{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 필요한 모듈 import" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.pipeline import FeatureUnion\n", "from imblearn.pipeline import make_pipeline, Pipeline\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import StratifiedKFold, GridSearchCV, train_test_split\n", "from sklearn.decomposition import PCA\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_selection import VarianceThreshold" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "apcFP = pd.read_csv('finalfingerprint_sub_apc.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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target_name_bintarget_name_tertarget_name_quatersmilesAPC2D1_C_CAPC2D1_C_NAPC2D1_C_OAPC2D1_C_SAPC2D1_C_PAPC2D1_C_F...SubFPC303SubFPC304SubFPC305SubFPC306SubFPC307bond:C=S_carbonyl_thio_genericbond:CN_amine_pri-NH2_aromaticbond:CS_sulfidebond:S=O_sulfonyl_genericring:hetero_[6_6]_O_benzopyrone_(1_4-)
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..................................................................
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587 rows × 1096 columns

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APC2D1_C_CAPC2D1_C_NAPC2D1_C_OAPC2D1_C_SAPC2D1_C_PAPC2D1_C_FAPC2D1_C_ClAPC2D1_C_BrAPC2D1_C_IAPC2D1_C_B...SubFPC303SubFPC304SubFPC305SubFPC306SubFPC307bond:C=S_carbonyl_thio_genericbond:CN_amine_pri-NH2_aromaticbond:CS_sulfidebond:S=O_sulfonyl_genericring:hetero_[6_6]_O_benzopyrone_(1_4-)
06100000000...0000601000
16120000000...0000601000
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316020000000...00001300000
410020000000...0000700000
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587 rows × 1092 columns

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" ], "text/plain": [ " APC2D1_C_C APC2D1_C_N APC2D1_C_O APC2D1_C_S APC2D1_C_P APC2D1_C_F \\\n", "0 6 1 0 0 0 0 \n", "1 6 1 2 0 0 0 \n", "2 7 3 3 1 0 0 \n", "3 16 0 2 0 0 0 \n", "4 10 0 2 0 0 0 \n", ".. ... ... ... ... ... ... \n", "582 3 2 0 3 0 0 \n", "583 2 2 0 1 0 0 \n", "584 4 1 0 2 0 0 \n", "585 4 1 0 2 0 0 \n", "586 2 1 0 2 0 0 \n", "\n", " APC2D1_C_Cl APC2D1_C_Br APC2D1_C_I APC2D1_C_B ... SubFPC303 \\\n", "0 0 0 0 0 ... 0 \n", "1 0 0 0 0 ... 0 \n", "2 0 0 0 0 ... 0 \n", "3 0 0 0 0 ... 0 \n", "4 0 0 0 0 ... 0 \n", ".. ... ... ... ... ... ... \n", "582 0 0 0 0 ... 0 \n", "583 0 0 0 0 ... 0 \n", "584 0 0 0 0 ... 0 \n", "585 0 0 0 0 ... 0 \n", "586 0 0 0 0 ... 0 \n", "\n", " SubFPC304 SubFPC305 SubFPC306 SubFPC307 \\\n", "0 0 0 0 6 \n", "1 0 0 0 6 \n", "2 0 0 0 9 \n", "3 0 0 0 13 \n", "4 0 0 0 7 \n", ".. ... ... ... ... \n", "582 0 0 0 0 \n", "583 0 0 0 0 \n", "584 0 0 0 1 \n", "585 0 0 0 0 \n", "586 0 0 0 1 \n", "\n", " bond:C=S_carbonyl_thio_generic bond:CN_amine_pri-NH2_aromatic \\\n", "0 0 1 \n", "1 0 1 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", ".. ... ... \n", "582 1 0 \n", "583 1 0 \n", "584 0 0 \n", "585 0 0 \n", "586 0 0 \n", "\n", " bond:CS_sulfide bond:S=O_sulfonyl_generic \\\n", "0 0 0 \n", "1 0 0 \n", "2 1 0 \n", "3 0 0 \n", "4 0 0 \n", ".. ... ... \n", "582 1 0 \n", "583 0 0 \n", "584 0 0 \n", "585 1 0 \n", "586 1 0 \n", "\n", " ring:hetero_[6_6]_O_benzopyrone_(1_4-) \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", ".. ... \n", "582 0 \n", "583 0 \n", "584 0 \n", "585 0 \n", "586 0 \n", "\n", "[587 rows x 1092 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from imblearn.over_sampling import *\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "seed=28\n", "X_train_bin, X_test_bin, y_train_bin, y_test_bin = train_test_split(features, target_bin, test_size=0.2, random_state=seed, stratify=target_bin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "pipe_rf_pca = Pipeline([(\"std\", MinMaxScaler()),\n", " (\"thresholder\", VarianceThreshold(threshold=0.01)),\n", " (\"PCA\", PCA()),\n", " (\"smote\", SMOTE(random_state=seed)),\n", " (\"classifier\", RandomForestClassifier(random_state=seed))])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "param_grid_pca = [{\"PCA__n_components\": [1, 2, 3],\n", " \"classifier__n_estimators\": [200, 400, 800, 1600, 3200],\n", " \"classifier__max_depth\": [5, 10, 20, 40, 80]}]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "clf_rf_pca = GridSearchCV(pipe_rf_pca, param_grid_pca, cv=cv, scoring='f1_macro', verbose=2, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 50.3s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 2.6min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 5.7min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 6.3min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('PCA',\n", " PCA(copy=True, iterated_power='auto',\n", " n_components=None,\n", " random_state=None,\n", " svd_solver='auto', tol=0.0,\n", " whiten=False)),\n", " (...