline/s] ************************************************************************************************** Entered Generation: 17 Generation 17 - Current Pareto Front: Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1796/2600 [12:17<05:23, 2.49pipeline/s] ************************************************************************************************** Entered Generation: 18 Generation 18 - Current Pareto Front: Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.06439369835052022, Difference Score = 2.0345994293688063, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.0610482571625276, Difference Score = 2.159746393520789, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1896/2600 [13:10<05:05, 2.31pipeline/s] ************************************************************************************************** Entered Generation: 19 Generation 19 - Current Pareto Front: Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.08121426616679583, Difference Score = 1.891254824100652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.06443574781404104, Difference Score = 2.039092758878361, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.0610482571625276, Difference Score = 2.159746393520789, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1996/2600 [14:02<05:13, 1.93pipeline/s] ************************************************************************************************** Entered Generation: 20 Generation 20 - Current Pareto Front: Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09633135927421621, Difference Score = 1.4589934059446803, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.08121426616679583, Difference Score = 1.891254824100652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.06443574781404104, Difference Score = 2.039092758878361, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.0610482571625276, Difference Score = 2.159746393520789, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2096/2600 [14:57<03:50, 2.19pipeline/s] ************************************************************************************************** Entered Generation: 21 Generation 21 - Current Pareto Front: Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09633135927421621, Difference Score = 1.4589934059446803, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.08121426616679583, Difference Score = 1.891254824100652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.06443574781404104, Difference Score = 2.039092758878361, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.06333900392196967, Difference Score = 2.1861386684323607, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2196/2600 [15:50<02:30, 2.69pipeline/s] ************************************************************************************************** Entered Generation: 22 Generation 22 - Current Pareto Front: Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.0989552601143665, Difference Score = 1.2582991314275893, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09771123055896536, Difference Score = 1.3726967320540677, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09633135927421621, Difference Score = 1.4589934059446803, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.08121426616679583, Difference Score = 1.891254824100652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.06443574781404104, Difference Score = 2.039092758878361, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.06434605453850939, Difference Score = 2.059445128373356, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.06333900392196967, Difference Score = 2.1861386684323607, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2296/2600 [16:45<02:12, 2.29pipeline/s] ************************************************************************************************** Entered Generation: 23 Generation 23 - Current Pareto Front: Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.0989552601143665, Difference Score = 1.2582991314275893, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09771123055896536, Difference Score = 1.3726967320540677, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09633135927421621, Difference Score = 1.4589934059446803, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.08290553620191865, Difference Score = 1.8577629128811413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.08121426616679583, Difference Score = 1.891254824100652, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.06443574781404104, Difference Score = 2.039092758878361, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.06434605453850939, Difference Score = 2.059445128373356, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.06333900392196967, Difference Score = 2.1861386684323607, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2396/2600 [17:39<01:20, 2.52pipeline/s] ************************************************************************************************** Entered Generation: 24 Generation 24 - Current Pareto Front: Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.0989552601143665, Difference Score = 1.2582991314275893, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09824321215098786, Difference Score = 1.4548754174731175, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09633135927421621, Difference Score = 1.4589934059446803, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.08995464031605815, Difference Score = 1.7282718476568728, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.08475763917493218, Difference Score = 1.8878097579577036, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.08285687356115823, Difference Score = 1.9012714091636993, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.06762652926928858, Difference Score = 2.0290662168768905, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.06757448021405887, Difference Score = 2.032960422777595, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.06443574781404104, Difference Score = 2.039092758878361, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.06434605453850939, Difference Score = 2.059445128373356, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.06333900392196967, Difference Score = 2.1861386684323607, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2496/2600 [18:36<00:58, 1.79pipeline/s] ************************************************************************************************** Entered Generation: 25 Generation 25 - Current Pareto Front: Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.0989552601143665, Difference Score = 1.2582991314275893, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09824321215098786, Difference Score = 1.4548754174731175, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09633135927421621, Difference Score = 1.4589934059446803, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09623372405354869, Difference Score = 1.4852408811750424, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09495308898448518, Difference Score = 1.54001240104373, Pipeline: RandomForestRegressor(DominantEncoder(UnderDominanceEncoder(RecessiveEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09443797068608129, Difference Score = 1.5727527514801956, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09335614461222042, Difference Score = 1.6290835527432257, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09282135421365267, Difference Score = 1.6430823014705278, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09114674547304669, Difference Score = 1.7130707766872233, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.09084900563969533, Difference Score = 1.7359022504613137, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.08937907332245665, Difference Score = 1.7541856234253315, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.08913835548982907, Difference Score = 1.7758269107788471, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.0875947421308314, Difference Score = 1.8054514449027559, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.08708940972875956, Difference Score = 1.8517622477441518, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.08475763917493218, Difference Score = 1.8878097579577036, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.08285687356115823, Difference Score = 1.9012714091636993, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.07779731567233594, Difference Score = 1.947580849063496, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.07722954131549109, Difference Score = 1.9957395329407561, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.0698861721817412, Difference Score = 2.053452201166121, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.06434605453850939, Difference Score = 2.059445128373356, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.06424166740243353, Difference Score = 2.093333381242939, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.0634596922031192, Difference Score = 2.1454278108910665, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.06333900392196967, Difference Score = 2.1861386684323607, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.05964207325502535, Difference Score = 2.204156053135818, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.05884573033182483, Difference Score = 2.2288564218011975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.05672351572484269, Difference Score = 2.263626486958165, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.05525255278425678, Difference Score = 2.504472970100898, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.0542399714888272, Difference Score = 2.539882645387408, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.04058928493873182, Difference Score = 3.889186592507688, Pipeline: DecisionTreeRegressor(FeatureEncodingFrequencySelector(VarianceThreshold(UnderDominanceEncoder(UnderDominanceEncoder(input_matrix)), VarianceThreshold__threshold=0.25), FeatureEncodingFrequencySelector__threshold=0.0), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.03613344394323326, Difference Score = 5.8076452096948605, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2)), SelectPercentile__percentile=70)) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = 0.03325455290803725, Difference Score = 6.027209218192399, Pipeline: LinearRegression(SelectPercentile(FeatureEncodingFrequencySelector(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), FeatureEncodingFrequencySelector__threshold=0.2), SelectPercentile__percentile=70)) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] Test R^2 = -0.000765629642629273, Difference Score = 6.051092222762979, Pipeline: RandomForestRegressor(DominantEncoder(DominantEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2596/2600 [19:32<00:01, 2.53pipeline/s] ************************************************************************************************************************************************************************************************************************** Random Seed 92 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Test R^2 = 0.09059797769301758, Difference Score = 1.486889524034361, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06135039435977174, Difference Score = 1.7226147052219327, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06070289096916348, Difference Score = 1.8132812769798488, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.043808298634681386, Difference Score = 1.8606939557762416, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) ****************************************************************************************************************************************************************** Entered Generation: 2 Generation 2 - Current Pareto Front: Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09059797769301758, Difference Score = 1.486889524034361, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08133061828104293, Difference Score = 1.6907132621713237, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06135039435977174, Difference Score = 1.7226147052219327, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06070289096916348, Difference Score = 1.8132812769798488, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.045911905029293765, Difference Score = 2.186089864514374, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) ************************************************************************************************************************************************************** Entered Generation: 3 Generation 3 - Current Pareto Front: Test R^2 = 0.09829593527440217, Difference Score = 1.2862540498739405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09059797769301758, Difference Score = 1.486889524034361, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0842419019055185, Difference Score = 1.7313836429758556, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07841392502512468, Difference Score = 1.7497868851197205, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06224946294345712, Difference Score = 1.8137613409558184, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.059684136432552104, Difference Score = 1.92620954349699, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04720434838968823, Difference Score = 2.183955098886316, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.045911905029293765, Difference Score = 2.186089864514374, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.005585381878555062, Difference Score = 4.12337451967038, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.0016168803236218388, Difference Score = 4.986898523356924, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) ************************************************************************************************************************************************* Entered Generation: 4 Generation 4 - Current Pareto Front: Test R^2 = 0.09829593527440217, Difference Score = 1.2862540498739405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09517563953556818, Difference Score = 1.3953856872065376, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09059797769301758, Difference Score = 1.486889524034361, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0842419019055185, Difference Score = 1.7313836429758556, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.059684136432552104, Difference Score = 1.92620954349699, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.053117646683983444, Difference Score = 2.0232301358676525, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.005585381878555062, Difference Score = 4.12337451967038, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.0016168803236218388, Difference Score = 4.986898523356924, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) ********************************************************************************************************************************************************* Entered Generation: 5 Generation 5 - Current Pareto Front: Test R^2 = 0.09829593527440217, Difference Score = 1.2862540498739405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09517563953556818, Difference Score = 1.3953856872065376, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09351119523068441, Difference Score = 1.5198526885959687, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0842419019055185, Difference Score = 1.7313836429758556, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.059684136432552104, Difference Score = 1.92620954349699, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.005585381878555062, Difference Score = 4.12337451967038, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.0016168803236218388, Difference Score = 4.986898523356924, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) ******************************************************************************************************************************************************* Entered Generation: 6 Generation 6 - Current Pareto Front: Test R^2 = 0.09829593527440217, Difference Score = 1.2862540498739405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09517563953556818, Difference Score = 1.3953856872065376, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09351119523068441, Difference Score = 1.5198526885959687, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0842419019055185, Difference Score = 1.7313836429758556, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08124332387428623, Difference