{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hemato_paper.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NcfJJonJucQ",
        "outputId": "147265b1-7f79-4470-fd92-676c6c80a534"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TykDF9Kge3h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15595187-1630-4e4c-a081-d005020b8577"
      },
      "source": [
        "!pip install optuna\n",
        "!pip install eli5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting optuna\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/67/ed0af7c66bcfb9a9a56fbafcca7d848452d78433208b59b003741879cc69/optuna-2.6.0-py3-none-any.whl (293kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/d6/7d9acb68a77acd140be7fececb7f2701b2a29d2da9c54184cb8f93509590/cliff-3.7.0-py3-none-any.whl (80kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fb/48/69046506f6ac61c1eaa9a0d42d22d54673b69e176d30ca98e3f61513e980/pbr-5.5.1-py2.py3-none-any.whl (106kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/49/b602307aeac3df3384ff1fcd05da9c0376c622a6c48bb5325f28ab165b57/stevedore-3.3.0-py3-none-any.whl (49kB)\n",
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            "\u001b[?25hCollecting Mako\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/54/dbc07fbb20865d3b78fdb7cf7fa713e2cba4f87f71100074ef2dc9f9d1f7/Mako-1.1.4-py2.py3-none-any.whl (75kB)\n",
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            "\u001b[?25hRequirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from alembic->optuna) (2.8.1)\n",
            "Collecting python-editor>=0.3\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from sqlalchemy>=1.1.0->optuna) (3.8.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from sqlalchemy>=1.1.0->optuna) (1.0.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from PrettyTable>=0.7.2->cliff->optuna) (0.2.5)\n",
            "Collecting pyperclip>=1.6\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/2c/4c64579f847bd5d539803c8b909e54ba087a79d01bb3aba433a95879a6c5/pyperclip-1.8.2.tar.gz\n",
            "Collecting colorama>=0.3.7\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->sqlalchemy>=1.1.0->optuna) (3.7.4.3)\n",
            "Building wheels for collected packages: pyperclip\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyperclip: filename=pyperclip-1.8.2-cp37-none-any.whl size=11107 sha256=5cddb21ae0fa1590cdb68650cf8494cac03946c3dfcde16749dd4fdf183c446d\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/af/b8/3407109267803f4015e1ee2ff23be0c8c19ce4008665931ee1\n",
            "Successfully built pyperclip\n",
            "Installing collected packages: colorlog, pyperclip, colorama, cmd2, pbr, stevedore, cliff, Mako, python-editor, alembic, cmaes, optuna\n",
            "Successfully installed Mako-1.1.4 alembic-1.5.8 cliff-3.7.0 cmaes-0.8.2 cmd2-1.5.0 colorama-0.4.4 colorlog-4.8.0 optuna-2.6.0 pbr-5.5.1 pyperclip-1.8.2 python-editor-1.0.4 stevedore-3.3.0\n",
            "Collecting eli5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/54/04cab6e1c0ae535bec93f795d8403fdf6caf66fa5a6512263202dbb14ea6/eli5-0.11.0-py2.py3-none-any.whl (106kB)\n",
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            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from eli5) (1.4.1)\n",
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            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->eli5) (1.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->eli5) (1.1.1)\n",
            "Installing collected packages: eli5\n",
            "Successfully installed eli5-0.11.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99reL88piH_t"
      },
      "source": [
        "# Load and preprocess data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FyU7E84ilnoQ",
        "outputId": "0509f272-16cc-4126-b900-e9cb04eb9f67"
      },
      "source": [
        "import os, glob\n",
        "dataset_paths = glob.glob('/content/drive/My Drive/hemato/data/*')\n",
        "\n",
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/hemato/modules/')\n",
        "\n",
        "import re\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.font_manager import FontProperties\n",
        "import matplotlib\n",
        "matplotlib.rcParams['font.family'] = 'IPAGothic'\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "\n",
        "import my_utility, my_models\n",
        "import importlib\n",
        "importlib.reload(my_utility)\n",
        "# Read cleaned data\n",
        "GVHD_CLEANED_SAMPLE_PATH = '/content/drive/My Drive/hemato/data/BonBonPJ_dataset_20200528reduced_cleaned.csv'\n",
        "\n",
        "df_cleaned = pd.read_csv(GVHD_CLEANED_SAMPLE_PATH, index_col=0, header=0, sep=',', dtype=object)\n",
        "number_var_lst = ['do_age','tyear','dx_to_sct','pt_age', 'ks_tx2','hctci_points_corr',\n",
        "                  'bm_tnc_total_harvest_kg','pb_tnc_total_cryop_kg','pb_cd34_total_kg']\n",
        "df_cleaned[number_var_lst] = df_cleaned[number_var_lst].astype(float) # avoid error caused by nan type\n",
        "\n",
        "# MLevent_agvhd2434: NY　をYYに\n",
        "df_cleaned.loc[pd.Series([i+j for i,j in zip(df_cleaned.loc[:,'MLevent_agvhd24'],df_cleaned.loc[:,'MLevent_agvhd34'])],index=df_cleaned.index)=='NY','MLevent_agvhd24'] = 'Y'\n",
        "\n",
        "# Cleaning\n",
        "my_utility.clean_data_type(df_cleaned)\n",
        "my_utility.nan2string_object(df_cleaned)\n",
        "\n",
        "df_cleaned_raw = df_cleaned.copy()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.bagging module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "findfont: Font family ['IPAGothic'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0 tid :  object\n",
            "nan %:  0.0\n",
            "['T19000010-1' 'T19000017-1' 'T19000023-1' ... 'T19092197-1' 'T19092201-1'\n",
            " 'T19092203-1']\n",
            "Number of unique class is  14008\n",
            "\n",
            "\n",
            "1 ps_tx :  object\n",
            "nan %:  0.05711022272986865\n",
            "['3' '0' '1' '2' nan '4']\n",
            "Number of unique class is  6\n",
            "0    7840\n",