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'PCA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.fit(X_train_bin, y_train_bin.values.ravel())" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PCA__n_components': 3,\n", " 'classifier__max_depth': 40,\n", " 'classifier__n_estimators': 1600}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_params_" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5894137051161377" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_score_" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.6373\n" ] } ], "source": [ "from sklearn import metrics\n", "bin_pca_predict = clf_rf_pca.predict(X_test_bin)\n", "testscore_bin_pca = metrics.f1_score(y_test_bin, bin_pca_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_bin_pca))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[78 19]\n", " [10 11]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_bin, bin_pca_predict))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Binary_APC_RF_PCA.pkl']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import joblib\n", "joblib.dump(clf_rf_pca, \"Binary_APC_RF_PCA.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LDA " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "pipe_rf_lda = Pipeline([(\"std\", MinMaxScaler()),\n", " (\"thresholder\", VarianceThreshold(threshold=0.01)),\n", " (\"LDA\", LinearDiscriminantAnalysis(solver='eigen', shrinkage='auto')),\n", " (\"smote\", SMOTE(random_state=seed)),\n", " (\"classifier\", RandomForestClassifier(random_state=seed))])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "param_grid_lda = [{\"LDA__n_components\": [1, 2, 3],\n", " \"classifier__n_estimators\": [200, 400, 800, 1600, 3200],\n", " \"classifier__max_depth\": [5, 10, 20, 40, 80]}]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "clf_rf_lda = GridSearchCV(pipe_rf_lda, param_grid_lda, cv=cv, scoring='f1_macro', verbose=2, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 11.2s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 40.2s\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 1.6min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 1.7min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('LDA',\n", " LinearDiscriminantAnalysis(n_components=None,\n", " priors=None,\n", " shrinkage='auto',\n", " solver='eigen',\n", " store_covariance=False,\n", " to...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'LDA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.fit(X_train_bin, y_train_bin.values.ravel())" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'LDA__n_components': 1,\n", " 'classifier__max_depth': 5,\n", " 'classifier__n_estimators': 400}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_params_" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6179828179451061" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_score_" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.5851\n" ] } ], "source": [ "bin_lda_predict = clf_rf_lda.predict(X_test_bin)\n", "testscore_bin_lda = metrics.f1_score(y_test_bin, bin_lda_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_bin_lda))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[73 24]\n", " [11 10]]\n" ] } ], "source": [ "print(\"오차 행렬: \\n\", confusion_matrix(y_test_bin, bin_lda_predict))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Binary_APC_RF_LDA.pkl']" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_lda, \"Binary_APC_RF_LDA.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AtomPairCount_Binary_smotx" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "seed=28\n", "X_train_bin, X_test_bin, y_train_bin, y_test_bin = train_test_split(features, target_bin, test_size=0.2, random_state=seed, stratify=target_bin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "pipe_rf_pca = Pipeline([(\"std\", MinMaxScaler()),\n", " (\"thresholder\", VarianceThreshold(threshold=0.01)),\n", " (\"PCA\", PCA()),\n", " (\"classifier\", RandomForestClassifier(random_state=seed))])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "param_grid_pca = [{\"PCA__n_components\": [1, 2, 3],\n", " \"classifier__n_estimators\": [200, 400, 800, 1600, 3200],\n", " \"classifier__max_depth\": [5, 10, 20, 40, 80]}]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "clf_rf_pca = GridSearchCV(pipe_rf_pca, param_grid_pca, cv=cv, scoring='f1_macro', verbose=2, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 10.2s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 36.5s\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 1.3min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 1.4min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('PCA',\n", " PCA(copy=True, iterated_power='auto',\n", " n_components=None,\n", " random_state=None,\n", " svd_solver='auto', tol=0.0,\n", " whiten=False)),\n", " (...