Score = 1.7542482880717873, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06976803356837635, Difference Score = 1.8900364868967376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.059684136432552104, Difference Score = 1.92620954349699, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.005585381878555062, Difference Score = 4.12337451967038, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = -0.0016168803236218388, Difference Score = 4.986898523356924, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) ******************************************************************************************************************************************************* Entered Generation: 7 Generation 7 - Current Pareto Front: Test R^2 = 0.09829593527440217, Difference Score = 1.2862540498739405, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09517563953556818, Difference Score = 1.3953856872065376, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09351119523068441, Difference Score = 1.5198526885959687, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0842419019055185, Difference Score = 1.7313836429758556, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08124332387428623, Difference Score = 1.7542482880717873, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06976803356837635, Difference Score = 1.8900364868967376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.059684136432552104, Difference Score = 1.92620954349699, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.005585381878555062, Difference Score = 4.12337451967038, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.0015524172290154459, Difference Score = 4.560276512121094, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=25), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=17, DecisionTreeRegressor__min_samples_split=13) Test R^2 = -0.0016168803236218388, Difference Score = 4.986898523356924, Pipeline: RandomForestRegressor(DominantEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) ***************************************************************************************************************************************************** Entered Generation: 8 Generation 8 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09517563953556818, Difference Score = 1.3953856872065376, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09351119523068441, Difference Score = 1.5198526885959687, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0842419019055185, Difference Score = 1.7313836429758556, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08124332387428623, Difference Score = 1.7542482880717873, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06976803356837635, Difference Score = 1.8900364868967376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.059684136432552104, Difference Score = 1.92620954349699, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.005637219381740111, Difference Score = 4.044513980119584, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.005585381878555062, Difference Score = 4.12337451967038, Pipeline: DecisionTreeRegressor(DominantEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ********************************************************************************************************************************************************************** Entered Generation: 9 Generation 9 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09517563953556818, Difference Score = 1.3953856872065376, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09351119523068441, Difference Score = 1.5198526885959687, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08591287892708632, Difference Score = 1.715566329907973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06976803356837635, Difference Score = 1.8900364868967376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06888427652600704, Difference Score = 1.8972304611602302, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=9, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06702312524220655, Difference Score = 1.9846874833938408, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) **************************************************************************************************************************************************************** Entered Generation: 10 Generation 10 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09588809399203257, Difference Score = 1.4287607909196807, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09351119523068441, Difference Score = 1.5198526885959687, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09000866409065433, Difference Score = 1.5804353265580968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07315384811731085, Difference Score = 1.922623973517897, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06702312524220655, Difference Score = 1.9846874833938408, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06496806511409259, Difference Score = 2.049825726962203, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04879666880283107, Difference Score = 2.196758190662727, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.04021779002542414, Difference Score = 2.214276066893832, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=40)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ******************************************************************************************************************************************************** Entered Generation: 11 Generation 11 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09588809399203257, Difference Score = 1.4287607909196807, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09153973988175124, Difference Score = 1.5220432502434251, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09000866409065433, Difference Score = 1.5804353265580968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07315384811731085, Difference Score = 1.922623973517897, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0707974974221427, Difference Score = 1.933579281395926, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06702312524220655, Difference Score = 1.9846874833938408, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06496806511409259, Difference Score = 2.049825726962203, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) *************************************************************************************************************************************************************** Entered Generation: 12 Generation 12 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09588809399203257, Difference Score = 1.4287607909196807, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09153973988175124, Difference Score = 1.5220432502434251, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09068581189580338, Difference Score = 1.5491165759356498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09000866409065433, Difference Score = 1.5804353265580968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08446680271588025, Difference Score = 1.7754321965818851, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06496806511409259, Difference Score = 2.049825726962203, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Test R^2 = 0.061575947996455827, Difference Score = 2.0800889181714837, Pipeline: RandomForestRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05674059845784607, Difference Score = 2.193511306017945, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ****************************************************************************************************************************************************** Entered Generation: 13 Generation 13 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09588809399203257, Difference Score = 1.4287607909196807, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09153973988175124, Difference Score = 1.5220432502434251, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09134311744241341, Difference Score = 1.5853046449993413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08446680271588025, Difference Score = 1.7754321965818851, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.061575947996455827, Difference Score = 2.0800889181714837, Pipeline: RandomForestRegressor(UnderDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05796996043866964, Difference Score = 2.1989536090337145, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.057109504048314386, Difference Score = 2.216526845157326, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) *********************************************************************************************************************************************************************** Entered Generation: 14 Generation 14 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09886323025698263, Difference Score = 1.3470815284927857, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09641160469080468, Difference Score = 1.3535482832182815, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09588809399203257, Difference Score = 1.4287607909196807, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09153973988175124, Difference Score = 1.5220432502434251, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09134311744241341, Difference Score = 1.5853046449993413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08446680271588025, Difference Score = 1.7754321965818851, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.061684219420036435, Difference Score = 2.109234122602146, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05796996043866964, Difference Score = 2.1989536090337145, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.057109504048314386, Difference Score = 2.216526845157326, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ***************************************************************************************************************************************************************** Entered Generation: 15 Generation 15 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09886323025698263, Difference Score = 1.3470815284927857, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09705529463389106, Difference Score = 1.480564814776966, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09153973988175124, Difference Score = 1.5220432502434251, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09134311744241341, Difference Score = 1.5853046449993413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08446680271588025, Difference Score = 1.7754321965818851, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.061684219420036435, Difference Score = 2.109234122602146, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05813468844644487, Difference Score = 2.190964823107473, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05796996043866964, Difference Score = 2.1989536090337145, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.057109504048314386, Difference Score = 2.216526845157326, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ********************************************************************************************************************************************************************* Entered Generation: 16 Generation 16 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09886323025698263, Difference Score = 1.3470815284927857, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09705529463389106, Difference Score = 1.480564814776966, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09153973988175124, Difference Score = 1.5220432502434251, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09134311744241341, Difference Score = 1.5853046449993413, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08446680271588025, Difference Score = 1.7754321965818851, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0843229543747771, Difference Score = 1.8309364546447153, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.061684219420036435, Difference Score = 2.109234122602146, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05772719973665186, Difference Score = 2.2045510810203512, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.057109504048314386, Difference Score = 2.216526845157326, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0036682040710120933, Difference Score = 5.116945341764398, Pipeline: DecisionTreeRegressor(SelectPercentile(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25), SelectPercentile__percentile=25)), SelectPercentile__percentile=10), DecisionTreeRegressor__max_depth=8, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=12) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ***************************************************************************************************************************************************************************** Entered Generation: 17 Generation 17 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09886323025698263, Difference Score = 1.3470815284927857, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09705529463389106, Difference Score = 1.480564814776966, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09260372217407975, Difference Score = 1.5215836112818795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.061684219420036435, Difference Score = 2.109234122602146, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05772719973665186, Difference Score = 2.2045510810203512, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.057109504048314386, Difference Score = 2.216526845157326, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0036682040710120933, Difference Score = 5.116945341764398, Pipeline: DecisionTreeRegressor(SelectPercentile(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25), SelectPercentile__percentile=25)), SelectPercentile__percentile=10), DecisionTreeRegressor__max_depth=8, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=12) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ****************************************************************************************************************************************************************** Entered Generation: 18 Generation 18 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09886323025698263, Difference Score = 1.3470815284927857, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09705529463389106, Difference Score = 1.480564814776966, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09260372217407975, Difference Score = 1.5215836112818795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05772719973665186, Difference Score = 2.2045510810203512, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.057109504048314386, Difference Score = 2.216526845157326, Pipeline: RandomForestRegressor(VarianceThreshold(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=10, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0036682040710120933, Difference Score = 5.116945341764398, Pipeline: DecisionTreeRegressor(SelectPercentile(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25), SelectPercentile__percentile=25)), SelectPercentile__percentile=10), DecisionTreeRegressor__max_depth=8, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=12) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ***************************************************************************************************************************************************************************************************************** Entered Generation: 19 Generation 19 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09886323025698263, Difference Score = 1.3470815284927857, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09705529463389106, Difference Score = 1.480564814776966, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09260372217407975, Difference Score = 1.5215836112818795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07609164919207323, Difference Score = 1.9187803715086742, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0036682040710120933, Difference Score = 5.116945341764398, Pipeline: DecisionTreeRegressor(SelectPercentile(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25), SelectPercentile__percentile=25)), SelectPercentile__percentile=10), DecisionTreeRegressor__max_depth=8, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=12) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ********************************************************************************************************************************************************************************** Entered