            "1    5331\n",
            "2     645\n",
            "3     151\n",
            "4      33\n",
            "Name: ps_tx, dtype: int64\n",
            "\n",
            "\n",
            "2 rbc_transfusion :  object\n",
            "nan %:  16.833238149628784\n",
            "['20回以上' '1-9回' '10-19回' '0回' nan]\n",
            "Number of unique class is  5\n",
            "1-9回      4197\n",
            "20回以上     3039\n",
            "10-19回    3022\n",
            "0回        1392\n",
            "Name: rbc_transfusion, dtype: int64\n",
            "\n",
            "\n",
            "3 plt_transfusion :  object\n",
            "nan %:  16.78326670474015\n",
            "['20回以上' '1-9回' '10-19回' '0回' nan]\n",
            "Number of unique class is  5\n",
            "20回以上     4012\n",
            "1-9回      3245\n",
            "10-19回    2666\n",
            "0回        1734\n",
            "Name: plt_transfusion, dtype: int64\n",
            "\n",
            "\n",
            "4 do_sex :  object\n",
            "nan %:  0.1427755568246716\n",
            "['女' '男' nan]\n",
            "Number of unique class is  3\n",
            "男    8793\n",
            "女    5195\n",
            "Name: do_sex, dtype: int64\n",
            "\n",
            "\n",
            "5 do_age :  float64\n",
            "nan %:  0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "6 pt_abo :  object\n",
            "nan %:  0.0356938892061679\n",
            "['O' 'B' 'A' 'AB' nan]\n",
            "Number of unique class is  5\n",
            "A     5367\n",
            "O     4185\n",
            "B     3031\n",
            "AB    1420\n",
            "Name: pt_abo, dtype: int64\n",
            "\n",
            "\n",
            "7 do_abo :  object\n",
            "nan %:  0.028555111364934323\n",
            "['O' 'A' 'B' 'AB' nan]\n",
            "Number of unique class is  5\n",
            "A     5441\n",
            "O     4271\n",
            "B     3014\n",
            "AB    1278\n",
            "Name: do_abo, dtype: int64\n",
            "\n",
            "\n",
            "8 tyear :  float64\n",
            "nan %:  0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SsfW/I+J0YDkwH7gmInqBM4DTUkpX5WkWAbdFxIKU0nX5dN6WD5sJ7NM4n4jYAzgT2CuldEfe++4ieZUkSZIkqYoGe494b/65Iv+cT3aW/MpagpTS7cC9wMEFpnsi8CfghIi4OyKWRcTXImJGqxEiYkJE9NQ6YFqRBemUZX95otNZkCRJkiSVaMCBeESMAc4Hrk0p3ZL3ng2sSyk91pD8kXxYu3YCdiC7vP21wOlkQf73+hjnHGBlXXd/gfl1zB+XP97pLEiSJEmSSlTo0vQGi4G9gEOHKC/1xgATgNemlO4EiIgzgKURMa/ucvV6HwM+Xfd7GiMkGJckSZIkdY8BnRGPiC8AJwAvSCnVB7sPA+MjYnrDKLPyYe16CHi6FoTnbss/5zQbIaW0NqW0qtYBqwvMT5IkSZKkUhR9fVnkQfhJwBEppcYHqC0FngKOrBtnHlnwvKTArK4FxkbEznX9dss/7ymSZ0mSJEmSqqTopemLgdOAlwCrI6J23/fKlNKTKaWVEXEB8OmIWAGsAj4PLKk9MR0gInYBppLdNz4pIvbLB92aUlpH9rC33wIXRsQ7yP4wWAz8rOEsuSRJkiRJI0rRQPzM/PPqhv6LgIvy72cB64FLyO7zvgJ4c0P6r5G9Y7zmd/nnXGBZSml9RJxIFsRfAzwB/Bh4Z8H8SpIkSZJUKUXfIx5tpFkDvCXvWqVZ2MZ0HgReXiR/kiRJkiRV3WDfIy5JkiRJkgowEJckSZIkqUQG4pIkSZIklchAXJIkSZKkEhmIS5IkSZJUIgNxSZIkSZJKZCAuSZIkSVKJDMQlSZIkSSqRgbgkSZIkSSUyEJckSZIkqUQG4pIkSZIklchAXJIkSZKkEhmIS5IkSZJUIgNxSZIkSZJKZCAuSZIkSVKJDMQlSZIkSSqRgbgkSZIkSSUyEJckSZIkqUQG4pIkSZIklchAXJIkSZKkEhmIS5IkSZJUIgNxSZIkSZJKZCAuSZIkSVKJDMQlSZIkSSqRgbgkSZIkSSUyEJckSZIkqUQG4pIkSZIklchAXJIkSZKkEhmIS5IkSZJUIgNxSZIkSZJKVCgQj4hzIuKGiFgdEcsj4rKImNeQZmJELI6IRyPi8Yi4JCJmNaT5XEQsjYi1EXFjP/PcJZ/fY0XyKkmSJElSFRU9I344sBhYABwNjAN+GhFT6tJ8BjgRODlPvw1waZNpXQhc3NfMImIc8G/ALwvmU5IkSZKkShpbJHFK6dj63xFxOrAcmA9cExG9wBnAaSmlq/I0i4DbImJBSum6fDpvy4fNBPbpY5YfBm4Hfg4cUiSvkiRJkiRV0WDvEe/NP1fkn/PJzpJfWUuQUroduBc4uMiEI+IIsrPqb2kz/YSI6Kl1wLQi85MkSZIkqQwDDsQjYgxwPnBtSumWvPdsYF1KqfF+7kfyYe1OewvgIuD0lNKqNkc7B1hZ193f7vwkSZIkSSrLYM6ILwb2Ak4dorzU+yrwnZTSNQXG+RjZGfpat90w5EuSJEmSpEEZUCAeEV8ATgBekFKqP/P8MDA+IqY3jDIrH9auI4B3RcTTEfE0cAHQm/9+fbMRUkprU0qrah2wusD8JEmSJEkqRaGHtUVEAJ8HTgIWppTubkiyFHgKOBK4JB9nHjAHWFJgVgcDm9X9fglwNtkD2x4okmdJkiRJkqqkUCBOdjn6aWSB8eqIqN33vTKl9GRKaWVEXAB8OiJWAKvIAvcltSemQ/ZucGAq2X3jkyJiv3zQrSmldSml2+pnGhEHAOvr7kWXJEmSJGlEKhqIn5l/Xt3QfxHZw9UAzgLWk50RnwBcAby5If3XyN4xXvO7/HMusKxgniRJkiRJGjGKvkc82kizhuyVYy1fO5ZSWlhwvhfxbKAvSZIkSdKINdj3iEuSJEmSpAIMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSGYhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSGYhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSGYhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSFQrEI+KciLghIlZHxPKIuCwi5jWkmRgRiyPi0Yh4PCIuiYhZDWk+FxFLI2JtRNzYZD4LI+L7EfFQRDwRETdGxKsGtoiSJEmSJFVH0TPihwOLgQXA0cA44KcRMaUuzWeAE4GT8/TbAJc2mdaFwMUt5nMIcDPwcmAf4OvANyPihIL5lSRJkiSpUsYWSZxSOrb+d0ScDiwH5gPXREQvcAZwWkrpqjzNIuC2iFiQUroun87b8mEzyQLtxvl8tKHXZyPihcDLgB8WybMkSZIkSVUy2HvEe/PPFfnnfLKz5FfWEqSUbgfuBQ4egnmt6DeVJEmSJEkVVuiMeL2IGAOcD1ybUrol7z0bWJdSeqwh+SP5sIHO65XAc4E39ZFmAjChrte0gc5PkiRJkqThMpgz4ouBvYBThygvTUXEC8juEf+HlNIf+kh6DrCyrrt/OPMlSZIkSdJADCgQj4gvACcAL0gp1Qe8DwPjI2J6wyiz8mFF53M48APgrJTSN/tJ/jGyy9dr3XZF5ydJkiRJ0nAr+vqyyIPwk4AjUkp3NyRZCjwFHFk3zjxgDrCk4LwWApcDZ6eUvtJf+pTS2pTSqloHrC4yP0mSJEmSylD0HvHFwGnAS4DVEVG773tlSunJlNLKiLgA+HRErABWAZ8HltSemA4QEbsAU8nuG58UEfvlg25NKa3LL0f/IfBZ4JK6+axLKfnANkmSJEnSiFU0ED8z/7y6of8i4KL8+1nAeuASsoenXQG8uSH918jeMV7zu/xzLrAMeB0wmey+73Pq0v0CWFgwz5IkSZIkVUbR94hHG2nWAG/Ju1ZpFvYzjdOB04vkTZIkSZKkkWCw7xGXJEmSJEkFGIhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSGYhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSGYhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRAbikiRJkiSVyEBckiRJkqQSGYhLkiRJklQiA3FJkiRJkkpkIC5JkiRJUokMxCVJkiRJKpGBuCRJkiRJJTIQlyRJkiSpRIUC8Yg4JyJuiIjVEbE8Ii6LiHkNaSZGxOKIeDQiHo+ISyJiVkOaz0XE0ohYGxE3tpjXPhHxy4hYExH3RcR7ii+eJEmSJEnVUvSM+OHAYmABcDQwDvhpREypS/MZ4ETg5Dz9NsClTaZ1IXBxs5lERA/wU+AeYD7wbuDciHhjwfxKkiRJklQpY4skTikdW/87Ik4HlpMFy9dERC9wBnBaSumqPM0i4LaIWJBSui6fztvyYTOBfZrM6lXAeOD1KaV1wB8iYj/gn4CvFMmzJEmSJElVMth7xHvzzxX553yys+RX1hKklG4H7gUOLjDdg4Fr8iC85gpgXkRs3myEiJgQET21DphWYH6SJEmSJJViwIF4RIwBzgeuTSndkveeDaxLKT3WkPyRfFi7ZufjNE6jNqyZc4CVdd39BeYnSZIkSVIpBnNGfDGwF3DqEOVlsD5Gdoa+1m3X2eyoCk4/ZMcBj/uLdy9k2XnH8+oFc4YuQxX26/cd2eksSJI0KO87bo9OZ0GS2lLoHvGaiPgCcAJwWEqp/szzw8D4iJjecFZ8Vj6sXQ/n49SbVTdsEymltcDaujwWmJ2kwDojSZIklaHo68siD8JPAo5IKd3dkGQp8BRwZN0484A5wJICs1oCHBYR4+r6HQ3ckVL6a5E8S4PVLQHqmO5YTEmSJKnjil6avhh4NXAasDoiZufdJICU0krgAuDTEfGCiJgPfB1YUntiOkBE7JI/BX02MCki9su78XmS7wDrgAsiYs+IOAV4O/DpQSyrpD6M8SoSSdIIl0idzoIktaXopeln5p9XN/RfBFyUfz8LWA9cAkwge9r5mxvSf43sHeM1v8s/5wLLUkorI+KFZIH/UuAvwIdSSr66TBomxuGSJElSOYq+R7zfQ/WU0hrgLXnXKs3CNqZzM/D8IvmTNHA+V0GSJEkqx2DfIy5plPAecUnSSJe8Ml3SCGEgLgnwjLgkSZJUFgNxSYBnxCVJkqSyGIhLAnxquiRJklQWA3GpH8ankiRJkoaSgbgkwDPikiRJUlkMxCUB3iMuSRr5fGi6pJHCQFxqIeiuyNSnpkuSJEnlMBCXWkhd9r+6Z8QlSZKkchiISwI8Iy5JGvlSd/2HLmkEMxCXWui2S9MlSZIklcNAXOqH4bgkSSODF3dJGikMxKUWuu0ecUmSRjovTZc0UhiIS/1YsNMWnc6CJElq0/F7b93pLEhSvwzEO2zb6ZM2+v2GQ+d2KCeZWT0TBjTePtv1DnFOivnG6w8c8mnW7hE/dq/ZLdPMmDJ+yOfbl9MP2XFYp/+jtz2ffbefPqzz0LP+xxG7dDoLA/ay/bctbV5vP3JX3vXC3fpMc+bCnUvKzfD68qv3Z/zYcnfNu82aWur8RpMJJZdVlRyww+aFxznrqL7r8VA5/9T9SplPlWzdO5Fvn3EQ33z9gXzmlH07nR1V3Mv3347nbN3T6Wx0ve7dg1TEzGkbB757dzigvf5/HjWg8craubZy+G4zh23arZ4mPibgtx84etjm28y5L95zWKf/nG16OGjujGGdR6NObztDYffZ0wqPc/w+W/POF84b0nzsP6e8P1EO23X46lyjbadP4q1H7NpnmkWH7Mj4zVrv0voL5F95wHYDyttQO3avrZkxudw/+F4xvxrLPhLNmTG501nomDc8f6fC4xy5x1bDkJONJRLj+mgLytR4smU4vfOF8zh01y05bLeZnPR31umynHLA9p3OwoB86pX7clTB+jiQP9/Ut2q0VJIqw+fcFLdZRV7CXuYr6Cr5DIU+Ft/7RiVJQ82HA2owDMQ1JCp5UK4RYTTsxKoSiKdujzYHsfi+rlAD0eU1Tup6Zf4BPuRGct5HCQNxSRsruV0eDbuBMe7MRrxuLkL/hBi4rv/zq6Buq2dlLq/bYmd02zatoWUg3mGjpdnsxgO5Ef0vaB+6sSwHqyInxMu9NL2KjdcgFn+UVmcNsypWA1WH7croZxFrMAzEpRa6dQfarcs9GJ4RH2ZtrN7BB0SWoYqr5B9S6kqj9eSAhk/RLcZNbOgZiEsteIBVjtGwmsdU5ZS4WurvAKJKBxhlP3PDZ3wMXHdfDlzNZa9SkZR5hVl3b4udM5L/iB/BWR81DMQlddRo2A9UJQ4v80DMYz5JUrfrpmDW/f7QMxDvtNGyVXdRQzTalV2Uo2EnVpWnpmvgurkEfS7EwI2SPbiGyWjYv6lvI7mIbfs7z0BcaqFbd6DdutyDUZVL07xHUCrX+tHyZ/qAFG9vPPDXaON+V4NhIC610K3HVx4oFVeVQLxMI6169Fefu7AINQS6dT8haeQrut9zPzn0DMQlaZC8NH3k8w8oDYSBuPpiqzL6dVNwans39AzEO2y0bNOjsR3q9ynL5WSjdGXvVEZDw24c3nmD3Y666WCqkU9Nl6SB6cYr4jR0DMQlddRo2Id14z1iVXxVTl+l0IVFJFVOt9XDMvcN1WuRu8NI3qRHct5HCwNxqYUKxhmlsGEubrOKHF1WIxedM5gq283rzsvyB66Kf0ipOqxZo19Fdv8aoQoF4hFxTkTcEBGrI2J5RFwWEfMa0kyMiMUR8WhEPB4Rl0TErIY0cyLi8oj4Wz6dT0TE2IY0r4qIm/I0D0XEhRGxxcAXVZKGh/eIj3zdeFWDBs8wvHr8c0RlGsn7jhGc9VGj6Bnxw4HFwALgaGAc8NOImFKX5o7YB3gAABMWSURBVDPAicDJefptgEtrAyNiM+ByYDxwCPA64HTgQ3Vpngd8E7gA2DOf1oHAVwvmt/JGy/5iJDdErYzCRWpP1y74wHXjKqti09WFxaAOGy378LJ0XVvZbcvbhUZyEY/GY/eRZmz/SZ6VUjq2/ndEnA4sB+YD10REL3AGcFpK6ao8zSLgtohYkFK6Dngh8BzgqJTSI8CNEfEB4OMRcW5KaR1wMLAspfS5fFZ3R8S/AmcPdEEltcdmuTjPiI98Ho9oIHzQndTl3HdoEAZ7j3hv/rki/5xPdpb8ylqClNLtwL1kwTX55+/zILzmCqCH7Ow3wBJg+4g4LjKzgFcAP2qVkYiYEBE9tQ6YNrhFUxFVbIfGxOAOrrv1vcNlPwF0NPwjO3ZM8aZ0OJZ6FKzKAUukPrfd/razKt0n7VN4Rw7Lqphuu4LA7WP0G8ll7HvEO2/AgXhEjAHOB65NKd2S954NrEspPdaQ/JF8WC3NI02G14aRUroWeBVwMbAOeBhYCbyljyydk6epdfcXXKRKOGbP2XzghOew5dQJXLTouS3TbTl1wpDNc/zYbDM449C5AHz/Lc/bMOw9x87jM6fsy4wp4/nuGxew++xpTJ2w8YUU55+yHwt2au/2/fGbjWHOjMkb9XvTYTtx2kFz2GGLyczumdjvNHaa+eydENvPmMRxe2eb1rF7zt4o3bfPOIgzF+68yfjP22XTvD5n656Nfu+7XS/bbT5pw+8vvWp/ZkwZz4dfuteGft98/UEAfPqV+27oN33yOPbetpe+9NWQnXvic7j4jQuaDvvOG7L5feCE5zQd/vL9t2s53fr1sOXUCey05ZSNhl/xjsM2fH/dITu0ziD0uV1Ctg7aNXHcGM44dC67bjV1o/7zZvX/P1qZJ6HHBJts9zVnHDqXf3rhbht+H7rLlvROar0Opk8ex8xpEzj72N0BeOl+2/Q7/22nT+LtR+7KzGkTOGbPWWzTu2k9OXSXLfn4y/fZ8HvLqRP6bCcat5dj9swe5fH2I3flPcfOazYKkJXZjltM5oR9tubbZxzE5pPHMatnAtvPmLRRXWjHntv08NqDd2DGlPGbDHvvi3bn7w/cfsO8anmb1TNhQ17rze6ZyEWLnsvk8Zs1nderD9qBbadP4oxD5/KV18xnxpTxfPP1B/LWF+zC7J6J/OPCnfjfL96z6bgApx+y4yb9Xrb/tv0u4zuO2pWXtFHG8Gzb9MVX7c8WU8Yzadymy7LDFpM36dfo/cfvwbbTs/arcd2e97K9mTFlPLN6sm1j6oSxnHLg9jx/1y03pNl3u77bsAtedwDTJha6oK5PbzpsJzavazfefUzr7a9my6mbbjPNtLNPGYyLFh244Xv9OgTYukk9bayTO/ZTnltOfbas6s2YMp43HbZTkaxyxO5bMXHcxod9zepSvT227tmkfYZs37tw3kxO3Le9bbtmt1nTNllP7ahtzzUHzZ2x4fuJ+27Dh17ybN19zYIdAXjtwdm+rNn6g6ye7NGw7x9q55+yX9Pt+fDdZm7Sb+eZUzbp10z9cUi9WjtZU98+TWux/6o3JmDvbXsZW7dzPXSXLXnZ32XTmTJ+M/7ncbtvGHbey/ZuOa05MyYzpUlbfNQeW7Uc59Tnbg/QdP9Z2+cNZNtptr8cjMZjtDccOpcPNjkua7fdrzdzWratNh7P1jTbV37y5Gf3u822q3q75HW5tj971YHZfvH1z5u7UR1qdNhuM5nVM4HP//3+G45xD5w7g3NetPtG6erLrnF7bObvD9y+af/pk8dx1lG7NR0G8K+vmd/vtEeKGOhDLSLiS8CLgENTSvfn/U4Dvp5SmtCQ9tfAf6aUzo6IrwA7pJSOqRs+GXgCOC6l9OOIeA7ZWfXPkJ0t3xr4BHBDSumMFvmZANTPdxpw/8qVK+npGd6GdjCeemY9KWWXtmZncLMGMKVERDR96Eir/tl4mwZ6faWvH1Z/xqixX31+GqdXn6ZVnrJ0TQf3ucztLE+zfPeVr9r4td5jxjSff+MZtL6GN863Ps369YkxY1qvu/7yXFsHjfNrXK/Nyrl+3Gbzrf9sXNZWGpev1Ta6fn0tzabTqC/XZttVfRk1y39fGreZZuMWmV5j+v7KrL9pN1vn7eSlVf1rlc/G3/XrJaWNt/tm+arfxrLhzfPfqm1oVnfbyXN9//rp17SaX1/tULNp9fW9cTlbrffG7bRVe1U/3b626WbLuX59ajrtdvLV7rBmy16bb32e+2tP6n/Xj9+f2vxr47RTP1ulaaf+Nw7rr34PtD1sZ5vpa7z+6nyz7W/MmP63mVb5b2cZG/tD3/v+/upDY9pm49SWq6/9bP00W7UXrZar3f1B0WOvZnlrNa1228vGcRu3o03z1V7Z1q/jvrbjVt+blVF/eX82j+3v59ut90XyVFS7x8D9LU9fZV4/7vrEhvig1bFhf8cfjWmb1ZFmy9CYnyLtY3/1biDj1x9TV92qVavo7e0F6E0prWqVbkB/aUfEF4ATgMNSHoTnHgbGR8T0tPFZ8Vn5sFqaA9nYrLphkJ3dvjal9In8980R8QTwy4h4f0rpocY8pZTWAmvr8jiAJSvfuM2aX5RQy3+r5Wjdv/l8+lofzYY19mvMTzvj9JenVuPXT6fo8rSbr4hoeYDQ37SLrC94NuBpNX5/02u1c+9rp99s3P7KtL88tc5f87R9NZTtrPvGZWy3TrezXopMr1n6/sus/fVXJC/9baN9TXvjddk8fV/rv9U8WrUNRVbvwJan/frUbFiR7/3VlWcPhFvOukU59J/XZ9uPvqbZenssMqxZu1U/73bak2bjt2vjefY/fvNlay9d47C+6u9g2sOi+59W+eprv9RYPu1sM/31K5JmIPv+Vvuq5nluNU77+9KB7Nf7St8qj0XGa+dYp7/pDWS+zdI0ruO+1m3zNrLY/Db+XTy/m/Zrlq79aRZV5Fiz9TTam8dmLffXrfr3vX7aLdtW+RnoPncw86w3EoLwIoq+viwiC8JPAo5IKd3dkGQp8BRwZN0484A5ZPd9k3/uHRH116ccDawCbs1/TwbWN0z7mdoki+RZkiRJkqQqKXpGfDFwGvASYHVE1G5iWJlSejKltDIiLgA+HREryILrzwNLUvbEdICfkgXc34qI95DdF/5hYHF+VhvgB8BXI+JMnr00/Xzg1ymlBwe0pJIkSZIkVUDRQPzM/PPqhv6LgIvy72eRnc2+hOye7SuAN9cSppSeiYgTgC+RnR1/AvgG8MG6NBdFxDTgrcCngMeAq/D1ZZIkSZKkEW7AD2urusheYbay6g9rkyRJkiSNDu0+rG2w7xGXJEmSJEkFGIhLkiRJklSiAb2+bCRZtarl1QCSJEmSJA2ZduPP0XyP+LbA/f0mlCRJkiRpaG2XUnqg1cDRHIgHsA2wutN56cc0sj8MtqP6ee1Glk+1WT7VZdlUm+VTbZZPtVk+1Wb5VFc3lc004MHUR7A9ai9Nzxe65T8QVZH9XwDA6r6eqqfOsHyqzfKpLsum2iyfarN8qs3yqTbLp7q6rGz6XT4f1iZJkiRJUokMxCVJkiRJKpGBeOetBf53/qnqsXyqzfKpLsum2iyfarN8qs3yqTbLp7osmzqj9mFtkiRJkiRVkWfEJUmSJEkqkYG4JEmSJEklMhCXJEmSJKlEBuKSJEmSJJXIQLzDIuItEbEsItZExPURcWCn8zSaRMS5EZEautvrhk+MiMUR8WhEPB4Rl0TErIZpzImIyyPibxGxPCI+ERFjG9IsjIjfRsTaiPjviDi9pEUcUSLisIj4QUQ8mJfFSxuGR0R8KCIeiognI+LKiNi1Ic2MiPg/EbEqIh6LiAsiYmpDmn0i4pd5vbovIt7TJC8nR8TteZrfR8Rxw7PUI0cb5XNRk/r0k4Y0ls8wiIhzIuKGiFidt0OXRcS8hjSltWfuuzbWZvlc3aT+fLkhjeUzDCLizIi4OW+XVkXEkoh4Ud1w604HtVE+1p2KiIj35uv//Lp+1p+BSinZdagDTiF7fP8i4DnAV4C/Alt1Om+jpQPOBW4BZtd1W9YN/xJwL3AEMB9YAlxbN3wz4PfAz4D9gBcBfwY+WpdmLvAE8ClgD+CtwNPAMZ1e/qp1+fr7MHASkICXNgw/G3gMeAmwD/B94E/AxLo0PwZuBA4CDgX+CHynbngP8DDwbWBP4FTgb8Ab69IckpfRu/My+2dgHbBXp9dRxcvnonz919enzRvSWD7DUzY/AU7P19m+wOXAPcCUujSltGe47xpo+Vydr6v6+tNj+ZRSPicCxwG7ArsBH8nblD3z4dadapePdacCHfBc4G7gJuD8uv7Wn4Gu005noJs74HrgC3W/xwAPAO/tdN5GS0cWiN/YYlhv3tC/oq7f7mQByIL894uAZ4BZdWn+EVgJjM9/fxy4pWHa3wV+0unlr3JHQ6AHBPAQ8K6GMloDnJr/3iMf74C6NMcC64Ft8t9nAitq5ZP3Ow+4ve73xcAPG/JzHfDlTq+XqnSN5ZP3uwi4rI9xLJ/yymdmvq4Py3+X1p657ypePnm/q6k7eG0yjuVTbhmtAM6w7lSzq5VP/t260/nymArcCRxVXx7Wn8F1XpreIRExnuxfoytr/VJK6/PfB3cqX6PUrpFdavunyC6ZnZP3nw+MY+MyuJ3sX71aGRwM/D6l9Ejd9K4gO6u3Z12aK9nYFViORc0l+5e7vjxWkjW89eXxWErpN3XjXUkW6B1Ul+aalNK6ujRXAPMiYvO6NJbZwCzMLyu7IyK+FBFb1A2zfMrTm3+uyD9Lac/cd7WtsXxqXhURf4mIWyLiYxExuW6Y5VOCiNgsIk4FppCdubPuVEiT8qmx7nTWYuDylFLjOrT+DMLY/pNomGxJdqnGIw39HyH7J0lD43qyywXvALYG/hfwy4jYiyzoW5dSeqxhnEfyYeSfzcqINtL0RMSklNKTg12ILlFbn83WZf26Xl4/MKX0dESsaEhzd5Np1Ib9ldZlNhv15SfApWTrd2fgo8CPI+LglNIzWD6liIgxwPlkl/7dkvcupT0DNsd9V59alA/Ad8guV3+Q7NabjwPzgJflwy2fYRQRe5MFdhOBx4GTUkq3RsR+WHc6rlX55IOtOx2U/zGyP9ml6Y3c9wyCgbhGtZTSj+t+3hwR15M15q8EDJClAlJK3637+fuIuBm4C1gI/LwjmepOi4G9yO7BV/U0LZ+U0lfqfv4+Ih4Cfh4RO6eU7iozg13qDrL7U3uBVwDfiIjDO5sl1WlaPimlW607nRMR2wOfBY5OKa3pdH5GGy9N75y/kN8v0dB/FtmDjDQM8n/s7gR2IVvP4yNiekOy+jJ4mOZlRBtpVnk2vJDa+uyrTjwMbFU/MH/q5gyGpsysewWklP5E1pbtkveyfIZZRHwBOAF4QUrp/rpBZbVn7rv60Ef5NHN9/llffyyfYZJSWpdS+u+U0tKU0jlkD5x6O9adSuijfJqx7pRnPtl+/bcR8XREPA0cDrwt//4I1p8BMxDvkPz+yKXAkbV++eVsR7LxPTEaQpG9RmlnsoeCLQWeYuMymAfM4dkyWALsHRH1wcXRwCrg1ro0R7Kxo7Eci7qbrDGtL48esnuL68tjekTMrxvvCLK27Pq6NIdFxLi6NEcDd6SU/lqXxjIbpIjYDtiCrD6B5TNsIvMFsifaH5FSary8v5T2zH1Xc22UTzP75Z/19cfyKc8YYALWnaqqlU8z1p3y/BzYm2yd17rfAP+n7rv1Z6A6/bS4bu7IHsO/Bngd2dOG/5Xs/shZnc7baOmAT5L9c7cj2SuRfkb2yoSZ+fAvkV2q/gKyf/1+BfyqbvzaKxeuIHslzTFk98A2e+XCv5Ddp/JmfH1Zq/KYyrMNeQLOyr/PyYefndeBF5M1/JfR/PVlvwUOBJ5HdoVD/euxeskC+m+SPQTklLx8Gl+P9RTwzrzMzqXLX4/VX/nkwz4BLMjr05FkO8U7gQmWz7CXzRfJXu13OBu/wmdSXZpS2jPcdxUuH7I/gD+Ql8uOeRt3F/ALy6eU8vkYcFi+7vfOf68nu9zWulPh8rHuVK+j4Sn21p9BrMtOZ6DbO7L35N1D9l6864GDOp2n0dSRvfrgwXz93p//3rlu+ESy+/lW5A3ApcDshmnsAPyI7F3HfyYL7sc2pFkI/C6fz13A6Z1e9ip2+XpKTbqL8uEBfIgsUFtD9jTM3RqmMYPswS2ryV59cSEwtSHNPsAv82ncD5zdJC8nk92TtpbsXfPHdXr9dLrrq3yASflOdDlZULyM7B2esxqmYfkMT9k0K5dU39aU2Z7hvqtQ+QDbA78AHs23+z+SHXD2NEzH8hme8rkgb7PW5m3YleRBeD7culPR8rHuVK9j00Dc+jPALvKFkiRJkiRJJfAecUmSJEmSSmQgLkmSJElSiQzEJUmSJEkqkYG4JEmSJEklMhCXJEmSJKlEBuKSJEmSJJXIQFySJEmSpBIZiEuSJEmSVCIDcUmSJEmSSmQgLkmSJElSiQzEJUmSJEkqkYG4JEmSJEkl+v++etf8nB023wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "9 dx_to_sct :  float64\n",
            "nan %:  0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "10 dx_to_sct_group :  object\n",
            "nan %:  0.0\n",
            "['90 to 180' '<=90' '>180']\n",
            "Number of unique class is  3\n",
            ">180         8609\n",
            "90 to 180    4561\n",
            "<=90          838\n",
            "Name: dx_to_sct_group, dtype: int64\n",
            "\n",
            "\n",
            "11 sct_type :  object\n",
            "nan %:  0.0\n",
            "['Rel-PB' 'UR-BM' 'Rel-BM' 'UR-CB' 'UR-PB']\n",
            "Number of unique class is  5\n",
            "UR-BM     7238\n",
            "Rel-PB    4016\n",
            "Rel-BM    1822\n",
            "UR-CB      529\n",
            "UR-PB      403\n",
            "Name: sct_type, dtype: int64\n",
            "\n",
            "\n",
            "12 pt_age :  float64\n",
            "nan %:  0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AepzSAPeOqPwtX/xjm6n7RDB3fb0OANCuWV2Mu7CHs8QqxGIl+lWDyqHRL+qb5X3IxHmOfgszUjLyK4ZwPTDK3LW9KuUYCkvKULtGNUeffSSnsGJm+3tHdHa8PzXlZbNkdwbO6Xqyq/sHgDu/XIOd6XmYs/2Iyd/K2bX8mvecunFgO3Rr4elDWrw7Az+uPQCshaV7/pipa/zS7AvxblIvCROuOk3zPc9558y5qPcpGN69ud9rP65NxW8b0/DbxjT8o18b81/KwEc65YCK4zCoHbomeo7DrVM8w5xemrkdd5zT0W8/B7NOYPISz3l+34jOqFHNWp+gW1F6vvy3Z8uGuGd4Z1f2GUmzthzCz+sP4uf1/o35n9zcz/B9Gw9k4+uVnlWA7jq3U8jSF22czM49BsAsKaXTQTwTADRSPFo73F/cO2yiVQgw1xPtk2cwAZWPk9Bl9fg3M5OxHTS57NEJ776OGbS2mnUoy17PkpbiMndbJrNPOOsJKihynh4nxQWrv49y+1iIfk3Lrjxf9XrOjW7cmSaPj5sh+m5cM1WZ7Z7oEJzPx/L98wez9wmzMvPcPVdicUz0kZyi4BvZ4OawoEJFxFK4hxsVK2JNQ9FIosxXjSbNdCL1mNVoAXe+p7JMlFfo7nczc95qlQGzCuyVOYwi1jLzjNNSVKIfr6zca1GpcVxzuIcfup0/RopWOdNMT3RVjWqzVYkWQrQDMArAZMXThwHUFEI0Vm2e6H1Nk5SySEqZ43sAiM7pjk0oLSvHx4uSsflANtbtP47Ji/cEDTk6mluESfN344jJAk9WQXHFGrYAsDXN2gQ1eoW3jQeyMHnxHpSVSxzJKcSk+bv9wlN99mcWYNL83cgxEdr355bD+HBhMs55db7uNrvSc/HBguSgleqSsnJ8tDDZ1PfdfCAbnyza41ohbfqGg/hrq/YpvOmAJz0r9mTiy+UplkOIVuzJxJcr9pna1um4tUkL9M+z1GOe31WZgWptqx739/vGNFz1/tKKNbgB4JuV+7FkV0bAuW214jBt3QHMS0rHwawTeH9+5SRa367ab3qW1NzCEkyav9v00AAzVqccw5SleyGlxI9rUivCZc389mbPjw8WJGNbWg4mzd8dULhZnpxZ8f9J83cj22ZBJ5hpaw9g7vbgocAnisvwwYJkfL1yH75YnoLD2Z78I1hhyUdKiS+Wp2DlnsrvNXPzIfyxyVr77IxNhzBz8yHN147nF2PS/N0B4bQ/rE7FAq1wZ9XJuulAFl6fvQPvzt2FA8cL/F77ed0BUzPjKis0HyxIRobJ4xONtqZl46OFybqz0JshJfDl8hS/8znarDdxHy8vl5i8eA82pGqvGav3Xs/1FXjebDmYXZF3+gqvOYUlmDBzO574aRNSjxVgy8FsXPLuYszSON+V9wkpPfnFl8tTDL6lsZ/WHvBbPcBsnlouUbEKQSg4vbvvPpKHDxYkhzwMd8vBbHy8KDnojPzS7//+385KucuIuiyjvMfbadj7ae2BgOeyT5Tgw4XOQ3q/X73f8T58lu3OwFcmy1lGvl3lKdtoCVVkTUFxKd5fsBvJR/MABJ4byjxqx+FcfLgwGdknSvDBgmTsSrdendLKrvS+mtnyezyzG859G4AjAGYonlsLoATASADTAEAI0Q1AWwDLHaQxZny7aj9enuk/pf/JDWoZhmb+58s1WLc/C7O2HMIf950d9DMe/8l/NuiL31liKTxMryI2ZWkKAKBh7RqYujwFW9NyMGd7On65+yz/z3t3MXILS7ErPRdvXX+G7ueUlpXj/75aGzQ95725CACQV1SCx0Z3191u6rIUTJiVBMxC0O976XtLAAB1a1XDjQPbBU2DkSO5hRXhOnsnXKQ5eUzy0Txc/7Fn3ryOJ9e3tH9fqHW3xAYY0KGJ4bZOJ/Vavz+rIjxK7ZJ3lyD7RAl2pufibe/v+h+N30/59fOLSitCry+ftBQpEy/GhtQsPPnLZgCe8PO1+457KjcPnG05hLWguAz//nwNWjWu4zcj9/veMTcpEy8OOpnPC39sww9rDuDdebuQ9MKFlj5fz7UferKzsnJZ8fulTLzY5PqV5j7jlT+T8MqfnrwkLesEXrqyd8VrSYpl1V6bvQNbDmbjg3/1NZt8U00xqccKKoYRBPP23F1+hSbfEJFlyRmmhkYs2pVR8Z6UiRejoLgUd3tDiId1PRkNatcIuo+8olLc843nPdueH426Nf1vbQ//sAHzdxzFd6v3Y/HjIwB4Zvr1za5vlKes238cV72/rOJv5RCHtKwTePiHjUH3AfgXTmZsPoSjuUX44f90pwuJOspz9+J3PHls9WoJGDPU+hATAFi6O8OTpyP4sYuUK72/u+8+rpXd/LYxrSIf0KIOiwT8ry/1d7/k3SUVeecfmw5h1gNn49npWyv2MzcpHRneXq+7vl4XcF9SplEC+OcnnntTp+b1MaRTs+BfWmFvRj4eVaXTl6e+M3cXdryon6f+tsH4uNjhZgTHqDcWAgCOFxTjyYvcG6agdsm7nmulVvVquGVIe1v7MFvu0iOEp8KsLsvcolMeMEN5biipxzVbJSGRdDgHT0zbbLxdkHup8nXf8JtuLRqgf/smpt6vtjE1C+N+9qTpyjMCy/Shivp49c8d+HxZCl79c4dmPunLo5rVr4UHv/f8vhO9+eorfyZZzlu1voder76v/J5bWIKhXazlLfHCck+0ECIBnkr0VCllRReJlDIbwKcA3hBCDBdC9AUwBcDyqjIz97ZDga0+u4/kGb