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'PCA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.fit(X_train_bin, y_train_bin.values.ravel())" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PCA__n_components': 3,\n", " 'classifier__max_depth': 20,\n", " 'classifier__n_estimators': 200}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_params_" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5698515684547165" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_score_" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.5609\n" ] } ], "source": [ "from sklearn import metrics\n", "bin_pca_predict = clf_rf_pca.predict(X_test_bin)\n", "testscore_bin_pca = metrics.f1_score(y_test_bin, bin_pca_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_bin_pca))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[94 3]\n", " [18 3]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_bin, bin_pca_predict))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Binary_APC_RF_PCA_smotx.pkl']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import joblib\n", "joblib.dump(clf_rf_pca, \"Binary_APC_RF_PCA_smotx.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LDA " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "pipe_rf_lda = Pipeline([(\"std\", MinMaxScaler()),\n", " (\"thresholder\", VarianceThreshold(threshold=0.01)),\n", " (\"LDA\", LinearDiscriminantAnalysis()),\n", " (\"classifier\", RandomForestClassifier(random_state=seed))])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "param_grid_lda = [{\"LDA__n_components\": [1, 2, 3],\n", " \"classifier__n_estimators\": [200, 400, 800, 1600, 3200],\n", " \"classifier__max_depth\": [5, 10, 20, 40, 80]}]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "clf_rf_lda = GridSearchCV(pipe_rf_lda, param_grid_lda, cv=cv, scoring='f1_macro', verbose=2, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 4.1s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 28.6s\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 1.2min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 1.3min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('LDA',\n", " LinearDiscriminantAnalysis(n_components=None,\n", " priors=None,\n", " shrinkage=None,\n", " solver='svd',\n", " store_covariance=False,\n", " tol=0....\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'LDA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.fit(X_train_bin, y_train_bin.values.ravel())" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'LDA__n_components': 1,\n", " 'classifier__max_depth': 10,\n", " 'classifier__n_estimators': 800}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_params_" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5709218702004106" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_score_" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.5469\n" ] } ], "source": [ "bin_lda_predict = clf_rf_lda.predict(X_test_bin)\n", "testscore_bin_lda = metrics.f1_score(y_test_bin, bin_lda_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_bin_lda))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[79 18]\n", " [15 6]]\n" ] } ], "source": [ "print(\"오차 행렬: \\n\", confusion_matrix(y_test_bin, bin_lda_predict))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Binary_APC_RF_LDA_smotx.pkl']" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_lda, \"Binary_APC_RF_LDA_smotx.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AtomPairCount_Ternary" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "X_train_ter, X_test_ter, y_train_ter, y_test_ter = train_test_split(features, target_ter, test_size=0.2, random_state=seed, stratify=target_ter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 17.4s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 2.4min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 5.3min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 5.7min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('PCA',\n", " PCA(copy=True, iterated_power='auto',\n", " n_components=None,\n", " random_state=None,\n", " svd_solver='auto', tol=0.0,\n", " whiten=False)),\n", " (...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'PCA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.fit(X_train_ter, y_train_ter.values.ravel())" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PCA__n_components': 3,\n", " 'classifier__max_depth': 10,\n", " 'classifier__n_estimators': 400}" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_params_" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.42110064642759176" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_score_" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4122\n" ] } ], "source": [ "ter_pca_predict = clf_rf_pca.predict(X_test_ter)\n", "testscore_ter_pca = metrics.f1_score(y_test_ter, ter_pca_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_ter_pca))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[43 16 12]\n", " [14 5 7]\n", " [ 4 7 10]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_ter, ter_pca_predict))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Ternary_APC_RF_PCA.pkl']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_pca, \"Ternary_APC_RF_PCA.