Generation: 20 Generation 20 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09940981377438252, Difference Score = 1.410064115876218, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09705529463389106, Difference Score = 1.480564814776966, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09480849060784069, Difference Score = 1.5931147613000942, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0780364425538963, Difference Score = 1.9266931037180015, Pipeline: RandomForestRegressor(DominantEncoder(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.015206009720152625, Difference Score = 3.5807925382083536, Pipeline: DecisionTreeRegressor(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=8 Test R^2 = 0.013102510007799162, Difference Score = 4.4264115720495445, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0036682040710120933, Difference Score = 5.116945341764398, Pipeline: DecisionTreeRegressor(SelectPercentile(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25), SelectPercentile__percentile=25)), SelectPercentile__percentile=10), DecisionTreeRegressor__max_depth=8, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=12) Test R^2 = 0.0035075711537085885, Difference Score = 5.681450545317964, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=25)), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=15) ******************************************************************************************************************************************************************************************************* Entered Generation: 21 Generation 21 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09940981377438252, Difference Score = 1.410064115876218, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.099022160727809, Difference Score = 1.5054157009642426, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09480849060784069, Difference Score = 1.5931147613000942, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0780364425538963, Difference Score = 1.9266931037180015, Pipeline: RandomForestRegressor(DominantEncoder(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016415013567312675, Difference Score = 8.650983410022262, Pipeline: DecisionTreeRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=17) ********************************************************************************************************************************************************************************* Entered Generation: 22 Generation 22 - Current Pareto Front: Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09940981377438252, Difference Score = 1.410064115876218, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.099022160727809, Difference Score = 1.5054157009642426, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09480849060784069, Difference Score = 1.5931147613000942, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0780364425538963, Difference Score = 1.9266931037180015, Pipeline: RandomForestRegressor(DominantEncoder(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016415013567312675, Difference Score = 8.650983410022262, Pipeline: DecisionTreeRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=17) *************************************************************************************************************************************************************************** Entered Generation: 23 Generation 23 - Current Pareto Front: Test R^2 = 0.10300793162700994, Difference Score = 1.32552389846645, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10082974503648356, Difference Score = 1.4049224988163758, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09940981377438252, Difference Score = 1.410064115876218, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.099022160727809, Difference Score = 1.5054157009642426, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09480849060784069, Difference Score = 1.5931147613000942, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0780364425538963, Difference Score = 1.9266931037180015, Pipeline: RandomForestRegressor(DominantEncoder(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016415013567312675, Difference Score = 8.650983410022262, Pipeline: DecisionTreeRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=17) *********************************************************************************************************************************************************************************** Entered Generation: 24 Generation 24 - Current Pareto Front: Test R^2 = 0.10300793162700994, Difference Score = 1.32552389846645, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10142038491578753, Difference Score = 1.4399154296400776, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.099022160727809, Difference Score = 1.5054157009642426, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09480849060784069, Difference Score = 1.5931147613000942, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09247355215087505, Difference Score = 1.6163174311586892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0916038107519227, Difference Score = 1.6325790060367869, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0780364425538963, Difference Score = 1.9266931037180015, Pipeline: RandomForestRegressor(DominantEncoder(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06408860638014646, Difference Score = 2.0534224714206895, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=60)), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016415013567312675, Difference Score = 8.650983410022262, Pipeline: DecisionTreeRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=17) ****************************************************************************************************************************************************************************************** Entered Generation: 25 Generation 25 - Current Pareto Front: Test R^2 = 0.10300793162700994, Difference Score = 1.32552389846645, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10176180662813072, Difference Score = 1.346486360129212, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.10142038491578753, Difference Score = 1.4399154296400776, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.099022160727809, Difference Score = 1.5054157009642426, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09493460064430714, Difference Score = 1.5207212116706335, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09480849060784069, Difference Score = 1.5931147613000942, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.09306444975529027, Difference Score = 1.6411653106705484, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08985353273366636, Difference Score = 1.6658663932271554, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08906998677503775, Difference Score = 1.6736534078001715, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08844872777515811, Difference Score = 1.6744302745405968, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08810651533717073, Difference Score = 1.697342099052102, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08762831970045826, Difference Score = 1.7139162693583394, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08720709602096599, Difference Score = 1.7438118333329835, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08494374364156321, Difference Score = 1.76133026805769, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08467657049208044, Difference Score = 1.8352020023859807, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08123601417145199, Difference Score = 1.8457875408548583, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.08041252334465099, Difference Score = 1.8686648890646256, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0780364425538963, Difference Score = 1.9266931037180015, Pipeline: RandomForestRegressor(DominantEncoder(FeatureEncodingFrequencySelector(OverDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.07552718941781456, Difference Score = 1.946674406379796, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Test R^2 = 0.0730761683056852, Difference Score = 2.0397106518633965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06848929444060936, Difference Score = 2.050489452951339, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06408860638014646, Difference Score = 2.0534224714206895, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=60)), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Test R^2 = 0.06329882137281795, Difference Score = 2.1398576004199428, Pipeline: RandomForestRegressor(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=90), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05865551093547339, Difference Score = 2.2011184904654417, Pipeline: RandomForestRegressor(VarianceThreshold(SelectPercentile(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(RecessiveEncoder(FeatureEncodingFrequencySelector(UnderDominanceEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35))))), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05852320854144355, Difference Score = 2.225340644294882, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Test R^2 = 0.05449205478600705, Difference Score = 2.232418124352176, Pipeline: RandomForestRegressor(RecessiveEncoder(OverDominanceEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Test R^2 = 0.03997793141986594, Difference Score = 2.5289038985196806, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Test R^2 = 0.03978408170400005, Difference Score = 3.522830509905924, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Test R^2 = 0.016415013567312675, Difference Score = 8.650983410022262, Pipeline: DecisionTreeRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(UnderDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.35)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=10, DecisionTreeRegressor__min_samples_split=17) ************************************************************************************************************************************************************************************************************************** Random Seed 102 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.09525599701120913, Difference Score = 1.4970258055568575, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.09263486435781754, Difference Score = 1.691481791523145, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.09189077042402727, Difference Score = 1.7077240474716016, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.06482505524621784, Difference Score = 1.7590075805160756, Pipeline: RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.06371118748764937, Difference Score = 1.840126215116441, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.05338277368884903, Difference Score = 2.385047467354207, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.04538725336076166, Difference Score = 3.812097900429425, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.04033309886596759, Difference Score = 3.8163429577376897, Pipeline: LinearRegression(HeterosisEncoder(input_matrix)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.03819042685066365, Difference Score = 4.416897432872612, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.03242383436726404, Difference Score = 4.677564099200487, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=15)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Test R^2 = 0.014543771162176533, Difference Score = 4.790721222548457, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=1, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:27, 3.83pipeline/s] Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Test R^2 = 0.06371118748764937, Difference Score = 1.840126215116441, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Test R^2 = 0.05338277368884903, Difference Score = 2.385047467354207, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Test R^2 = 0.046082837315518765, Difference Score = 3.8940555916861266, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=12) Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Test R^2 = 0.038848087918951335, Difference Score = 5.775124928174153, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=50)) Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Test R^2 = 0.0371411716938691, Difference Score = 8.15989322600606, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=40)) Optimization Progress: 11%|█▏ | 298/2600 [01:07<07:47, 4.92pipeline/s] Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.09653687081214579, Difference Score = 1.3524202197583355, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.07436745286092261, Difference Score = 1.9029999105692443, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.05338277368884903, Difference Score = 2.385047467354207, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.05252523530795028, Difference Score = 2.3934758578773443, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=13, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.038848087918951335, Difference Score = 5.775124928174153, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=50)) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.0371411716938691, Difference Score = 8.15989322600606, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=40)) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Test R^2 = 0.03496780693723023, Difference Score = 12.27275564707864, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=15)) Optimization Progress: 15%|█▌ | 398/2600 [01:32<07:27, 4.92pipeline/s] Entered Generation: 4 Generation 4 - Current Pareto Front: Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.09653687081214579, Difference Score = 1.3524202197583355, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=4, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.08337321285921806, Difference Score = 1.8422245216980493, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.07761036375287911, Difference Score = 1.8865687846255845, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.07436745286092261, Difference Score = 1.9029999105692443, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.056897290236929576, Difference Score = 2.4183093620398797, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.038848087918951335, Difference Score = 5.775124928174153, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=50)) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.0371411716938691, Difference Score = 8.15989322600606, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=40)) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Test R^2 = 0.03496780693723023, Difference Score = 12.27275564707864, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=15)) Optimization Progress: 19%|█▉ | 498/2600 [01:52<06:34, 5.33pipeline/s] Entered Generation: 5 Generation 5 - Current Pareto Front: Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.08690745696655922, Difference Score = 1.892423669109117, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.07817033525193495, Difference Score = 1.9287943075276728, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.06604998364808268, Difference Score = 2.1272817187998325, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.0371411716938691, Difference Score = 8.15989322600606, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=40)) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Test R^2 = 0.03496780693723023, Difference Score = 12.27275564707864, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=15)) Optimization Progress: 23%|██▎ | 598/2600 [02:16<09:43, 3.43pipeline/s] Entered Generation: 6 Generation 6 - Current Pareto Front: Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.08690745696655922, Difference Score = 1.892423669109117, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.07817033525193495, Difference Score = 1.9287943075276728, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.07109061722070442, Difference Score = 2.2236864093474975, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.0611473749654069, Difference Score = 2.278550963403429, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.0371411716938691, Difference Score = 8.15989322600606, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=40)) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.03563146242839266, Difference Score = 9.55843141754599, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=15), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Test R^2 = 0.03496780693723023, Difference Score = 12.27275564707864, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=15)) Optimization Progress: 27%|██▋ | 698/2600 [02:35<06:07, 5.18pipeline/s] Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.08690745696655922, Difference Score = 1.892423669109117, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.07817033525193495, Difference Score = 1.9287943075276728, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.07109061722070442, Difference Score = 2.2236864093474975, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.0611473749654069, Difference Score = 2.278550963403429, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 31%|███ | 798/2600 [02:52<01:53, 15.82pipeline/s] Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.09859566255725571, Difference Score = 1.39420131906004, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.09857598616146823, Difference Score = 1.4931673891909802, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.08690745696655922, Difference Score = 1.892423669109117, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.07109061722070442, Difference Score = 2.2236864093474975, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.0611473749654069, Difference Score = 2.278550963403429, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 35%|███▍ | 898/2600 [03:12<03:53, 7.29pipeline/s] Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.10463660452421442, Difference Score = 1.3807797915512459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.09859566255725571, Difference Score = 1.39420131906004, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.09857598616146823, Difference Score = 1.4931673891909802, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.09749780862466284, Difference Score = 1.578071286090411, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.07109061722070442, Difference Score = 2.2236864093474975, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.0611473749654069, Difference Score = 2.278550963403429, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 38%|███▊ | 998/2600 [03:33<05:06, 5.22pipeline/s] Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.10463660452421442, Difference Score = 1.3807797915512459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.10043604218672075, Difference Score = 1.571614925201168, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.09749780862466284, Difference Score = 1.578071286090411, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.07109061722070442, Difference Score = 2.2236864093474975, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.0611473749654069, Difference Score = 2.278550963403429, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 42%|████▏ | 1098/2600 [03:50<02:30, 9.96pipeline/s] Entered Generation: 11 Generation 11 - Current Pareto Front: Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.10463660452421442, Difference Score = 1.3807797915512459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.10451010997195298, Difference Score = 1.3808750306839799, Pipeline: RandomForestRegressor(UnderDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.10192728612753199, Difference Score = 1.5542961642496806, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.10043604218672075, Difference Score = 1.571614925201168, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.09749780862466284, Difference Score = 1.578071286090411, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.0611473749654069, Difference Score = 2.278550963403429, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 46%|████▌ | 1198/2600 [04:01<01:49, 12.78pipeline/s] Entered Generation: 12 Generation 12 - Current Pareto Front: Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.10463660452421442, Difference Score = 1.3807797915512459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.10451010997195298, Difference Score = 1.3808750306839799, Pipeline: RandomForestRegressor(UnderDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.10294148159986716, Difference Score = 1.5924427398670797, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.09214778813895352, Difference Score = 1.854414684206965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 50%|████▉ | 1298/2600 [04:17<05:46, 3.75pipeline/s] Entered Generation: 13 Generation 13 - Current Pareto Front: Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.10463660452421442, Difference Score = 1.3807797915512459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.10451010997195298, Difference Score = 1.3808750306839799, Pipeline: RandomForestRegressor(UnderDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.10294148159986716, Difference Score = 1.5924427398670797, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.0970810008902766, Difference Score = 1.6151538208524436, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.09214778813895352, Difference Score = 1.854414684206965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 54%|█████▍ | 1398/2600 [04:31<02:29, 8.04pipeline/s] Entered Generation: 14 Generation 14 - Current Pareto Front: Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.10463660452421442, Difference Score = 1.3807797915512459, Pipeline: RandomForestRegressor(UnderDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.10451010997195298, Difference Score = 1.3808750306839799, Pipeline: RandomForestRegressor(UnderDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.10429637912326029, Difference Score = 1.4605578033947957, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.09214778813895352, Difference Score = 1.854414684206965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.08842196116559509, Difference Score = 1.8551943835147304, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 58%|█████▊ | 1498/2600 [04:47<02:52, 6.39pipeline/s] Entered Generation: 15 Generation 15 - Current Pareto Front: Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.10914389766282595, Difference Score = 1.410189364100876, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.10547890392083137, Difference Score = 1.4196052970154172, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.10429637912326029, Difference Score = 1.4605578033947957, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.09214778813895352, Difference Score = 1.854414684206965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.08842196116559509, Difference Score = 1.8551943835147304, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.0831231337560212, Difference Score = 2.050115693360171, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.07026597435484627, Difference Score = 2.257014065839217, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85)), FeatureEncodingFrequencySelector__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 61%|██████▏ | 1597/2600 [05:04<02:33, 6.53pipeline/s] Entered Generation: 16 Generation 16 - Current Pareto Front: Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.10914389766282595, Difference Score = 1.410189364100876, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.10547890392083137, Difference Score = 1.4196052970154172, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.10429637912326029, Difference Score = 1.4605578033947957, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.09224970393763032, Difference Score = 1.843282437837154, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.09214778813895352, Difference Score = 1.854414684206965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.08842196116559509, Difference Score = 1.8551943835147304, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.0831231337560212, Difference Score = 2.050115693360171, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.07026597435484627, Difference Score = 2.257014065839217, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85)), FeatureEncodingFrequencySelector__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 65%|██████▌ | 1697/2600 [05:21<03:04, 4.90pipeline/s] Entered Generation: 17 Generation 17 - Current Pareto Front: Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.10914389766282595, Difference Score = 1.410189364100876, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.10591374266928122, Difference Score = 1.4261751023442308, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.10429637912326029, Difference Score = 1.4605578033947957, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.09273563803501972, Difference Score = 1.8375052787794766, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.09224970393763032, Difference Score = 1.843282437837154, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.09214778813895352, Difference Score = 1.854414684206965, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.08842196116559509, Difference Score = 1.8551943835147304, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.0831231337560212, Difference Score = 2.050115693360171, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.07256635011731039, Difference Score = 2.1775692492074468, Pipeline: RandomForestRegressor(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.0720880392991109, Difference Score = 2.2385570954686504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.07026597435484627, Difference Score = 2.257014065839217, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85)), FeatureEncodingFrequencySelector__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 69%|██████▉ | 1797/2600 [05:41<01:18, 10.21pipeline/s] Entered Generation: 18 Generation 18 - Current Pareto Front: Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.10914389766282595, Difference Score = 1.410189364100876, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.10604659897362789, Difference Score = 1.5330198406242534, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.10357704598266315, Difference Score = 1.598927042592771, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.09273563803501972, Difference Score = 1.8375052787794766, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.09229638837643117, Difference Score = 1.8581001307952505, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.0831231337560212, Difference Score = 2.050115693360171, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.07306238375809393, Difference Score = 2.2814179160540635, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=45), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 73%|███████▎ | 1897/2600 [06:06<02:09, 5.42pipeline/s] Entered Generation: 19 Generation 19 - Current Pareto Front: Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.10914389766282595, Difference Score = 1.410189364100876, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.10648267557299007, Difference Score = 1.4667691096111892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.10604659897362789, Difference Score = 1.5330198406242534, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.10357704598266315, Difference Score = 1.598927042592771, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.09273563803501972, Difference Score = 1.8375052787794766, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.09229638837643117, Difference Score = 1.8581001307952505, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.0831231337560212, Difference Score = 2.050115693360171, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.07306238375809393, Difference Score = 2.2814179160540635, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=45), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 77%|███████▋ | 1997/2600 [06:27<01:54, 5.26pipeline/s] Entered Generation: 20 Generation 20 - Current Pareto Front: Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.10914389766282595, Difference Score = 1.410189364100876, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.10653614191777805, Difference Score = 1.4343922595253722, Pipeline: RandomForestRegressor(RecessiveEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.1)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.10648267557299007, Difference Score = 1.4667691096111892, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.10604659897362789, Difference Score = 1.5330198406242534, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.10357704598266315, Difference Score = 1.598927042592771, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.10345074705682555, Difference Score = 1.5996654933194832, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=95))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.09859925544043358, Difference Score = 1.61059620672597, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.09841548031968894, Difference Score = 1.6982111881975666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=13, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.09554372363137942, Difference Score = 1.8337860672632527, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.09273563803501972, Difference Score = 1.8375052787794766, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.09229638837643117, Difference Score = 1.8581001307952505, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.08787930686755185, Difference Score = 2.025282490174974, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.08467935649764835, Difference Score = 2.043228160950443, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.0831231337560212, Difference Score = 2.050115693360171, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.1), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.08070835036724744, Difference Score = 2.1160186901605997, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.07848778832052128, Difference Score = 2.1465028081510327, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.07306238375809393, Difference Score = 2.2814179160540635, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=45), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.06932023956914268, Difference Score = 2.291711935124888, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=85))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.05988193466358016, Difference Score = 2.5623325576831157, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=20, DecisionTreeRegressor__min_samples_split=10) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.04794439553481977, Difference Score = 4.456053577031977, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=8, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.04517508934968706, Difference Score = 5.683193737760648, Pipeline: DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=6, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.04517508934968695, Difference Score = 5.6831937377608135, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=95), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=19, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.041834455047964236, Difference Score = 5.776621226039958, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2))), SelectPercentile__percentile=50)) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.04176324377089913, Difference Score = 7.005708826503119, Pipeline: LinearRegression(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(input_matrix)), SelectPercentile__percentile=40)) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.039433146583358725, Difference Score = 8.139935338558509, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.2)), SelectPercentile__percentile=50)) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Test R^2 = 0.03798917886148245, Difference Score = 16.78724400853735, Pipeline: LinearRegression(SelectPercentile(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), SelectPercentile__percentile=90)) Optimization Progress: 81%|████████ | 2097/2600 [06:46<02:03, 4.08pipeline/s] Entered Generation: 21 ************************************************************************************************************************************************************************************************************************** Random Seed 122 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.10445477674870918, Difference Score = 1.2493172088300406, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.1018569062929322, Difference Score = 1.7275905926330568, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.06275668049192795, Difference Score = 1.8562706899355481, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.04555614607383429, Difference Score = 2.1505859862686267, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.04244742005702884, Difference Score = 4.392039428254908, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.042392196051385556, Difference Score = 7.068239198698095, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Test R^2 = 0.04001231366863722, Difference Score = 11.198268135339237, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 8%|▊ | 198/2600 [00:43<10:20, 3.87pipeline/s] Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.11287002739290986, Difference Score = 1.3221831564308009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.10282562678493079, Difference Score = 1.4774022448263051, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.1018569062929322, Difference Score = 1.7275905926330568, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.10067690469737633, Difference Score = 1.7914080494259303, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.09834013888521653, Difference Score = 1.818077686370984, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.09312206117334154, Difference Score = 1.932075360268284, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.05819735634278711, Difference Score = 1.9511367603420742, Pipeline: RandomForestRegressor(OverDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=50)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.05264759574060862, Difference Score = 2.2387941014201354, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.04335044804853927, Difference Score = 8.134080758495626, Pipeline: LinearRegression(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25))) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.04001231366863722, Difference Score = 11.198268135339237, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Test R^2 = 0.03997492726305185, Difference Score = 15.059144910020088, Pipeline: LinearRegression(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15))) Optimization Progress: 11%|█▏ | 297/2600 [01:09<05:43, 6.70pipeline/s] Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.11287002739290986, Difference Score = 1.3221831564308009, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.10700159162591649, Difference Score = 1.586878333144679, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.10416882942558048, Difference Score = 1.6230472183159785, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.1018569062929322, Difference Score = 1.7275905926330568, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.10128588275574424, Difference Score = 1.80526328288467, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.09834013888521653, Difference Score = 1.818077686370984, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=15, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.09312206117334154, Difference Score = 1.932075360268284, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.05842702011436918, Difference Score = 1.9919881081204232, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.05264759574060862, Difference Score = 2.2387941014201354, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.048545693237859955, Difference Score = 2.276585762402289, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=17) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.04339066083732168, Difference Score = 7.844558219632682, Pipeline: LinearRegression(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15))) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.04335044804853927, Difference Score = 8.134080758495626, Pipeline: LinearRegression(HeterosisEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.25))) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.04001231366863722, Difference Score = 11.198268135339237, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Test R^2 = 0.03997492726305185, Difference Score = 15.059144910020088, Pipeline: LinearRegression(UnderDominanceEncoder(VarianceThreshold(input_matrix, VarianceThreshold__threshold=0.15))) Optimization Progress: 15%|█▌ | 397/2600 [01:42<10:11, 3.60pipeline/s] Entered Generation: 4 ************************************************************************************************************************************************************************************************************************** Random Seed 132 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.09948758464170926, Difference Score = 1.1813069057069143, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.09943252078884757, Difference Score = 1.521364544198592, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.09929295236296798, Difference Score = 1.7336685995851502, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.06684775768061657, Difference Score = 1.880722026766602, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.0482405876718589, Difference Score = 3.173472507341351, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406315, Pipeline: LinearRegression(HeterosisEncoder(input_matrix)) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 8%|▊ | 198/2600 [00:45<10:43, 3.73pipeline/s] Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.1097387316223768, Difference Score = 1.303031866249337, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.10422703969997282, Difference Score = 1.6033986227435444, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.09929295236296798, Difference Score = 1.7336685995851502, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.09300598144972105, Difference Score = 1.932353269393057, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.06460795042961753, Difference Score = 2.4123252828006807, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.0482405876718589, Difference Score = 3.173472507341351, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406315, Pipeline: LinearRegression(HeterosisEncoder(input_matrix)) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 11%|█▏ | 298/2600 [01:14<12:30, 3.07pipeline/s] Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.1097387316223768, Difference Score = 1.303031866249337, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.10422703969997282, Difference Score = 1.6033986227435444, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.0996562208590086, Difference Score = 1.7989406261749845, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.09709838758137468, Difference Score = 1.822317599464169, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.09300598144972105, Difference Score = 1.932353269393057, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.09227149618050523, Difference Score = 1.940194171558292, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.06460795042961753, Difference Score = 2.4123252828006807, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.0482405876718589, Difference Score = 3.173472507341351, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406315, Pipeline: LinearRegression(HeterosisEncoder(input_matrix)) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 15%|█▌ | 398/2600 [01:51<17:17, 2.12pipeline/s] Entered Generation: 4 Generation 4 - Current Pareto Front: Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.1097387316223768, Difference Score = 1.303031866249337, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.10727578384083436, Difference Score = 1.5297307952351944, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.10527958798715031, Difference Score = 1.5756382568879337, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.10422703969997282, Difference Score = 1.6033986227435444, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.10285868768299544, Difference Score = 1.7209226389584666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.1026655433839182, Difference Score = 1.8113423115061227, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.09300598144972105, Difference Score = 1.932353269393057, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.09227149618050523, Difference Score = 1.940194171558292, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.08207445427657778, Difference Score = 1.9736266184190752, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.06460795042961753, Difference Score = 2.4123252828006807, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.0482405876718589, Difference Score = 3.1734725073413688, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406315, Pipeline: LinearRegression(HeterosisEncoder(input_matrix)) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 19%|█▉ | 497/2600 [02:24<07:47, 4.50pipeline/s] Entered Generation: 5 Generation 5 - Current Pareto Front: Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.1097387316223768, Difference Score = 1.303031866249337, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10727578384083436, Difference Score = 1.5297307952351944, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10673826445418011, Difference Score = 1.5671260241735514, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10527958798715031, Difference Score = 1.5756382568879337, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10422703969997282, Difference Score = 1.6033986227435444, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10294534865052196, Difference Score = 1.7165298994352693, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10285868768299544, Difference Score = 1.7209226389584666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.1026655433839182, Difference Score = 1.8113423115061227, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.09300598144972105, Difference Score = 1.932353269393057, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.09227149618050523, Difference Score = 1.940194171558292, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.09015361025621227, Difference Score = 2.0278516019650294, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.06460795042961753, Difference Score = 2.4123252828006807, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.0482405876718589, Difference Score = 3.1734725073413688, Pipeline: DecisionTreeRegressor(HeterosisEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=16) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406315, Pipeline: LinearRegression(HeterosisEncoder(input_matrix)) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 23%|██▎ | 597/2600 [02:51<06:12, 5.37pipeline/s] Entered Generation: 6 Generation 6 - Current Pareto Front: Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.1097387316223768, Difference Score = 1.303031866249337, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10732167573723495, Difference Score = 1.4379883329804926, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=9, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10727578384083436, Difference Score = 1.5297307952351944, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10673826445418011, Difference Score = 1.5671260241735514, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10527958798715031, Difference Score = 1.5756382568879337, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10422703969997282, Difference Score = 1.6033986227435444, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10294534865052196, Difference Score = 1.7165298994352693, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10285868768299544, Difference Score = 1.7209226389584666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.1026655433839182, Difference Score = 1.8113423115061227, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.09300598144972105, Difference Score = 1.932353269393057, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.09227149618050523, Difference Score = 1.940194171558292, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.09015361025621227, Difference Score = 2.0278516019650294, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.06948441337799083, Difference Score = 2.377520857937619, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.06465849742556273, Difference Score = 2.4897154007464075, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.05713616772830099, Difference Score = 3.256958514357746, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 27%|██▋ | 697/2600 [03:23<11:22, 2.79pipeline/s] Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.10294534865052196, Difference Score = 1.7165298994352693, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.10285868768299544, Difference Score = 1.7209226389584666, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.1026655433839182, Difference Score = 1.8113423115061227, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.09370523420197008, Difference Score = 2.026710586862484, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.07132815086532052, Difference Score = 2.2477052164428173, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.06465849742556273, Difference Score = 2.4897154007464075, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.06096990950949155, Difference Score = 2.5719245763781498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 31%|███ | 797/2600 [04:00<09:19, 3.23pipeline/s] Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.0994591829867515, Difference Score = 1.8575152044481, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.09584944808931961, Difference Score = 1.925114064391269, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.09370523420197008, Difference Score = 2.026710586862484, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.07132815086532052, Difference Score = 2.2477052164428173, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.06465849742556273, Difference Score = 2.4897154007464075, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.06096990950949155, Difference Score = 2.5719245763781498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.06036506983310075, Difference Score = 2.59733171949975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.04587473694750033, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=75)) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 34%|███▍ | 897/2600 [04:36<06:56, 4.09pipeline/s] Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.0994591829867515, Difference Score = 1.8575152044481, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09860787384171554, Difference Score = 1.8943169919337823, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09621118594873657, Difference Score = 1.9248476231210283, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09584944808931961, Difference Score = 1.925114064391269, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09425273313423821, Difference Score = 1.9692506032878383, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(HeterosisEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09370523420197008, Difference Score = 2.026710586862484, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.07132815086532052, Difference Score = 2.2477052164428173, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.06465849742556273, Difference Score = 2.4897154007464075, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.06096990950949155, Difference Score = 2.5719245763781498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.06036506983310075, Difference Score = 2.59733171949975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 38%|███▊ | 997/2600 [05:11<09:26, 2.83pipeline/s] Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.10397224823586082, Difference Score = 1.6695770365520435, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.0994591829867515, Difference Score = 1.8575152044481, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09860787384171554, Difference Score = 1.8943169919337823, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09621118594873657, Difference Score = 1.9248476231210283, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09584944808931961, Difference Score = 1.925114064391269, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09425273313423821, Difference Score = 1.9692506032878383, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(HeterosisEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09370523420197008, Difference Score = 2.026710586862484, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.08142760035851127, Difference