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LDA" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 12.6s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 1.7min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 5.3min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 5.9min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('LDA',\n", " LinearDiscriminantAnalysis(n_components=None,\n", " priors=None,\n", " shrinkage=None,\n", " solver='svd',\n", " store_covariance=False,\n", " tol=0....\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'LDA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.fit(X_train_ter, y_train_ter.values.ravel())" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'LDA__n_components': 2,\n", " 'classifier__max_depth': 10,\n", " 'classifier__n_estimators': 200}" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_params_" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4775083550460565" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_score_" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4266\n" ] } ], "source": [ "ter_lda_predict = clf_rf_lda.predict(X_test_ter)\n", "testscore_ter_lda = metrics.f1_score(y_test_ter, ter_lda_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_ter_lda))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[41 19 11]\n", " [11 9 6]\n", " [ 3 10 8]]\n" ] } ], "source": [ "print(\"오차 행렬: \\n\", confusion_matrix(y_test_ter, ter_lda_predict))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Ternary_APC_RF_LDA.pkl']" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_lda, \"Ternary_APC_RF_LDA.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AtomPairCount_Ternary_smotx" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "X_train_ter, X_test_ter, y_train_ter, y_test_ter = train_test_split(features, target_ter, test_size=0.2, random_state=seed, stratify=target_ter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 4.0s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 31.7s\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 1.3min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 1.4min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('PCA',\n", " PCA(copy=True, iterated_power='auto',\n", " n_components=None,\n", " random_state=None,\n", " svd_solver='auto', tol=0.0,\n", " whiten=False)),\n", " (...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'PCA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.fit(X_train_ter, y_train_ter.values.ravel())" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PCA__n_components': 3,\n", " 'classifier__max_depth': 40,\n", " 'classifier__n_estimators': 400}" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_params_" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.43385303939202335" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_score_" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4679\n" ] } ], "source": [ "ter_pca_predict = clf_rf_pca.predict(X_test_ter)\n", "testscore_ter_pca = metrics.f1_score(y_test_ter, ter_pca_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_ter_pca))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[62 7 2]\n", " [21 4 1]\n", " [12 2 7]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_ter, ter_pca_predict))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Ternary_APC_RF_PCA_smotx.pkl']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_pca, \"Ternary_APC_RF_PCA_smotx.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LDA" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 3.9s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 29.6s\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 1.2min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 1.3min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('LDA',\n", " LinearDiscriminantAnalysis(n_components=None,\n", " priors=None,\n", " shrinkage=None,\n", " solver='svd',\n", " store_covariance=False,\n", " tol=0....