Score = 2.2214337747946504, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.07132815086532052, Difference Score = 2.2477052164428173, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.06465849742556273, Difference Score = 2.4897154007464075, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.06096990950949155, Difference Score = 2.5719245763781498, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.06036506983310075, Difference Score = 2.59733171949975, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 42%|████▏ | 1097/2600 [05:51<06:14, 4.01pipeline/s] Entered Generation: 11 Generation 11 - Current Pareto Front: Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.10397224823586082, Difference Score = 1.6695770365520435, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09860787384171554, Difference Score = 1.8943169919337823, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09621118594873657, Difference Score = 1.9248476231210283, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09584944808931961, Difference Score = 1.925114064391269, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09425273313423821, Difference Score = 1.9692506032878383, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(HeterosisEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09370523420197008, Difference Score = 2.026710586862484, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.07132815086532052, Difference Score = 2.2477052164428173, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 46%|████▌ | 1197/2600 [06:27<04:54, 4.76pipeline/s] Entered Generation: 12 Generation 12 - Current Pareto Front: Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.10397224823586082, Difference Score = 1.6695770365520435, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09880922806876358, Difference Score = 1.8823591880987707, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09860787384171554, Difference Score = 1.8943169919337823, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09815106448459476, Difference Score = 1.9584815684619419, Pipeline: RandomForestRegressor(OverDominanceEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09425273313423821, Difference Score = 1.9692506032878383, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(HeterosisEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09370523420197008, Difference Score = 2.026710586862484, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09203080387107376, Difference Score = 2.0448972703869663, Pipeline: RandomForestRegressor(UnderDominanceEncoder(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.07132815086532052, Difference Score = 2.2477052164428173, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.05, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.040077431784314665, Difference Score = 3.9030776958285784, Pipeline: LinearRegression(VarianceThreshold(VarianceThreshold(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), VarianceThreshold__threshold=0.2), VarianceThreshold__threshold=0.25)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 50%|████▉ | 1297/2600 [07:04<05:44, 3.79pipeline/s] Entered Generation: 13 Generation 13 - Current Pareto Front: Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10592314564515792, Difference Score = 1.5895947865672149, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10397224823586082, Difference Score = 1.6695770365520435, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.09203080387107376, Difference Score = 2.0448972703869663, Pipeline: RandomForestRegressor(UnderDominanceEncoder(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.07302315052808273, Difference Score = 2.3665363861785464, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.05722277198163206, Difference Score = 3.265231172749563, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.040077431784314665, Difference Score = 3.9030776958285784, Pipeline: LinearRegression(VarianceThreshold(VarianceThreshold(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), VarianceThreshold__threshold=0.2), VarianceThreshold__threshold=0.25)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 54%|█████▎ | 1397/2600 [07:43<07:57, 2.52pipeline/s] Entered Generation: 14 Generation 14 - Current Pareto Front: Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.1069856063700022, Difference Score = 1.6184035785531072, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=11, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.10397224823586082, Difference Score = 1.6695770365520435, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.07302315052808273, Difference Score = 2.3665363861785464, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.07069798234284796, Difference Score = 2.381466796595684, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.35), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.05753739648991574, Difference Score = 3.3084077119721242, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.040077431784314665, Difference Score = 3.9030776958285784, Pipeline: LinearRegression(VarianceThreshold(VarianceThreshold(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), VarianceThreshold__threshold=0.2), VarianceThreshold__threshold=0.25)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 58%|█████▊ | 1497/2600 [08:21<06:25, 2.86pipeline/s] Entered Generation: 15 Generation 15 - Current Pareto Front: Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10840233410874522, Difference Score = 1.6218591298054106, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10422827211779806, Difference Score = 1.6660468241491087, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10397224823586082, Difference Score = 1.6695770365520435, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=17, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.05753739648991574, Difference Score = 3.3084077119721242, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.040077431784314665, Difference Score = 3.9030776958285784, Pipeline: LinearRegression(VarianceThreshold(VarianceThreshold(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), VarianceThreshold__threshold=0.2), VarianceThreshold__threshold=0.25)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 61%|██████▏ | 1597/2600 [08:51<05:06, 3.28pipeline/s] Entered Generation: 16 Generation 16 - Current Pareto Front: Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.10840233410874522, Difference Score = 1.6218591298054106, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.10625294732545754, Difference Score = 1.6786875601395135, Pipeline: RandomForestRegressor(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.05753739648991574, Difference Score = 3.3084077119721242, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 65%|██████▌ | 1697/2600 [09:19<05:07, 2.94pipeline/s] Entered Generation: 17 Generation 17 - Current Pareto Front: Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.10840233410874522, Difference Score = 1.6218591298054106, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.10625294732545754, Difference Score = 1.6786875601395135, Pipeline: RandomForestRegressor(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.05753739648991574, Difference Score = 3.3084077119721242, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 69%|██████▉ | 1797/2600 [09:50<03:59, 3.35pipeline/s] Entered Generation: 18 Generation 18 - Current Pareto Front: Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.10985306859878574, Difference Score = 1.5820171036605102, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.10954530215531522, Difference Score = 1.5872226574492767, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.10840233410874522, Difference Score = 1.6218591298054106, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.10625294732545754, Difference Score = 1.6786875601395135, Pipeline: RandomForestRegressor(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.05753739648991574, Difference Score = 3.3084077119721242, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04587473694750055, Difference Score = 3.414910957215506, Pipeline: LinearRegression(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0))), SelectPercentile__percentile=75)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 73%|███████▎ | 1897/2600 [10:14<02:34, 4.54pipeline/s] Entered Generation: 19 Generation 19 - Current Pareto Front: Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.11014459049597769, Difference Score = 1.6163007487486676, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.10840233410874522, Difference Score = 1.6218591298054106, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.10625294732545754, Difference Score = 1.6786875601395135, Pipeline: RandomForestRegressor(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 77%|███████▋ | 1997/2600 [10:41<03:08, 3.20pipeline/s] Entered Generation: 20 Generation 20 - Current Pareto Front: Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.11014459049597769, Difference Score = 1.6163007487486676, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.10840233410874522, Difference Score = 1.6218591298054106, Pipeline: RandomForestRegressor(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.10625294732545754, Difference Score = 1.6786875601395135, Pipeline: RandomForestRegressor(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.09906491354799363, Difference Score = 1.9060058580839907, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.09837812615804309, Difference Score = 1.9060921176435468, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.07511214003523725, Difference Score = 2.3700329443144605, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.2), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 81%|████████ | 2097/2600 [11:07<01:57, 4.28pipeline/s] Entered Generation: 21 Generation 21 - Current Pareto Front: Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.11015940366044108, Difference Score = 1.752330844926403, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.09944078289867042, Difference Score = 1.9264393238899786, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.07511214003523725, Difference Score = 2.3700329443144605, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.2), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 84%|████████▍ | 2197/2600 [11:37<02:09, 3.11pipeline/s] Entered Generation: 22 Generation 22 - Current Pareto Front: Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.11015940366044108, Difference Score = 1.752330844926403, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.09944078289867042, Difference Score = 1.9264393238899786, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.07870454042009967, Difference Score = 2.313146363747242, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.07511214003523725, Difference Score = 2.3700329443144605, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(RecessiveEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.2), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.07467464381017863, Difference Score = 2.4173397757563793, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.040028418452198955, Difference Score = 4.003504509346599, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80))) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 88%|████████▊ | 2297/2600 [12:03<01:00, 4.97pipeline/s] Entered Generation: 23 Generation 23 - Current Pareto Front: Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.11015940366044108, Difference Score = 1.752330844926403, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.09944078289867042, Difference Score = 1.9264393238899786, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.07870454042009967, Difference Score = 2.313146363747242, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.0757171847738729, Difference Score = 2.3722348048016317, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.07467464381017863, Difference Score = 2.4173397757563793, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.045764662847722515, Difference Score = 3.462501424849456, Pipeline: LinearRegression(OverDominanceEncoder(SelectPercentile(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), SelectPercentile__percentile=80))) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.040032878746888545, Difference Score = 4.554748590385395, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=55)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 92%|█████████▏| 2397/2600 [12:29<01:08, 2.98pipeline/s] Entered Generation: 24 Generation 24 - Current Pareto Front: Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.11015940366044108, Difference Score = 1.752330844926403, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.09944078289867042, Difference Score = 1.9264393238899786, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.07870454042009967, Difference Score = 2.313146363747242, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.0757171847738729, Difference Score = 2.3722348048016317, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.07467464381017863, Difference Score = 2.4173397757563793, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.045764662847722515, Difference Score = 3.462501424849456, Pipeline: LinearRegression(OverDominanceEncoder(SelectPercentile(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), SelectPercentile__percentile=80))) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.040032878746888545, Difference Score = 4.554748590385395, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=55)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.03940499945765874, Difference Score = 5.600780750081931, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=95)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 96%|█████████▌| 2497/2600 [12:52<00:28, 3.62pipeline/s] Entered Generation: 25 Generation 25 - Current Pareto Front: Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.11226175244272318, Difference Score = 1.425821247194904, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.1104071686385365, Difference Score = 1.4510460170497284, Pipeline: RandomForestRegressor(OverDominanceEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=5, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.11027059398254613, Difference Score = 1.4660123055996697, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=16, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.11015940366044108, Difference Score = 1.752330844926403, Pipeline: RandomForestRegressor(HeterosisEncoder(RecessiveEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.1038131969225704, Difference Score = 1.8362455581992705, Pipeline: RandomForestRegressor(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.0), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.10122728873657616, Difference Score = 1.8532854797202751, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.10036118424002682, Difference Score = 1.8759634076354113, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.09944078289867042, Difference Score = 1.9264393238899786, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.09818379928699039, Difference Score = 2.0271312449627907, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.09451692633990316, Difference Score = 2.0515894874527523, Pipeline: RandomForestRegressor(RecessiveEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.09165549645387305, Difference Score = 2.1208860060624892, Pipeline: RandomForestRegressor(RecessiveEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.09039270731443172, Difference Score = 2.1289206846899624, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.08839493324565639, Difference Score = 2.152520389193075, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.08631310114962232, Difference Score = 2.210508366064755, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.0833094676395576, Difference Score = 2.2320258451945505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.08294731472495875, Difference Score = 2.235350979136645, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(RecessiveEncoder(UnderDominanceEncoder(VarianceThreshold(HeterosisEncoder(input_matrix), VarianceThreshold__threshold=0.05))), SelectPercentile__percentile=95)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.07933786633279194, Difference Score = 