\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'LDA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.fit(X_train_ter, y_train_ter.values.ravel())" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'LDA__n_components': 2,\n", " 'classifier__max_depth': 5,\n", " 'classifier__n_estimators': 3200}" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_params_" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4798112320569735" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_score_" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4392\n" ] } ], "source": [ "ter_lda_predict = clf_rf_lda.predict(X_test_ter)\n", "testscore_ter_lda = metrics.f1_score(y_test_ter, ter_lda_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_ter_lda))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[50 14 7]\n", " [15 7 4]\n", " [ 6 8 7]]\n" ] } ], "source": [ "print(\"오차 행렬: \\n\", confusion_matrix(y_test_ter, ter_lda_predict))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Ternary_APC_RF_LDA_smotx.pkl']" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_lda, \"Ternary_APC_RF_LDA_smotx.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Quaternary(w/o SMOTE)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "seed=42\n", "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=seed, stratify=target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "pipe_rf_pca = Pipeline([(\"std\", MinMaxScaler()),\n", " (\"thresholder\", VarianceThreshold(threshold=0.01)),\n", " (\"PCA\", PCA()),\n", " (\"classifier\", RandomForestClassifier(random_state=seed))])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "clf_rf_pca_smotx = GridSearchCV(pipe_rf_pca_smotx, param_grid_pca, cv=cv, scoring='f1_macro', verbose=2, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 21.0s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 1.9min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 4.3min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 4.7min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('PCA',\n", " PCA(copy=True, iterated_power='auto',\n", " n_components=None,\n", " random_state=None,\n", " svd_solver='auto', tol=0.0,\n", " whiten=False)),\n", " (...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'PCA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca_smotx.fit(X_train_quater, y_train_quater.values.ravel())" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PCA__n_components': 3,\n", " 'classifier__max_depth': 40,\n", " 'classifier__n_estimators': 200}" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca_smotx.best_params_" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3872162012014563" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca_smotx.best_score_" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4400\n" ] } ], "source": [ "quater_pca_predict = clf_rf_pca_smotx.predict(X_test_quater)\n", "testscore_quater_pca = metrics.f1_score(y_test_quater, quater_pca_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_quater_pca))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[12 11 1 1]\n", " [ 5 30 8 3]\n", " [ 2 16 6 2]\n", " [ 0 13 1 7]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_quater, quater_pca_predict))" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Quaternary_APC_RF_PCA_smotx.pkl']" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_pca_smotx, \"Quaternary_APC_RF_PCA_smotx.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LDA" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "pipe_rf_lda_smotx = Pipeline([(\"std\", MinMaxScaler()),\n", " (\"thresholder\", VarianceThreshold(threshold=0.01)),\n", " (\"LDA\", LinearDiscriminantAnalysis()),\n", " (\"classifier\", RandomForestClassifier(random_state=seed))])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "clf_rf_lda_smotx = GridSearchCV(pipe_rf_lda_smotx, param_grid_lda, cv=cv, scoring='f1_macro', verbose=2, n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 18.0s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 1.3min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 3.8min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 4.2min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('LDA',\n", " LinearDiscriminantAnalysis(n_components=None,\n", " priors=None,\n", " shrinkage=None,\n", " solver='svd',\n", " store_covariance=False,\n", " tol=0....\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'LDA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda_smotx.fit(X_train_quater, y_train_quater.values.ravel())" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'LDA__n_components': 2,\n", " 'classifier__max_depth': 5,\n", " 'classifier__n_estimators': 200}" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda_smotx.best_params_" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.41243676011223673" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda_smotx.best_score_" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.3953\n" ] } ], "source": [ "quater_lda_predict = clf_rf_lda_smotx.predict(X_test_quater)\n", "testscore_quater_lda = metrics.f1_score(y_test_quater, quater_lda_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_quater_lda))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[17 8 0 0]\n", " [11 20 4 11]\n", " [ 4 15 2 5]\n", " [ 1 5 4 11]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_quater, quater_lda_predict))" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Quaternary_APC_RF_LDA_smotx.pkl']" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_lda_smotx, \"Quaternary_APC_RF_LDA_smotx.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Quaternary (w/ SMOTE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCA" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 20.3s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 2.1min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 5.1min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 5.5min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('PCA',\n", " PCA(copy=True, iterated_power='auto',\n", " n_components=None,\n", " random_state=None,\n", " svd_solver='auto', tol=0.0,\n", " whiten=False)),\n", " (...\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'PCA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.fit(X_train_quater, y_train_quater.values.ravel())" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PCA__n_components': 3,\n", " 'classifier__max_depth': 10,\n", " 'classifier__n_estimators': 1600}" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_params_" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.38241835802914825" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_pca.best_score_" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4420\n" ] } ], "source": [ "quater_smot_pca_predict = clf_rf_pca.predict(X_test_quater)\n", "testscore_quater_smot_pca = metrics.f1_score(y_test_quater, quater_smot_pca_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_quater_smot_pca))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[12 10 2 1]\n", " [ 5 18 13 10]\n", " [ 3 11 8 4]\n", " [ 1 6 1 13]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "print(\"오차 행렬: \\n\", confusion_matrix(y_test_quater, quater_smot_pca_predict))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Quaternary_APC_RF_PCA_smot.pkl']" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_pca, \"Quaternary_APC_RF_PCA_smot.pkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LDA" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 75 candidates, totalling 375 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 21.5s\n", "[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 1.8min\n", "[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 2.9min\n", "[Parallel(n_jobs=-1)]: Done 375 out of 375 | elapsed: 3.1min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=28, shuffle=True),\n", " error_score=nan,\n", " estimator=Pipeline(memory=None,\n", " steps=[('std',\n", " MinMaxScaler(copy=True,\n", " feature_range=(0, 1))),\n", " ('thresholder',\n", " VarianceThreshold(threshold=0.01)),\n", " ('LDA',\n", " LinearDiscriminantAnalysis(n_components=None,\n", " priors=None,\n", " shrinkage=None,\n", " solver='svd',\n", " store_covariance=False,\n", " tol=0....\n", " n_estimators=100,\n", " n_jobs=None,\n", " oob_score=False,\n", " random_state=28,\n", " verbose=0,\n", " warm_start=False))],\n", " verbose=False),\n", " iid='deprecated', n_jobs=-1,\n", " param_grid=[{'LDA__n_components': [1, 2, 3],\n", " 'classifier__max_depth': [5, 10, 20, 40, 80],\n", " 'classifier__n_estimators': [200, 400, 800, 1600,\n", " 3200]}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring='f1_macro', verbose=2)" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.fit(X_train_quater, y_train_quater.values.ravel())" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'LDA__n_components': 3,\n", " 'classifier__max_depth': 10,\n", " 'classifier__n_estimators': 400}" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_params_" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.41292303367912425" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rf_lda.best_score_" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "f1 score (test): 0.4205\n" ] } ], "source": [ "quater_smot_lda_predict = clf_rf_lda.predict(X_test_quater)\n", "testscore_quater_smot_lda = metrics.f1_score(y_test_quater, quater_smot_lda_predict, average='macro')\n", "\n", "print(\"f1 score (test): {:.4f}\".format(testscore_quater_smot_lda))" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "오차 행렬: \n", " [[17 6 1 1]\n", " [11 15 6 14]\n", " [ 3 7 7 9]\n", " [ 1 2 7 11]]\n" ] } ], "source": [ "print(\"오차 행렬: \\n\", confusion_matrix(y_test_quater, quater_smot_lda_predict))" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Quaternary_APC_RF_LDA_smot.pkl']" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joblib.dump(clf_rf_lda, \"Quaternary_APC_RF_LDA_smot.pkl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 2 }