2.2399952320396532, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.07900217546410937, Difference Score = 2.2421415415313115, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.07870454042009967, Difference Score = 2.313146363747242, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.0757171847738729, Difference Score = 2.3722348048016317, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.07467464381017863, Difference Score = 2.4173397757563793, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.07448772998418773, Difference Score = 2.4550548832489154, Pipeline: RandomForestRegressor(SelectPercentile(VarianceThreshold(HeterosisEncoder(HeterosisEncoder(input_matrix)), VarianceThreshold__threshold=0.05), SelectPercentile__percentile=80), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.06845044618316154, Difference Score = 3.01082305700104, Pipeline: RandomForestRegressor(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.5, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.06760153131547952, Difference Score = 3.0688871871351817, Pipeline: DecisionTreeRegressor(SelectPercentile(HeterosisEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=5, DecisionTreeRegressor__min_samples_leaf=9, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.058616373135868094, Difference Score = 3.4497376323649096, Pipeline: RandomForestRegressor(SelectPercentile(OverDominanceEncoder(HeterosisEncoder(input_matrix)), SelectPercentile__percentile=25), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.045764662847722515, Difference Score = 3.462501424849456, Pipeline: LinearRegression(OverDominanceEncoder(SelectPercentile(HeterosisEncoder(OverDominanceEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), SelectPercentile__percentile=80))) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.045764662847722404, Difference Score = 3.462501424849498, Pipeline: LinearRegression(UnderDominanceEncoder(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80))) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.04576466284772229, Difference Score = 3.462501424849511, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=80)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.045627231527853085, Difference Score = 3.494319068550272, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(VarianceThreshold(OverDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)))) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.04417418185231403, Difference Score = 3.7535103978757633, Pipeline: LinearRegression(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.04398917551528314, Difference Score = 3.8138490652317283, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(HeterosisEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.04398054218670433, Difference Score = 3.8158165493406537, Pipeline: LinearRegression(OverDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix)))) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.04153450200198716, Difference Score = 3.9175797936453365, Pipeline: LinearRegression(SelectPercentile(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25), SelectPercentile__percentile=60)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.04028880533053392, Difference Score = 3.920754160642637, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=80)), SelectPercentile__percentile=95)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.040032878746888545, Difference Score = 4.554748590385395, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=55)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.03977396198062699, Difference Score = 5.416142697893589, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=85)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.03962318575975621, Difference Score = 5.697722723577338, Pipeline: LinearRegression(VarianceThreshold(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=90), VarianceThreshold__threshold=0.1)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.03919090585954477, Difference Score = 5.756193154084992, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=70)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.03867067075990438, Difference Score = 8.873985088286917, Pipeline: LinearRegression(SelectPercentile(UnderDominanceEncoder(input_matrix), SelectPercentile__percentile=95)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] Test R^2 = 0.038453043645077734, Difference Score = 10.285701579514154, Pipeline: LinearRegression(VarianceThreshold(UnderDominanceEncoder(input_matrix), VarianceThreshold__threshold=0.25)) Optimization Progress: 100%|█████████▉| 2597/2600 [13:22<00:00, 3.29pipeline/s] ************************************************************************************************************************************************************************************************************************** Random Seed 142 *************************************************************************************************************************************************************************************************************************************************************** Entered Generation: 1 Generation 1 - Current Pareto Front: Optimization Progress: 8%|▊ | 198/2600 [00:44<10:28, 3.82pipeline/s] Test R^2 = 0.09304256296250701, Difference Score = 1.705295638760836, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:28, 3.82pipeline/s] Test R^2 = 0.06174306771414195, Difference Score = 1.819315244269445, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:28, 3.82pipeline/s] Test R^2 = 0.05365919795356555, Difference Score = 2.3733555758963067, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:28, 3.82pipeline/s] Test R^2 = 0.03999475355497417, Difference Score = 3.7928389561004097, Pipeline: DecisionTreeRegressor(UnderDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=8) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:28, 3.82pipeline/s] Test R^2 = 0.03870224437059244, Difference Score = 3.958353992658215, Pipeline: DecisionTreeRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 8%|▊ | 198/2600 [00:44<10:28, 3.82pipeline/s] Entered Generation: 2 Generation 2 - Current Pareto Front: Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.10033358157398542, Difference Score = 1.4566329286082973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.09304256296250701, Difference Score = 1.705295638760836, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.09049812522906453, Difference Score = 1.8059373942359507, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.6500000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.07794321247276481, Difference Score = 1.8262658230202549, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.05445621470245421, Difference Score = 1.9826639295596298, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.05365919795356555, Difference Score = 2.3733555758963067, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Test R^2 = 0.040742616222773975, Difference Score = 7.09926824711473, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=3, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=7) Optimization Progress: 11%|█▏ | 298/2600 [01:09<13:39, 2.81pipeline/s] Entered Generation: 3 Generation 3 - Current Pareto Front: Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.10033358157398542, Difference Score = 1.4566329286082973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.09304256296250701, Difference Score = 1.705295638760836, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.07794321247276481, Difference Score = 1.8262658230202549, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.05365919795356555, Difference Score = 2.3733555758963067, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 15%|█▌ | 398/2600 [01:38<11:36, 3.16pipeline/s] Entered Generation: 4 Generation 4 - Current Pareto Front: Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.10109758637875443, Difference Score = 1.400720240575818, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.10033358157398542, Difference Score = 1.4566329286082973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.09304256296250701, Difference Score = 1.705295638760836, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.08067101809260946, Difference Score = 1.8155798703361377, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.08044041552682146, Difference Score = 1.8407892871711873, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.07474199321906794, Difference Score = 1.922391973993442, Pipeline: RandomForestRegressor(OverDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.05365919795356555, Difference Score = 2.3733555758963067, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 19%|█▉ | 497/2600 [02:11<09:41, 3.62pipeline/s] Entered Generation: 5 Generation 5 - Current Pareto Front: Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.10109758637875443, Difference Score = 1.400720240575818, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.10033358157398542, Difference Score = 1.4566329286082973, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.45, RandomForestRegressor__min_samples_leaf=6, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.09712227850036659, Difference Score = 1.466284569341715, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.09304256296250701, Difference Score = 1.705295638760836, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.0537168889462466, Difference Score = 2.2116008483683545, Pipeline: RandomForestRegressor(UnderDominanceEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.05365919795356555, Difference Score = 2.3733555758963067, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=9) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 23%|██▎ | 597/2600 [02:45<11:26, 2.92pipeline/s] Entered Generation: 6 Generation 6 - Current Pareto Front: Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.10184195734561252, Difference Score = 1.4759763708003861, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.09643291959682143, Difference Score = 1.5211517569167539, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.05385393086529067, Difference Score = 2.404985707785973, Pipeline: DecisionTreeRegressor(OverDominanceEncoder(input_matrix), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=12, DecisionTreeRegressor__min_samples_split=14) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Test R^2 = 0.04201033507012686, Difference Score = 9.253747873766827, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 27%|██▋ | 697/2600 [03:24<11:05, 2.86pipeline/s] Entered Generation: 7 Generation 7 - Current Pareto Front: Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.10184195734561252, Difference Score = 1.4759763708003861, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.09643291959682143, Difference Score = 1.5211517569167539, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.08383250345572135, Difference Score = 1.935161228404942, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 31%|███ | 797/2600 [03:58<10:08, 2.96pipeline/s] Entered Generation: 8 Generation 8 - Current Pareto Front: Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.08383250345572135, Difference Score = 1.935161228404942, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 34%|███▍ | 897/2600 [04:36<13:16, 2.14pipeline/s] Entered Generation: 9 Generation 9 - Current Pareto Front: Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.08383250345572135, Difference Score = 1.935161228404942, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 38%|███▊ | 997/2600 [05:16<10:24, 2.57pipeline/s] Entered Generation: 10 Generation 10 - Current Pareto Front: Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.08383250345572135, Difference Score = 1.935161228404942, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 42%|████▏ | 1097/2600 [05:57<07:02, 3.56pipeline/s] Entered Generation: 11 Generation 11 - Current Pareto Front: Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.09558384347746418, Difference Score = 1.5776672307406698, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=14, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.0877562135621297, Difference Score = 1.8925511642410462, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.08383250345572135, Difference Score = 1.935161228404942, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04237893607396326, Difference Score = 7.2766234706403194, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=10), VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 46%|████▌ | 1197/2600 [06:34<08:17, 2.82pipeline/s] Entered Generation: 12 Generation 12 - Current Pareto Front: Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.08898232264564498, Difference Score = 1.8807865147282399, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.0877562135621297, Difference Score = 1.8925511642410462, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.08621917181282501, Difference Score = 1.9445588518263872, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04237893607396326, Difference Score = 7.2766234706403194, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=10), VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 50%|████▉ | 1297/2600 [07:08<07:36, 2.85pipeline/s] Entered Generation: 13 Generation 13 - Current Pareto Front: Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.0900321702367628, Difference Score = 1.8330429765712617, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.08951676360366567, Difference Score = 1.9029067216867634, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.08621917181282501, Difference Score = 1.9445588518263872, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.08510894118663004, Difference Score = 1.9526050311201775, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.059831843752104485, Difference Score = 2.21885587151752, Pipeline: RandomForestRegressor(HeterosisEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04255823726990571, Difference Score = 6.423641709540266, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04237893607396326, Difference Score = 7.2766234706403194, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=10), VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04237076498135983, Difference Score = 7.350020050093596, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Test R^2 = 0.04215455419063352, Difference Score = 14.362924601457978, Pipeline: DecisionTreeRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), DecisionTreeRegressor__max_depth=10, DecisionTreeRegressor__min_samples_leaf=1, DecisionTreeRegressor__min_samples_split=3) Optimization Progress: 54%|█████▎ | 1397/2600 [07:45<07:58, 2.51pipeline/s] Entered Generation: 14 Generation 14 - Current Pareto Front: Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.08621917181282501, Difference Score = 1.9445588518263872, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.08510894118663004, Difference Score = 1.9526050311201775, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=15, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04259929525547623, Difference Score = 6.676928026209193, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04237893607396326, Difference Score = 7.2766234706403194, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=10), VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04237076498135983, Difference Score = 7.350020050093596, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.042299439896928415, Difference Score = 7.589258053754831, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 58%|█████▊ | 1497/2600 [08:20<07:01, 2.62pipeline/s] Entered Generation: 15 Generation 15 - Current Pareto Front: Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04259929525547623, Difference Score = 6.676928026209193, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04237893607396326, Difference Score = 7.2766234706403194, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=10), VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04237076498135983, Difference Score = 7.350020050093596, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04235546756983344, Difference Score = 8.298110864526413, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 61%|██████▏ | 1597/2600 [08:57<04:25, 3.78pipeline/s] Entered Generation: 16 Generation 16 - Current Pareto Front: Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04366761031276656, Difference Score = 5.079522929947155, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.8500000000000001, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04259929525547623, Difference Score = 6.676928026209193, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04237893607396326, Difference Score = 7.2766234706403194, Pipeline: RandomForestRegressor(UnderDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=10), VarianceThreshold__threshold=0.15)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04237076498135983, Difference Score = 7.350020050093596, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04236650352676574, Difference Score = 7.451829046994349, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04235546756983344, Difference Score = 8.298110864526413, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04215717510454331, Difference Score = 9.728035640808047, Pipeline: RandomForestRegressor(SelectPercentile(input_matrix, SelectPercentile__percentile=10), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 65%|██████▌ | 1697/2600 [09:34<06:16, 2.40pipeline/s] Entered Generation: 17 Generation 17 - Current Pareto Front: Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.097361537481639, Difference Score = 1.5658607350833338, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04418289647084861, Difference Score = 5.75486945122367, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04259929525547623, Difference Score = 6.676928026209193, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04244743040189336, Difference Score = 7.515686607898552, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04235546756983344, Difference Score = 8.298110864526413, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 69%|██████▉ | 1797/2600 [10:08<02:52, 4.66pipeline/s] Entered Generation: 18 Generation 18 - Current Pareto Front: Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.097361537481639, Difference Score = 1.5658607350833338, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 73%|███████▎ | 1897/2600 [10:41<03:54, 3.00pipeline/s] Entered Generation: 19 Generation 19 - Current Pareto Front: Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.097361537481639, Difference Score = 1.5658607350833338, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 77%|███████▋ | 1997/2600 [11:19<03:12, 3.14pipeline/s] Entered Generation: 20 Generation 20 - Current Pareto Front: Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.10603232495885029, Difference Score = 1.3303544257725641, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.097361537481639, Difference Score = 1.5658607350833338, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.04291293930459794, Difference Score = 5.854548776159338, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=10), FeatureEncodingFrequencySelector__threshold=0.35)), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 81%|████████ | 2097/2600 [11:55<02:14, 3.74pipeline/s] Entered Generation: 21 Generation 21 - Current Pareto Front: Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.10603232495885029, Difference Score = 1.3303544257725641, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.097361537481639, Difference Score = 1.5658607350833338, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.08731122744725983, Difference Score = 1.9143347205956929, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.04291293930459794, Difference Score = 5.854548776159338, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=10), FeatureEncodingFrequencySelector__threshold=0.35)), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 84%|████████▍ | 2197/2600 [12:29<02:16, 2.96pipeline/s] Entered Generation: 22 Generation 22 - Current Pareto Front: Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.10603232495885029, Difference Score = 1.3303544257725641, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.10336693417661047, Difference Score = 1.4804257930458844, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.097361537481639, Difference Score = 1.5658607350833338, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.08731122744725983, Difference Score = 1.9143347205956929, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.08692941413205146, Difference Score = 1.9234119808268177, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.08494204053913224, Difference Score = 1.9780732125834155, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.04291293930459794, Difference Score = 5.854548776159338, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=10), FeatureEncodingFrequencySelector__threshold=0.35)), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 88%|████████▊ | 2297/2600 [12:58<00:47, 6.42pipeline/s] Entered Generation: 23 Generation 23 - Current Pareto Front: Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.10603232495885029, Difference Score = 1.3303544257725641, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.10535998562461135, Difference Score = 1.3533627205195706, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.10336693417661047, Difference Score = 1.4804257930458844, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.0975398686805915, Difference Score = 1.5858684621959915, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09272479630352715, Difference Score = 1.8095935816561963, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.08731122744725983, Difference Score = 1.9143347205956929, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.08692941413205146, Difference Score = 1.9234119808268177, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.08494204053913224, Difference Score = 1.9780732125834155, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.04291293930459794, Difference Score = 5.854548776159338, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=10), FeatureEncodingFrequencySelector__threshold=0.35)), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 92%|█████████▏| 2397/2600 [13:29<00:42, 4.83pipeline/s] Entered Generation: 24 Generation 24 - Current Pareto Front: Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.10603232495885029, Difference Score = 1.3303544257725641, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.10535998562461135, Difference Score = 1.3533627205195706, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.10336693417661047, Difference Score = 1.4804257930458844, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.0975398686805915, Difference Score = 1.5858684621959915, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09322504918324304, Difference Score = 1.8218017413322263, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.08731122744725983, Difference Score = 1.9143347205956929, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.08692941413205146, Difference Score = 1.9234119808268177, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.08494204053913224, Difference Score = 1.9780732125834155, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.04291293930459794, Difference Score = 5.854548776159338, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=10), FeatureEncodingFrequencySelector__threshold=0.35)), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 96%|█████████▌| 2497/2600 [14:06<01:08, 1.51pipeline/s] Entered Generation: 25 Generation 25 - Current Pareto Front: Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.10603232495885029, Difference Score = 1.3303544257725641, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=11, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.10535998562461135, Difference Score = 1.3533627205195706, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.1052455919613563, Difference Score = 1.3905626510336293, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.10439684968122254, Difference Score = 1.4765117912692873, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.10336693417661047, Difference Score = 1.4804257930458844, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.10277165080310768, Difference Score = 1.5480477427750494, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.0975398686805915, Difference Score = 1.5858684621959915, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=10, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09699614041865456, Difference Score = 1.6016154326824494, Pipeline: RandomForestRegressor(UnderDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=7, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09610785779455888, Difference Score = 1.6546595173208283, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.25, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=8, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09509208181068296, Difference Score = 1.6833684645760376, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.4, RandomForestRegressor__min_samples_leaf=12, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09496188986389376, Difference Score = 1.7133251751652734, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=8, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.0944335649426078, Difference Score = 1.7547400799782351, Pipeline: RandomForestRegressor(OverDominanceEncoder(RecessiveEncoder(HeterosisEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.8, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09420007071446779, Difference Score = 1.7654003672379954, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.55, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=13, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.0940487878331231, Difference Score = 1.7738476117905164, Pipeline: RandomForestRegressor(OverDominanceEncoder(HeterosisEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.9000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09322772144130265, Difference Score = 1.7867854872308437, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09322504918324304, Difference Score = 1.8218017413322263, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(HeterosisEncoder(input_matrix), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.7000000000000001, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=19, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.09151443355765099, Difference Score = 1.9034081485887047, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=18, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.08731122744725983, Difference Score = 1.9143347205956929, Pipeline: RandomForestRegressor(HeterosisEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.08692941413205146, Difference Score = 1.9234119808268177, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.35000000000000003, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.08661319162228664, Difference Score = 1.9754272074281083, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.08494204053913224, Difference Score = 1.9780732125834155, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(input_matrix))), FeatureEncodingFrequencySelector__threshold=0.05), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.08283343738381788, Difference Score = 2.002265406049188, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=16, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.08157452841823642, Difference Score = 2.0737010544756505, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.0788922886889254, Difference Score = 2.10228404254795, Pipeline: RandomForestRegressor(UnderDominanceEncoder(FeatureEncodingFrequencySelector(DominantEncoder(OverDominanceEncoder(input_matrix)), FeatureEncodingFrequencySelector__threshold=0.05)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.07652433477204634, Difference Score = 2.1037451518449806, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=20, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.07318903883682326, Difference Score = 2.1253086201977225, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.07312312832268852, Difference Score = 2.161967969506899, Pipeline: RandomForestRegressor(HeterosisEncoder(input_matrix), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.06688257842928214, Difference Score = 2.1924827735340724, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.06326767559236934, Difference Score = 2.2090311660698045, Pipeline: RandomForestRegressor(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.06311066169112522, Difference Score = 2.209842688554558, Pipeline: RandomForestRegressor(UnderDominanceEncoder(DominantEncoder(OverDominanceEncoder(VarianceThreshold(SelectPercentile(input_matrix, SelectPercentile__percentile=65), VarianceThreshold__threshold=0.1)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.2, RandomForestRegressor__min_samples_leaf=18, RandomForestRegressor__min_samples_split=7, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.06010370197931747, Difference Score = 2.293144484499325, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=90)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=17, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.05957637945135197, Difference Score = 2.338890451869507, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=75)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=19, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.058809538208933754, Difference Score = 2.5817717754885576, Pipeline: DecisionTreeRegressor(DominantEncoder(OverDominanceEncoder(input_matrix)), DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=4, DecisionTreeRegressor__min_samples_split=11) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.044622679139704635, Difference Score = 3.6207786860430997, Pipeline: RandomForestRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=3, RandomForestRegressor__min_samples_split=6, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.0445373061745441, Difference Score = 3.636775610445376, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=45)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.04453730617454388, Difference Score = 3.636775610445411, Pipeline: DecisionTreeRegressor(HeterosisEncoder(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=55)), DecisionTreeRegressor__max_depth=2, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=4) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.044191061058018954, Difference Score = 5.761897369912441, Pipeline: RandomForestRegressor(HeterosisEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=20), FeatureEncodingFrequencySelector__threshold=0.0)), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.04291293930459794, Difference Score = 5.854548776159338, Pipeline: RandomForestRegressor(FeatureEncodingFrequencySelector(UnderDominanceEncoder(FeatureEncodingFrequencySelector(SelectPercentile(input_matrix, SelectPercentile__percentile=10), FeatureEncodingFrequencySelector__threshold=0.35)), FeatureEncodingFrequencySelector__threshold=0.3), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=14, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.04283795991089179, Difference Score = 6.06899914648786, Pipeline: RandomForestRegressor(SelectPercentile(SelectPercentile(input_matrix, SelectPercentile__percentile=45), SelectPercentile__percentile=20), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=1, RandomForestRegressor__min_samples_split=20, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.04263864050171495, Difference Score = 11.34404537964519, Pipeline: RandomForestRegressor(OverDominanceEncoder(UnderDominanceEncoder(HeterosisEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=15)))), RandomForestRegressor__bootstrap=True, RandomForestRegressor__max_features=0.1, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=12, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.042218922481348065, Difference Score = 12.331326286942405, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=5, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] Test R^2 = 0.04215455419063363, Difference Score = 14.362924601474944, Pipeline: RandomForestRegressor(UnderDominanceEncoder(SelectPercentile(input_matrix, SelectPercentile__percentile=10)), RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.3, RandomForestRegressor__min_samples_leaf=2, RandomForestRegressor__min_samples_split=2, RandomForestRegressor__n_estimators=100) Optimization Progress: 100%|█████████▉| 2597/2600 [14:43<00:01, 2.89pipeline/s] 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