5n3X5P6/WWg+YmxVq6W7sVTItWBSNYRWxnei62pnnSsn6/f8u6BJDrDTNaqbOEj0+ZxQxlY6pxD7MvTVZsP2R/ojEfM718yh77/ccKDLb00Do06t4tLW6s8X1Cp8XQ1wO9QtEbqP79Af9GGK3WR2UI/tp9nolZtnl/B60CkJmQKydLWvnO00KDEDFj+sdcfQ0oK0l6hT07IWZrgkwit3yP+z15VsK8N6Rqp2/pbnPp2pfp36OlDOcz+7spe5O0wgF90TvKUE0zIWqA8bCSXAshperCyaoU4zw0Fmx1sFRXSqZ70SGhtitd/z6+64hxb88OjQkvg11fvrzTdw9T5ssZQcJGlVmP8pxLNXFvUtOKRvIt3xcsnHanjV6wYPy/mzv7XB+mCcSclEfMlruMZGmE6SrTZDXSTStSEXCWRp9DOsNknDaimB0iqCXYpIChWnFhzT5zxzNYPmSWnQn79KJwqgI74dyj4Old/kzjtYcA/AFPT/QieMK4r7KduhixJyPfVNjE4exCv7DXUIcRaRUShRDILijByj2Zmi1ORpVVvZtWYUkZlu3OQHGQm6rSrvRc7Dlq3MBghtE+zOQFpWXlWKZqmNiWlqNZ4ND7/srnD9ms8O3LLMCOw8bnkJ08evJia7N7BqOsx1u9aexVhP/N2nzI8lAEtWW7M7AsufK30wp1U/426d5hCsHG7pgtbCpvyJ6ZzxWzq0qJZckZFeP2cgpLsDw5E+U26/Jb07KReqxAc4iC7ztmF5Tgzy2HsWrvsYBr+3h+MVanBD5frAoxzMgrqijA60k9VoDpGw5WhHNWM2jcWZacgbJyqfv5yvT77FMcf9932ZCaVVGgLy+XWLEn09QcATvTcz3DVLSOh6KwdzS38nvvSs/1C1XddMCdAoLZMe9O5FacZ4HHOa+oFMuTMw2Huew5modd6bnYcTgX+zLz/SpCy5MzAhrOyqTErvRc7M3IR4k3LzV7X1NfC+v2H9ctmEspsTrlmGbl0/ea0QzEWw5m4/eNaQH735CaZaoxZWlyht/qCMG8PnsH9mcWeNMX+Lo668xxMAb2gKpioDzX1R/t+40KS8oqwkONmC1Obz+Ugz+3HPZLywpV494yg2PoyXeCVxaUx239/uNIyzpR8X3MOJZfjDWqfCjpUC72ZxaguNRzbNR5olnKPG5/ZoHuaiFr9x3D1rRsv7Ig4GlI990T3e/U9D/hpKxsDKnYQquR20ZCjO4HavszCwIaF/Q+Ut3Y6iSgP7+oNOC6ATznqJn5gczYm5GPTQeysGx3hm4ov++c0zp/84pK8dacnTieby6E30wjiC9f91Gecz5WwrkrPlt4hlFWRZbDuaWUf0Gne0ZKWQjgHu+jynhn7i68M3cXLjOYcRgABk2YCwCY/eA56NaigalwZyc+1lgWQAhg9FuLcDinEG9f3yfgdaNeLb1etEd+2IgZmw/hlsHt8NzlvUyl7dnfgy8BFSyLLCkrx4j/LdR93UyL2ptzdmLS/MpQ1MM5hbjoncUAzIcYKsduvjNvt8GW+t6euwtvz92FDc+ch8Z1a2puY2dG370G49aC9YrWrJ4Q0DASdG1GnX0WlpRhwY7KZeZ9M/7eMth+uL0vRMvnmg+W4a+HhuluP/Blz/UXLLT7bMUYfqPvu01x85+XdMTvRvPhwj1YtPMoBrRvgh/+bzCufn8ZdgWJStGTln2iInx23IX6Qx4uemdxRa/95Jv7+c22f85r85FbWIrJN/dD3ZqVM+Z+s3I/XlaEig94aQ7KpfHnKI9PysSLDc/LGz5ZiUfP74qPF+1Bjvfzg60CcMWkpRX/v+ebdfjPOR0rZvxNmXgxpq07gMd+2oR2Teti4WPDDfd1vjfcTG34/xb4TZrT/yXPyoxTbu2P2z73D3H8xKWGqMHe/D+Urv1wOZIO5+LZS3sGvPbPj1dg88FsPH1JT80Q7GD5aVp2Ie79Zh0m39K/4rmcEyUVIX13ntMRHy/agxHdm+OzW/vr7aaCcgzh6pRjFcMktPLdeUlHMGbqGtSvVR1bnhvt99qc7Udwxxdr0LB2dWx6dnTAew8cL6gIqVXuf8vB7IpzLVhev35/Fv77yxbTfXXvzd+N9+bv9gzz0HhdXeC93zssRo9RTn32q/P90q9cXkqZJ0kJvPpnEj5ZvBfndD0Zi3YeVe4GSYdz0aeN/5Q2ZupQGXlFuPDtxQHPJ6kahSfNT8bxghK//MZn0MtzUVou8c0dA02HnE9esrdidubL+7SsGIZkZOgr81BQXIYpt1Wen7lFpTjntfm4vn8bfLfa+lJOvmM07LX5yCksxcc39cWdX3rKdmufGuW37ZJdGfjXp5X3rVkPVA7hu/cbzzmwbOwIy2mwataWwxXDX4wof//j+cU4qZ522USpuoVK9Dmvee4la1THScuw1xZgzsPaq7VsPpiNM9v6r/ZidOreOmWV5vM3fLISfdo0xq/3nBXwmpUqe1m5xPDXF1T8fd+Iznjk/G4B2z0zfQu+W52KK89ohTev8y+P9xo/28InBnc8vxij3/Lk1b78YugrnuO/bOwItGzsWbnFztju3UfyTEeexRsns3OTSmZ+YCu6VnbiW39woeomFg5CVM7aOltnsiyrZngnN5m63PnEDVbohSb7mJlY7EtVmgtUvShmspPZW81NcmVGmsHs4C5Ec1uirGz5+I29s7CvXJ2eluSj7oV07tQIudSq1NsP7dY3L+mIXwuur4DqC9k1U4HWa/VXHjut9Tp971OGvc9N8p8sy7cP9fNqvu+wxMKwkWCNO9+tTq3oaQv2+Vo+UjUG/rHJk9/sy7QenuqjN+vsryFcozvUk1ELUVlx+WVDWkBFbbM39HqaxmRAgLkVE+aoJpo7oujZ/cxboZln8jdWHo5gw5R8n6vVS+SbnEuvN1cvrHiNxXB6rUmUopHyN1Lnf1OXee536go0AKwLEoGix8p1+M1K7cmiSr0Xx2KdSZsqaec10zeYm4TQd39foHGO2qlAK/nOP+X5r+ztFAJYuNP/c7XWorbb2GpEnUXbKfuZHVZlZ9hZQPSXbl6pvW+z17LvelhtMETKjdBk9YSLX+uc975z7heNeRN06RybYB0cRr+f8pzTKoYEq1enh2jVgljASnSIaY0Nziss1ewlTc8phJTSr6Cg/ltPcWk5MvOKgobfKHsW8ywueaQslOlVUPOLPN8t3+ZySsrvWlYudUMDzRwTtwutecWltiePyCsqRUFxadAZOvMNQo19lZVg+zDL6Ktk5hUFXXdRnWmXlpVbrqC6vQxFYUmZ3w2stCw8y1wUlZajWCdUMdTDNoDACrh+hSgwP9FKn5XTPFj4nnIpmpzCkpAvu1NFDAAAACAASURBVONkllQ31m+2Eg6oDk82u9RgXlEpikrL/MJjlfvKLyrVPQ7qxkffZ9o5ak5WQDD6nfKK/PNavZ/F6Hsa7Vstt7Ak4J6cVRAYHq7On8uC3OvKy2XQcO5gYeJSSttLHlk5NL5NffeqsnKpmT+rh/FYXWrKqEzjy4t8r6u3M7o8y8olCopLcSSnUHc4g0+Ji4UDozSphxiVmLgfCehfi77vaMR33Vgd22o256vIL3ROLq2eaOX1rHW8qidUljWMU639qtb14Uue+v5mJX8uLi0P2czTTtJlRWlZecBnSSl18263ZhlX35/iFSvRLtIKZ1CGCvu8NHM7rvlwWcDzA1+eiw7jZqLX+NkVS6Y88sNG9Bo/u2Jcnt7p3fWpWej74hzc9Kl2mIqPssVIqzXarCM6Y9dOHT8bpz/3F8584W/L+/xtY5pfCMuJkjL0eObPgMzlh9Wp6DV+dkXPhx4zmYGV7OK0Z/+yFIK/zLt0y6YDWeg1fjZ6PjMbF2iEvSld++FyvKGY/VfJV3jxhZv76PUsBfPc71sDnpPSE1rZ98U5muMTjQoMo95YqDlbpxG3Q4C6P/2nXxiV3gQlbvtp7QHdXvUez/wZ8s+/VxUO+sv6g5pjhr9dlVoRauijdY1ZadwIVvE8ruj1nbHpEF5VhJu6RZmE279YY3s/TivR0zccRK/xs/H+AnPDOs584e+KsaDzdxxBr/Gz8fJM4xmNx07bjF7jZ6PbU3+i21OV55ayN2P3kTzdCX72ZuRXXNvK9EobbXPqkF0rfl5XmV7lettb07LRa/xs3O+dSRjQLuAfySnEqeNn44c11vK/XuNnB4yv7P3sX7ju48pFRH5ck4o+zwfew9T5VacnZ+LzZSm6n3Xj5JVBryXlb6jl/u82WJrAzi4pJV6fvaPiXnXl+0t1t/P5bMle3PyZcZlD7dYpqzV/AwD4fFkKPluyF73Gz8bQV+ah1/jZ+Hxp5X3e6Oq8+oNl6PnMbAx4eS46PjnT77WvVuzzK1vo9YirbUvLsTzxmLJHW1keKyopNzxXzDiSW4Sez8zWbODx8c3ArTyfBYI3qMzcEtg7rVyJQErPXCa9xs/GmzplFAColuBfrVienIle42dXrMCg/R7r+a4vIgnwDKObrhFFlF9U6nf/fWb6Vkth0me9Mg+9n52NotIyS5OSqY+1usEhp7AkoFzQa/xsLN5lv0z+rs5QwhH/W4gez/zpF2V52+ercbGiHKk8+vkuNPqXlUv0e3EOznj+b9c6faIVK9ERsk5j5mOl5//wjBn2LWuhDmnUYyUMM1Ts3PCFAJ5QLd/lo56Aw7cszVtzdhnu0411otWZoZXQ7Ve8ywwol/8JNls74Bljr8WX0anDls0uRaT27Srt8DX1MmpKCX7h3P43vhQH4bVu0powxK4wR9DbNmNT4Hqxqy3Mkqq+xsySUiJIwEKADxb4Nyy63TOtNau8WU6HTDz2o+faefVP8w0FE7zLIr7ozfO15rJQ+l4jpN+qeUmefMzX6PXqnzscR4WYebfeTz1Dsd6xbzLE3zdWhuhqNW7orQluhtawCGWI52MGeaAVenOMWGmrUR4Hq/zGRJvY/r35lQXxTQeyg77JV06xwjeMTes3UO7Tl4+bmT8FMA7DfepXe8sufbZ0r2ZHiB07TM5WLkTwPNGo8fk37/miPJ/Ve9Pau5nz7L/e4/i2ThkFCMxDX//Lkxd+abBOs/lKdOV26vKf+pqVkAHnhN6wMj1Hc4tQUiaxL7MA09a5N5xj1R7te7NvWUs3+VaM2axYSWHBjqO6DaDbHE74Cngie3ILS1FQXGZppY9YxEp0rAhPVGpUslvG1ltmK0QrEYRcuMdEa4mCJJBKKLIGs8udSGmvF8GJUF6/boRzxxJlaG6ox2wbUZ5DWhWIUPws4fq6muHcLuekZhuijBpK3ArjDKVQLSPkhNuHze1zwy1uD71SUjbE2m5UDeHpGwOXhms0x0Q72Ud0ns6uYSU6RszYfAhzt6cHTHyl5WhukV+ohhPKZTC2qZYi0As7tmPxrgzXM+m9OuG1Ztea/HBhMi6ftER/A6+MPJ1lWeDpDZ+52T9MSh1qpvleKXH71NUYO62yddWNdaLVFu08itGqGYyNZvT2tWo+8N163Ptt8Nk9fSbOCt7C2nHcDNP7C+bWKavwmIUe+k+X7MWl7y7BBW/5H4tD2YXo9+IctB87A+3HzsBKF9dkVn+WmYn5tELG9VrXb/9iTdDxcz5S+v/uViJa7BRs9ZazUZ+Laot3HfWb5d1tCQ7uiO3HzrC1PM7RPE9+rfxty8ol/jV5JZ77fSvG/bwZ7cfOwM2frbK1hqcWKYHbp67xmyPDzJJhgOd7alFG/uQUluCSdxej/dgZmsNG1KopziGtY6he+/iTRXs0eyhv+nQl/t6Wjs5PzkT7sTMs91YnH82zNRTJipmbDwUMyXHqvDcXYcGOwMmylMvXBLtK/9CIZtGcWdzFiuwPq1M1Z/ZWG/3mooAls4L59+erMVrnuJj109oDmLPdOPps5d5jQfMtPVohzmYO7z3frAuYvErpnx+v8Pt795E8jJlaueqA3kc89P2GinvdvarZu9WTsGpFzIx+c5HhMqmj31ykOXmccvb8mz5dVTHRnBWhrOSr17TXOvafL0vR/U2UKZNS6g452nJQe3lVn0Ev21/h4QWDqBHfGtjLdmdoTv7pa9j4aGEyLn9vCXIKSwyHU/6tuGYGvDTX1XJTtGElOoaMmWpurN///tphmJFZ8cgPlRUR9fqxemHH0aJhHe0V3JSFAKPJHCbOSnI0k7OUnvFAduxMz8Oc7Uf8xleFokHv5s9WmQ4zAzyzTGYXlGD6hjQTM6lWMhMK5WZP2IIdR/GjhbHiL/yxDZsPZgeEOG0+mO3XSHKdqnDihJPxpGZ9pxOyr0VvOIURCXu9t9crjqPyZw92Lgab88GpSPR07cssCMivlydnYsnuDExZmoJvV3nGby7aeRTrU+3NoKy2+0heQOUgWBi5FVOWpGDLQc93mrI0Jej2ygZCdaMj4H+O7M3Ix0s648YX78rAHV+sqSiE3/21+YY+AHj4+w0hDz+0miYzdh/Jw61TVgc8b7bMAHjDt1VC3QP3+LRNAesEa9mRnovrP15h6R44L+kIdqTnah4XN702e4ele6iS1pAzs9/RqDFRPYxgb0a+qWFOynkV1I0qP6494NdLrFXZ0joOyvfsSM/VnM1dWc7KKyrVbTAxyp5DGUnzxDT/e+Pc7doNM76VAowq9MFWJBn/m36j4+GcwpA0FTznbZB88pfNhttNmJWEjQey8fnSFMOhHOohgW6Wm6INK9FRyklZLtjST1ZYnXmTKklIlJTbq4RrtWhGSyhbLIT9kYeVnlGjmeGN2DkrzcxSG+o0aImGIRMAdPMNt+Zo0erpMRu1YIbVWVmrBcnblK+6MdeFnlDNkBspytmh7Ry1UPbukQ6TeZCTCZui/Ve1c42HctUHdTlYr8dZ776mTFppkDKh2dnX3eSrM5id16YqzLptlnZXHQVl5SQa9/MmXN+/raX9W1l7UR0qoRdebEewNQtfdXEiBL1e3982HjS9jp5yIokVe47h4ncWY2jnZn4VBKflZLOzYTvJ68b+HNgr+PpfO/DPAfrnkZQSV0xa6midSTNJVk6U5rZwzeT4wHfrsS0tB7uO5KFtk7ph+cxImDgrCV8ZTOjiszcz31b0ipMb+iXvLsZHN/Wz/X7AU0k47dm/LL3HaAKiUI2J/mV9ZNcY7mBiqIQytNspdfhjMHrj6sd8vjqgx8tq+Vqv10hLKIrhTlbBsOpFVe+Q+vK02nilPNZrUo7hxRnbDYf7WGFnwq4oaUe2xWz+uiE1C+/pzLSsdNfX6zCwQxNbabE7WZ1ypQWzgk2ia4X6/FYql8C/Jq+s+PvndQdRu0Y1Vz5Xfd3ohZvf9+16vDRje0BjbPaJEoz7eTOW7s6oGBKnZ03KcZz2rLkZxJeGcSJhZZi5nWv335+vxpBOTXH72R3dTFbEsRJtk3p8iJFvV6XqzoRsJMXkzUodKuH2skFG3l8QugqVj5UlTK6Y5L8sx9a0nICbl9Mbsd3ZsK3whUIqZRWU4Onp+rOMbkjNwkaNkDy3mZ0p3g7lsjehNH1DZSEi2E0t1pkJ43va5Oy1ZsfNmrHlYA7u/GINrunb2vY+LrAxFlGdRygZjTN04qHvreUZbtcV1JUprXF3szSWuLFLKyTbiF6erBUyajUS5mud5YzCFVCzx6VKpxmTgyz7aNViRQPANR8uN9gyPKJ10i03WZnZX28Zu3g1P8h8GOq5PMwuZabFbgPx4RztZTV9w3KCKS2XyDE5i/iNikYDJ4KViaWE35h6O+YlHcG8pCO4fkBb1K8VP1VPhnPblJYV+vVn4y20rKoJRRltd7p+L3OWixWcSDmSG551nckedW+lhLNGKTPjIY2kubwOeLSPVHCrJ85u6H6oWPleHE5ij53D5majmRtiuSeaKJapl1a1K97WjWYlOor9ZGFiJDLP7fGYeopLy11vN1+VEt8tz6GceZmc2ZuRHzBG8vvVqX69+laVy8pJTdw2dVmK5fcoJ/KLpCM52kNy7vtmfZhT4o7vVu33G5+rVmYhT5691Xi2ZLNCObY6Gm2xsf5rdauLwIfY5zau6Vi3br87kwmSedd+uCxqj/uSEESaBiun2p04T0u8ZbvRlUOSn6p4w6Cqbc2+6LxxETD89QUBzz1lMgw8EoxmOY12j0/TnildL1TQqnCHxY79eTMeNghptxJR4NaqEJ8tdTf0OdrZCW2tUS26un4/dTlcPRZc9f6ySCehylmdchxXfxD54Qta3JzzyEcIgW0uregTTCgngIsEVqJtYlgREVU58XX/q7Iicf8Ktt5uuB1yeShAPArVRHtEFD0E4NqEgcGwJ5oAVC5OTmRk+6HQrwUMeEMTXcicjua638pJFG67j4TnuotVpWEa0qI228XJy0JhT5A1XKsaM5MSErktGnsrozBJrtGbbTwU4q2MyUq0TX9vi65WdYpOr7i4BJiRTk/ODMvnUNUWK+WIUW9Yn7W7KonU3Ap6YeoUnaIteoAoUtwcFxyNJs0PvqyaGy56Z3FcTS7GSrRNjHIiIiIiIqJYts3hShlW5BeXhe2zQo2VaJuqwnqFRERK8RzSRlUHz2Oi6BVv42ZJJY5+X1ai7WIdmqLM1OUpkU4CxbmDWQWRTgKRY+8vSI50EohIx5cr9kU6CRRC6qUyYxkr0TaxDk3RhmssU6i9Ny8846aIiIgo/sRTJBAr0TZxTDQRVTUMsyMiIiK74qkYYbkSLYRoJYT4SgiRKYQ4IYTYLITop3hdCCGeF0Ic8r4+RwjRxd1kExFRuC3cyWgHIiIisicalzCzy1IlWghxEoClAEoAXAigJ4BHABxXbPY4gPsB/B+AgQDyAcwWQtR2I8HRorAkfqZoJyIiIiIiCqX4qUID1S1u/wSAVCnlbYrn9vr+I4QQAB4E8KKUcrr3uZsBpAO4AsB3zpJLREREREREFDlWw7kvA7BGCPGjEOKIEGK9EOIOxesdALQAMMf3hJQyG8BKAIO1diiEqCWEaOh7AGhgMU1EREREREQUxeIomttyJbojgLsA7AIwGsAHAN4RQtzifb2F99901fvSFa+pjQOQrXgcsJgmIiIiIiIiimJVeYmrBADrpJRPSinXSyk/BvAJPOOf7ZoAoJHi0drBvoiIiIiIiCjaxE8d2nIl+hCAbarntgNo6/3/Ye+/iaptEhWv+ZFSFkkpc3wPALkW00RERERERERRLI7q0JYr0UsBdFM91xXAPu//98JTWR7pe9E7znkggOU200hEREREREQxLJ7GRFudnftNAMuEEE8C+AHAAAB3eh+QUkohxFsAnhJC7IKnUv0CgDQAv7qWaiIiIiIiIooZ8TQm2lIlWkq5WghxJTzjmJ+Bp5L8oJTya8VmrwKoB+BjAI0BLAFwgZSy0J0kExERERERUSypyj3RkFL+AeAPg9clPBXsZxyki4iIiIiIiOJEHNWhLY+JJiIiIiIiIqqyWIkmIiIiIiKikBKRToCLWIkmIiIiIiIiMomVaCIiIiIiIiKTWIkmIiIiIiIiMomVaCIiIiIiIiKTWIkmIiIiIiIiMomVaCIiIiIiIiKTWIkmIiIiIiIiMomVaCIiIiIiIgopEUcLRbMSTURERERERGQSK9FEREREREREJrESTURERERERCElZaRT4B5WoomIiIiIiIhMYiWaiIiIiIiIyCRWoomIiIiIiIhMYiWaiIiIiIiIyCRWoomIiIiIiCikuE40ERERERERURXESjQRERERERGRSaxEExEREREREZlkqRIthHhWCCFVjyTF67WFEJOEEJlCiDwhxDQhRKL7ySYiIiIiIiIKPzs90VsBnKJ4DFW89iaASwFcC2AYgJYAfnaYRiIiIiIiIqKoUN3Ge0qllIfVTwohGgEYA+AGKeU873O3AdguhBgkpVzhLKlEREREREREkWWnJ7qLECJNCLFHCPG1EKKt9/m+AGoAmOPbUEqZBGA/gMHOk0pEREREREQUWVZ7olcCuBXADnhCuccDWCyE6AWgBYBiKWWW6j3p3tc0CSFqAaileKqBxTQRERERERERhYWlSrSUcpbiz01CiJUA9gH4B4ATNtMwDp7KOBEREREREVFUc7TElbfXeSeAzgAOA6gphGis2izR+5qeCQAaKR6tnaSJiIiIiIiIKFQcVaKFEPUBdAJwCMBaACUARipe7wagLYDlevuQUhZJKXN8DwC5TtJEREREREREFCqWwrmFEK8D+B2eEO6WAJ4DUAbgWyllthDiUwBvCCGOAcgB8C6A5ZyZm4iIiIiIiOKB1YnFWgP4FkBTAEcBLAEwSEp51Pv6QwDKAUyDZ7Kw2QDudiepRERERERERJFldWKx64O8XgjgHu+DiIiIiIiIKK44GhNdlf1+79BIJ4GIiIiIiIjCjJVom3q1ahjpJBAREREREcUEARHpJLiGlWgiIiIiIiIik1iJJiIiIiIiIjKJlWgiIiIiIiIik1iJtkmI+InpJyIiIiIiCiUJGekkuIaVaCIiIiIiIiKTWIkmIiIiIiIiMomVaCIiIiIiIiKTWIkmIiIiIiKikOI60URERERERERVECvRRERERERERCaxEk1ERERERERkEivRRERERERERCaxEk1ERERERERkEivRRERERERERCaxEk1ERERERERkEivRREREREREFFISMtJJcA0r0UREREREREQmsRJNREREREREZBIr0UREREREREQmOapECyHGCiGkEOItxXO1hRCThBCZQog8IcQ0IUSi86QSERERERERRZbtSrQQoj+A/wDYpHrpTQCXArgWwDAALQH8bPdziIiIiIiIiKKFrUq0EKI+gK8B3AHguOL5RgDGAHhYSjlPSrkWwG0AhgghBrmQXiIiIiIiIqKIsdsTPQnADCnlHNXzfQHUAFDxvJQyCcB+AINtfhYRERERERFRVKhu9Q1CiOsBnAmgv8bLLQAUSymzVM+ne1/T2l8tALUUTzWwmiYiIiIiIiKicLDUEy2EaAPgbQA3SikLXUrDOADZiscBl/ZLRERERERE5Cqr4dx9ATQHsE4IUSqEKIVn8rD7vf9PB1BTCNFY9b5EAId19jkBQCPFo7XFNBERERERERGFhdVw7rkAequemwIgCcArAFIBlAAYCWAaAAghugFoC2C51g6llEUAinx/CyEsJomIiIiIiIgoPCxVoqWUuQC2KJ8TQuQDyJRSbvH+/SmAN4QQxwDkAHgXwHIp5Qp3kkxEREREREQUGZYnFjPhIQDl8PRE1wIwG8DdIfgcIiIiIiIiigFSRjoF7nFciZZSnqv6uxDAPd4HERERERERUdywu040ERERERERUZXDSjQRERERERGRSaxEExEREREREZnESjQRERERERGRSaxEExEREREREZnESjQRERERERGRSaxEExEREREREZnESjQRERERERGRSaxEExEREREREZnESjQRERERERGRSaxEExERERERUUglCBHpJLiGlWgiIiIiIiIKqRaNakc6Ca5hJZqIiIiIiIjIJFaiiYiIiIiIiExiJZqIiIiIiIjIJFaiiYiIiIiIiExiJZqIiIiIiIjIJFaiiYiIiIiIiExiJZqIiIiIiIjIJFaiiYiIiIiIiExiJZqIiIiIiIjIJFaiiYiIiIiIiEyyVIkWQtwlhNgkhMjxPpYLIS5UvF5bCDFJCJEphMgTQkwTQiS6n2wiIiIiIiKi8LPaE30AwFgAfQH0AzAPwHQhxKne198EcCmAawEMA9ASwM/uJJWIiIiIiIgosqpb2VhK+bvqqf8KIe4CMEgIcQDAGAA3SCnnAYAQ4jYA24UQg6SUK1xJMREREREREVGE2B4TLYSoJoS4HkA9AMvh6Z2uAWCObxspZRKA/QAGG+ynlhCioe8BoIHdNBERERERERGFkuVKtBCitxAiD0ARgA8BXCml3AagBYBiKWWW6i3p3tf0jAOQrXgcsJomIiIiIiIionCw0xO9A0AfAAMBfABgqhCip4M0TADQSPFo7WBfRERERERERCFjaUw0AEgpiwHs9v65VgjRH8ADAL4HUFMI0VjVG50I4LDB/org6dUGAAghrCaJiIiIiIiIKCzcWCc6AUAtAGsBlAAY6XtBCNENQFt4xkwTERERERERxTSr60RPEEKcI4Ro7x0bPQHAuQC+llJmA/gUwBtCiOFCiL4ApgBYzpm5iShSXrm6d6STQERERERxxGo4d3MAXwA4BZ5JwDYBGC2l/Nv7+kMAygFMg6d3ejaAu91JKhGRdQkcIkJERERELrK6TvSYIK8XArjH+yAiIiIiIiKKK26MiSYiIiIiIiKqEliJJqK4xhn/g0vgISIiogioUY03IIpNrEQTEVVxbGggIiIiMo+VaAc+/FffSCeBiMgxVqGJiIiIzGMl2oEhnZtGOglEFAQriERERNFJykingMgeVqKJiIiIiCjs6tWyutouUXRgJZqIqqRWjesE3eamQe1w65D2oU9MhHEtbSIKtwasPBGAy05vGekkENnCSjQRxTW9+qGZOQ1euKIXnr3sVJdTFIVYhyaiMJtyW/9IJ4GiQHWD2bnP7XZyGFNCZA0r0Q5wHAcREREREVHVwkq0E6xEE0W1ZvVr4vxTW0Q6GVGPHdFERETG3r/xzEgngaIIK9FEYXRKo9qRTkKV0enkelj55CjU57i7oDgmOn6M6tE80kkAAHRNrB/pJFAU6dfupEgngcixi3qfEukkUBRhJZqI4pIQAtUS9CuHrDdW4rGIH9HSIBIt6aDooHU+8BQholjGSjRVeef3TAzbZ9WtWS1snxVuNavFVnbCOQ0oHkXLaV0VZrUnp1iLJuC8HvplMJ4hFM1iq9QbZWSUFFca1A5/uOotg9uF/TND5fQ2jcNWAawRYxVNK9irELuqWq/hjPuHYtINHNsWStf1b2Nqu9/vHRrilFBUqFpZDFlwepvGkU4CkS3xW6KvQnq0aBj2z2xWv1bYPzNUhAA6NKsX6WQQORZP12UondqyEXq1Cn++GQ7REmEhTDbM9G7dKMQpoagQJeclRZeGEegEInILK9EOREthhS28gMHQ15jRJ8ZbYzlpWjSwlynFweVDXq1PqhPpJBARuWJgx6aRTgK5KN4a+lmJJluiLfrzP8M6RToJjn12a3/N55s3qGVqWYVIj0FsfVLdiH6+WrBTNNrO4UiqVq3qHYxoaQS9qLd7S7AtePRctG8a/usw0nkPxbcz2jbGuAu7u7KvszqzUhZLxgztEOkkkIv+uC++hu+wEu1AlJTB2IsEZ2ONBURUVKia1Kup+bwQ5pZVuGFgW7eTRDHH3olcPR5COUKgbxiW5bm+v3vXbfsIDUv5Rz9z45+J7BjerblrDeU3DGiHBlz2MGbE8zwyVVGLOItY5NnpgIyWrgxy3JAQrp+y08nW104VMdJMEi0T7fl0PJnj3M2qahOLmRVvN3wiArq2aBDpJJBXdJUaiKxhJToOsPxrfAze+ecZ4UtIEM9ffiqus9hrY/b3ZZtOpSb1auKlK3sbbhPJ42V2NvinLu4R4pR4GK2nHa/M/Pw3D7K+CkGLhpGpeD9yXlfH+7hhYFtb80vwHkSx5L0bzsA/+rWOdDKIYt79IzpHOgkRxUq0A7FUZzmzbWxPWuVExyAhjuEsADatXwuvXHOapfeYTV6ke4KjqRI/8areUT2BxXOXn2pqu5MbhOc7RLISfdnpLSP22cHUrG79Ftm8YWTOu7besdBmZ8XWcsOAtnjk/G6W3xeqPLQ7ewwpBE5pVAevXnN6pJNBiM/hiFWpvK03DFFLNJfJ7LJUQhBCjBNCrBZC5AohjgghfhVCdFNtU1sIMUkIkSmEyBNCTBNC6K+kTo6ZCfe1UxisSurWqhbpJOgyWyiOdCW2bs3oPYZaItl7FunfSq12jdj67cLFToU02n5bK6TkMCUKnTqqfMbsXAxuZtVVMeommsVjbhOP38kd8XdkrNashgGYBGAQgPMA1ADwlxBC2dX3JoBLAVzr3b4lgJ+dJzX6xFJZ48aB7XBa60Z42IWQP5//DOvo2r7UOje3PnbYidevrXqt0m4vSTX+UnO9q0rBogTcEk8TyVx5RivX93lOl5NNh5hXJXaK22YiQh4c1UWxfeyLlXkbAKBrYn30bx/6CeMo0Nd3DPT7u1cr7TXC7x0emhDRAe2bYGSP5hV/33VuJ5zZtnGV6jkMBafHL9bzwHO7nRzWz3tgZJfgG1FYWCo1SSkvkFJ+LqXcKqXcCOBWAG0B9AUAIUQjAGMAPCylnCelXAvgNgBDhBCD3E16/LnktOAzMKt1b9HAVI9avVrV8Nu9Q3G/4uK73+GFOO7CHrjYxKzReoyWmvhHv9a44FT3hf6ldgAAIABJREFUln4xImBvwq9w0ft9T2vtXwCx2qhjp9JrpE0T60vr/P3wMFfToKd2jPWSG3nzuj6236s32VqtGgnY+vxo2/t1wk5EQI9TGpraLhLhwFrX4eMX+IdIPzjKWmOmmXHWTsK4q5K/HhqGDi413sVqZdzs9eO2M9v6H68Eod0o+Oho60MKgnlsdDf88H+D/WZ7fuKC7vj57rNQvYo3IDptmI2n3n0794xeLRthWNfwVaQfUnSGtbVR7iL3OM05fKX4Y95/+8LTOz3Ht4GUMgnAfgCDtXYghKglhGjoewCImUFQkR6DCkSu4BSOz7VyfGOpJyRaxNONj5ypFqcVMKfRQnYOSyxFKEU7NgyERrQc1Wj5fTmEwRmWv6quqn7l2K5ECyESALwFYKmUcov36RYAiqWUWarN072vaRkHIFvxOGA3TbHOzg3ljrM7WA7tGNzR0wN89ZkuhIXazDvHX9ozyG6ZKfuYOS3q1qyGBrWthSzHcx06WgpnToWrbFctQaB6grB8DrkhlN9xzNkdAAAX9rIX1WInH+qtEaKq3M+1fa3PCmymQdFXEXBSISiXEpf3cX5fsDpZ3IjuzTWfj+ZhGEM7N4t0EiqMGdrB1f2p84GWIVzqzUzafdn5oI5NLO9/QAfPe4zOyXIX8iCrq25EE6fnj4Q0FdLd+qQ6jj4nHO44W3+Yotlhho/amJzRLl95PlKqevuTk57oSQB6AbjeYRomwNOj7XvEzroDDk6eMzQyHDvF/tPbNMbAjk0xqkfg3G1ddC74r28fiM3Pno92TSOzju6mZ8/HbWe5e9N3UmcKVX1LqzBth5mC/Lqnz0OCxVpxQoKI+Oy3wVKsNVnZLYOtLzvk1K6XLgzbZ4VyGIPe8a6WICCEwJqnRmkOs3jmEuNGr2jVq2UjbHr2fLx/45lh+bxxF3YPOknhGW1DGwLspExTLiXaNKlreVZ4ZR467a4hePt6a0MOPr2lH1Y+ORLNVZ+bYFBCubxP5GZ13/zs+fji3wOCbndVCOYv0GI1HDfYOfK8avWABY8N19zOSgirXkNQr1aNsO7p80zt45vbB2Hzs+eb/kwA+O4Oz3uMhhuVuVCLnnh1b7/vcXaX6GlkCaZXq0aOywJ92gTP1/q1OwlLntA+l0KhZrUE0+fLpmfPx8bx5+P0NvqNAWYnTz0rjA1sjevWwNbnRqO+yw2OT17U3e/v9Sav0arGViVaCPEegEsADJdSKnuODwOoKYRQn4WJ3tcCSCmLpJQ5vgeAXDtpigQn2a5brTe+/TTU6EHS61VKSBBoULuGK59vp/7Z0ORnWzlGTurBoer1blzXpWNsInl2ZldOECLqQ7q1UheJXuYaYRwzF4neYN95UKt6NdSuHngu1Yiy2f3N9rYK4clvtM4ZM/uweqrVMVHIiuYgiXLvMXFSIGtYu7rla1QIgcSGtS1dZ5HMuxrUrmGq0TIS17Ib1JdGjWra37WWS/mC2WVy7JRdzLzHjXBuIYTf94i11SrCda66VfY0e3zNfl7D2jXQqI7xtgk6+Vqkh3bWq1Xd9fxQXS7WOz+qeEe05SWuhLcCfSWAEVLKvapN1gIoATBS8Z5u8Ew+ttxhWuOK1onXt507PRS+HmhlWF6X5jEz1BwA0L9DE0sFd6MLWS/jU35WKDSu618wuNjixHFdEz2/o9nwT6sFAb1IhWhy46Dw9zqH2xWu9qgZnwPX9NUOORwUhpAwvUmYQlmpNDsDfagrZEbf0UyI4HX927qYGn12O+SUX89Z43Llu8/q3BSXnGZwbTj4ILcasZUhwqe3difyyCxfL3Crxu6HyLoR3qxmtbFaWUEPdf4Uqu/bSWcix1jj5uzlAXmhzWOvnqROy00uR625e692ifd4RnPjbDyz2vQ0CcANAC4HkCuE8MUdZkspT0gps4UQnwJ4QwhxDEAOgHcBLJdSrnAt1VHC7bEA6uUeuiU2wI50/475BrWqI7eoNPDNynC6u4dgy4FsDOrYFCO6N0dWQYmtmZMjqU+bxkErv2YZhQROu2sw+hiE7zhRp0blB385ZgD6tzdfWR/VIxFvXd8HG/Zn2RoHFswf9w1Fy8Z1TGW8d5/bCe8vSAYA1KtZDfnFZbrbzntkGEb8b6ErafxqzEAM7NgEMzYdwsGsE4725fa1Ou2uIbj6g2Wu7Gvi1afh1w1pFX87S6rxD3rnOR1xWutGKJcSN326quJ5txrwjHx+2wCs35+FxnVr4JJ3lzjal9meTnVDlp6R3Zvjr23pTpJk2eLHhyOroAStGtfBqv+OxJ6j+bj+48Db5KonR6JJvZp4Z+4uw/35jomTc90X1hoNEy19cnM/1K5eDR2b1UNhaRl6tWqEAS/NrXi93CCNNasloLis3PU0/XrPWX6h7jcObIc2TeqioLgM3Vo0wEiX8j4jb13XB4kNa2NghyZYsTcTp57SCKnHC1z9DCe/f6vGdTTza7O9dXee4xmTunTsCCxPzsQpjWqjn4V7px1G5xIAvHJ1b3RuXh8tGtXBtLUHUC1B4LXZO4Lu95Hzu+Hur9f5PTfl1v647fPVjtJrxcW9T8GMzYcc7WPSjWdiW1oOCkvKcc8364K/QYeE83vxinEjcTDrBP7adhhLdmfobtenTWOMvbC77ut69G4t39w+0HZjzrCuJ2PhzqN+z13TtzV+WhuaKaBW/Xck9mcWoG2TutibkY/rFPeVZWNH4MK3FyP7RIkrnxUN94pIshqLcxc845YXADikeFyn2OYhAH8AmAZgETxh3Fc5TWjcMXHiNWsQWACsZSJst2HtGhjSuRkSEgTaNKmL3iFsIQ9FaK2vB9ZSOmx+Vt92ob05+5zd5WRLIdfdWtRH/VrVMbRLs5Asv6G3PqeW01pXNjJ0CNKy3tHFpcKGdmmGGtUSUF0nlDCSurdo4NrEbHZC8fUEuxyrJQic1bkZ6oVwwia9sK963vPZyrmnJ1w3buXxNDtO2GzSlHlz8wa1MahjU82w2eYNa5vKA9w4JuU2u+RC0QtSt2Z1JCQIDOncDCO6J6J5A/+oAnVKExtW/j4tG4dmIqw+bRr79fwmJAic2605Lup9imuhzcE0qlsDgzs19RybTs3QyKVhQ0pOTiW9ZfTM8l0DzerXwqWntwx5BRoI3hPdvGFt9G3XBK0a18H9I7uYDiWurrpJ9GnTOOxjpd3oDa9dvRpG9kg0HU3XvmnoOm1aNKptqtH3rM5NXR2G5StT21G7RkLAHDkX9XZ37hNlypo38DQ8NW9YGwNVFf+WjeuErOOoKrK6TrTQeXyu2KZQSnmPlLKJlLKelPIqKaXmeOhYZ2b8mx6tPLupalzQoA7mW716RmjdRyd6t/K/kH35k5kwnVjQvUVof5NwHSdlwd7MWElfmHio1lDVG58XS8z2yrRSzWYabNxauArybtIqsLcLYSGs4nNNbCMgKo7pAI3CfLdE/2EyZiJ+2pykv40b8zM4qUqXeX+McFRctFhplFXnfwMs3C/JmNlZlK00iEXzihtWG6Dq1bTXCBnKkFutOVhq1zB/P3BasVJ3fugNF1Mfg0Gd9K9bo/Mw2PnUSdGgH4ohD+rPOs2gs6p3q0YB31vdKGhXZ4cdF3rniNl7cPsITVAcLWKvxBVFGtWpgbev74NRPRLxgcbsry9c0avi/+2a1sWb151e8bcyz558cz+8es1paN/M/2S8d0RnPHNJT9wwUDkeTp3Ze/6+ZUh7PHFBd/xx31Db3yeYxy9wPm2/ckbTe0d09nttzsPD8Oj5XfHfi3sA8L+xPXNJT6x5apRu610obk73W1w6TKlBreq4aXA7PHFBd/x+r/XfRGvd3k9u7uf392Oju+HR87tizsPDAJgrgD5+QTfMvP9s3dcfHNUFH93U1+855W4TGwbP+L8cMxAPn9cV79/YN+C1dk3r4uOb+qJW9QTMe2SY5vsfG90Nsx7QTuOr15xmqnU51MU1o0P9ze0DdV+77PSW+OzWfrqvq/Vv3wTPX34q3r/xTDxyXlfMuC/wuCiXVqlbsxreu+GMoPt1oyNXaxZvz86d7/tFRd7p4+a4PCtmP3gOHj2/K8Zf5j9D+ZltG2OqIj+7rl8bnKszW7EA8M0dA/HspT31j1sQHZqFvrDiC+cOtgRhIO0LYpxGOOUj53W1mqwKn97iuXbO65mIm1XjHZXnjDIvXPPUqID9RGsAYoPa1fHMJT3xrOXjb42UEt/cPlD3cwarKjZCCDx32akB5+B/hukvBxTwmRE46maHqShn537igu5Be1Ibqiag+togz1ezcxTuOLsDfvy/wbqv33VuJ/x2T2U5Y0inpnjkvK7484FzArZ9/IJumr3hD53XFY+N7oa/HzoHcx4ehluHtLeUxmtVS3z5ygFGbhjYFq9dc5ru69//R/87K3v5P72lH6bfc5bf61co5gT6weDYTbiqN6bc1t8wneoOLqUnL+qOK/q0wj3DO+Ox0d3w54Oee/RHN/XF7UM74LHR3XDHOR39Is5eurKX7Ygs3+zhP989BOMu7I6rz3S2oNHKcaPw9CU9/Ro9nr6kJ87r6b/ij955O7JHczytsXqHVqNzPGIl2qHL+7TC5Fv64QKNdUhvUkyKdMGpLXDlGZUnu/KGMqpnIv6hscagEAL/HtrB1BqnNaol4K5zO7kSKqnn7nM7BzxnVFnRCv1R9qzVU/Xkdzy5Pu4d0UVzNsV/D+2AZvVr4f0b+2oWzEIRVu5kjdLERrUrfhM74fRa3+e8nonooYg4qFerOu4d0aViYiIzrel3n9sZPVvq95DffW5njD61hd+ETFZ7EFo0qo37R3bRDH9d+NhwnH9qC+x48UJ0PLm+ZsZ8z/DOft9T6aLe1iZni4QhBstbvPPPMzCie+BydEZuHtweF/U+BfeN7IK2GoW724a29/vbcDImFzWpZ20ZJD3qU71bYgPNGfxvGBi6Seb0rhwhgPbN6uHeEV0C0nTfiC5oobhObhva3jAfGtKpGW49q4PtvGq0yaXPnIR1+97q1gy6Tev7nyOXnt4S9zlonBzZIxEpEy/GJzf389wfFUsl6s2s26y+O+dpOAh47nW3urwEpJYhnZvpfo4QIiC67ZYh7TH/0XP9nqulMZO/WVrLcrrNbCVaOSb6rnM7BUw0GOyKNbukkZ0rv2tiffz34p6Gc6rcO7yz373h5sHtcd/ILmjfrF5A3tb6pLr4cszAgMbeujWr457hndElsQE6N6+Pe4YHlveMqI+ZrxygptzqvhGdDeetMOpBVjamj+yRiNPbNPbrcFKGXhvt558D2mJ4N+116n26GAwxvPOcTkhIEKhdoxruGd65IgJx9Kkt8NQlPXHP8M6oVb2a3xCAGx3cy3yBeGe2PQn/Gdap4nvava80qlsDY4Z2wOvXVnbyjRlq/j4lhNBcZ7x9s9iah8kuVqIjxEo5J5qXSrBaQKmpyPjcrPieZJAR273RN6xjvxLt9JvpzRbcomFoC4S+n0RZiVaef01dqjhVfJ6JbZQ3SzcnUbYS6maV7/cLdQG+RcPafue3r8EjePiafgak1fihtYTeSTpjMdUh6FYl6syorRxK0LS+uQnDjCjzIr3xzm6Fx6t7rkLBF2Lq5LNqhfCaCIUm9XTOwSDnvxvnTyi0NBF2WsfFORSiUeM67v02wZYs8mlhchZ/q9RzT7gVwqumLkrVC7JWvedNxi8rh01ZHQscquOpFKq5UmpqRLqdrPO7mR32ABgP43GDUZ7nOz+ae+9zWvc7rblZ9MovZobUnaTRe39Ko9CG1UdCbN0x44iVSvSZbU/C9f3b4IkLrM80aNYLl58a8NyoHolY8Oi5GH1qIt6+vo/m+x4Y1QXn90xEiyBhvveP7IJbh7S3NEu4mUPkC2e5vr/20j3X9WuDDs3q4dYh7f1avi84tQXe+Mfpmu/xUUYOqCtcZ3dphotPOwWTb+6HYV1PxsAOTfDylb1NpNjjtWtOM5xYQu+e9fJVvTGye3N8OWaA9gYKU27tj8GKSSX+v707D4+qvvc4/v4iJIGEJIRkQiAJAQIkYSchyCJLANlc2ESwoIDWfbmtW+m1Ld1cq7W9LpR7UWzV2/t0s+216lP6PFrbWp5bu6i3V9uqtdei0lYFvS0u7bl/nDPJmZMzkzOZJDPA5/U88yQz58yZM7/v/H7n/M75Ld+4YHbK9S9pq2+vsH5hwzQWNcT46rnH0zqqjPUt1Wxf3sBli8eypKmSixfW09YQ63LKh6+fP4uRQwcxuqKQb13Y+fP79TMuXTQ2Zf/pmK/AHxTSF219SzUrJg1L2Sws7NdUHxvM186fRVtDLO0LVV3dnf/2RXNoa4glNPMLu1rbXd+5eA4Lx1fw5bNbQ5v53rNtBnPqhzKidCA3rZvM6S01SUcq/dDixCZ3Vy1rYHFjJbs2N7Nldh2XLRrLypAWAGfOquvUSqatIcadmzo34+/Kzk3TKcw7jhl1Q7hhbed89K9ntrCkqZLV00aw4+Qmzp8/JmH5oobUdxLC1JQNYtucUVzSVs9VS8eHTlGUaqC8KM1Tp9SUsnJyVbea3JUV5iV0EwqeKD/ga764fOIw2rw0WD1tBCdNruLa1ZPYMruOSxeNZdPxHXdo1kwbEdpfcV1zdUJ5kay7RS6Nv7Ft7iiWTRjGrae7x6fr10xyy+WzWjh16vDQ4xq4d+5ObKpkfUs1ixtj3Oy7C3PylOHM9e4sJjvuZSrYXP7rXjm0s4u8s76lmpk9PCXj9uUNrG+pZu+H59HWEAstp1O5bs0kRvdAV4ObT5vC8onDemRaolvWT2HZhGEJLRVSuXHdFBY1xFJ2xUkmVfeZWYFBnT61Kvz3CG7Zect6bz8+GL4f92xrZXFjYll3wtjy9uPiNSsbWTu9mjljOu6MB881rf1v6mNY6aA8zps3mvPmj064GJGqBcH9H5zJooZYwl1Nf94KSnUefI3XrS+ZsMpuqu3df87MSMcJ/znqhOHFLG6M8fFAc+UHvOP7nq1dn4fFXbF0PEuawrt+hjmxqbLLc1S/286YRltDLLTZ/3cumktbQ4x7vd/3R1c0srixMqGL4LjKIjYdX5vQBD/ZOcOXNjfT1hBLOKe8ad1klk90z6tP9M4R42rLBtHWEOP2M6J99yNJ38yuLp2u+qXT4M7MuH6tW0HY/aMXem6ffMKaz/6b1//sS5uT9+EsGTiAXV5GrPvIg0nX66pvTHed5zuZ3nz8SL7y05cSlseb4ew4ZQLP/PEge//HncZm5+auT/TzfHehdm1u4cy73CmBRpUX8pWzOw5yi319Rz76racj7fdpLTWc1lLDyi8+zn/vP9RpebIrv1UlA9m9JXX/HYBL2+pZ2BDj0ecO8MQLfwG6btp2+Ykdfd5rygYlfM6N6zoKc3/Be+ejzwMd0zMFtdSV8diVC1N+7oeXjOOZpspuTXv0++tXJjy/8utPpfX+GXVlzNhSxrJbf8izr77V9RuS2DZnFHf9+MX25xNHlHBXIE492RR9cnUpd6c4gNfHBnPfOce3Pw/2V/OfbFy2OLHJXVlhXnveP9HXhLh6yEBefqNj6pqCAf24c1MzS255jN8eeBug03eOatnEKpZN7EifA4feSVge75/1ea+y9ORLbyQs371lRsryJ5mP+yozu85sYea1HVMondoDc4J+/KSmbk8ftmtzc8oBvqbWlHb6/YPbcuO2kJOVz6zquDjxiz+8weo7Eqdo+1zgZDfZBYTvXXYC3/3Vfi7591+0v5Zuo6IPzKzlvn1/SO9NIQbl9U8oyze01rKh1b1g8IUNySs3gws6jltxl3/tV4B7l+VeX2Xq1YOHue6hZzPeV4CF4yva8+3vDrzdngYtdWXctSV15Xh0eWFCOdxT/MfQ7uTfja21TBpRkvG0dWubq1nbnP7FpqqSAl45eDjhtTXTq1mTxoWrEaWpj6upWs2l6j7jP47PrS8nNriA95JMvRZP++B++yu788dVMH9cRUJZ98lTOirm55zQuY96sot9UfLs9hWdK7JLmmLt51FBs8eUM3tMYtP2tc3V7XnL/dxohYX/u4RN7ZruAKOz68uZXV9O48ce5m/vJZ+m06+tIZZwXhQ3taY07bxSMnBAp3Ftgs6eO4rdP3LPI+Ll07d/uT9heqxk6TdyaGHSfZpUnXg+4j/G+7frP0a464W35BhVXtTps+LntND5N9xSN4Rb1vfOBcls053oLOlus9RgBsrlKdoynec52fuTbbavJpuP8jFR9yVZs+2emiO7qwNWpp/TF2ke3Mfu7HNX6dCdpmH+/YiyS/6+qqnuAmTSZD1q0nQnbsF0j3+djH9DYa91scne+N0FN5nuR8TTI5N9S/XenvzKmXalCb7dfxzy/58sTbNdhqcSzJuZxTO4rfTKDL/uTq8TJkr8+3Xz7DDZtnsyXRO32/O6KtOi7Htwne5+30zTKdmxprvldtj2oo6bElwr6ulr2K4OCOlqEyWL9GSXsJ4Wtm/B1/py95PFNd1xN3J5ZP5MqRLdQ8yMBePdkVlLBw3gXu9O5QULxlBVUsAHvatqG1trqBs6iLu3zqCmbGDC4GMAe7bOcK8SJblitXNTM2VeX4OpNaUJw/h3x7IJw2iqKmZqTWnCFAfJRu3cOqeOEaUD2RQyMIJ/5O3Ll4zjqqXjqRicz5VLk4/qHW9CGfZ5yd5/+oxaRpQO7DR65MUL66kszufStnrOmjWS6iEDWe9r5t1YVczk6pKkTXouXzKOwrzj6N/P2uOydEIlE4YXM9s3WumuM5Pfxb5uzSSGFuZFvup2w9rJlBfltY/kvmW2m74bW2u7eGdn/j4xZ3u/twsXjKGyOJ9L2sIHCQl+fro2trqx2Dqnrlvvj0sVmx2nTKC8KL99BMhtc0dRVVIQOjLsqqnDqY8VccK4xKvhwWaSwXx307oplBfl8ZlVE9m5aTplhXmdmvb5m2YXDOjHV86eSVlhHrefMZ3z549hWHEBFy1MbGYMbh5r9PJY3KlThzNy6KD2gVB2n9VCWWEed2+dwT8tHkdscH6nZtapXLva/d0F7yYmM6W6lKaqYpZFHKwK4F82TmsfpbRl5JD2/mA3r5/C0MK8hO4Mly9xv8OerTMoL8rrlL/9zWSvCCkfJgzvaFr9xY2d7yhOHlHSPqDe6mnuKKyXtLn5/7o1kygrzGNPYMTVeJ7yN232qxicT6vXVLZicH6n/crv3699VNv6WFH7YEJtDTGmVJcwzpvu6oIFHb+BsCbiqfhH+w5OOePvDpBO15EwE4cXM2F4MUX5/SkrzEvajDiYr+NNLBc3VrbPBzy1ppTR5YUsHF/BlJrShKbiKyZVtXc38Jfll7SNbS+r/W47YzpDC/O66JqRvvjnpmrOGf/NfvjExHyX7HgTNKJ0IC0jh1BelNdepsbL34IB/RLKMEieBnHxY9g1KxsZWpjX3mQ9TGNVcfs8tK2jyhIG37xmZSNlhXncs62Vz66e6JUTXafvjWs7ykS/sHMUf0u2T5zcxAe8cm35xGHsOLmJ8qI8Prva3c725Q1UDM7nn0PuckYVz/NF+f25Y1Mzp7fU0M/cO5ZhA4+m6/OBtA42y54/roL6WBFrpo0g6Opl7vfbvtz9ftesbKS8KJ8d3h3j/v2MeeMqmFZbyn3nuMeQO0Ka+KYq0/3lZ1dTDMWPl+C2Zoi36pleW0pjmscAgJOmVFE3dBAbW2s6xTaZK71zuSuWjqcw7zjm1pczo24Iw739ir8G7lgbOzclpsfuLe5vzv+9V00dwcihgxLOleJ56rIUgxfevbW1vVJ+7rzw0eXPmzea4SUFbO3lAf5uP2N6+7gbZ8ys5dx57nnEhb5jyMdOaqK8KJ+asoGUF+Vx7ZrMyv50zK4fythYEadMGZ7wu406rVWyMvVoYpmM5NkbzKwYOHjw4EGKi3On71UUCXeafJfOHMcJfR58Pdn6UT+nu/yfF992V5+fbHnw/V19lyjbSyeNgt+l8537aGmbLF7BZel+nyjrp/v+4LaC+xjlO2fyO8r0/VG2k04apfptxHXnt+PfRth66e5Tqu/UnTTN9HcX9T1xXcUjSjkX3E6my3u7bE2njE+176mkem8m2w3bVqq0Cn5m8HODryfbt2Sf090YdVcmx6Ko+xSWVqnSOWrZHHXf/Z/tj0d3y5Z00iOdsrEnYhz2ef7nmUrneNHVslTlQ3eOZcFtdCXdPBh1e+lso6vzzO6kd3fyVLLPj/J5vSHKOXM65UBv7F8meSwb+5ypQ4cOUVJSAlDiOE7n/pY+qkSLiIiIiIjIMS2dSrSac4uIiIiIiIhEpEq0iIiIiIiISESqRIuIiIiIiIhEpEq0iIiIiIiISESqRIuIiIiIiIhEpEq0iIiIiIiISET9s70DyRw6lHJUcREREREREZEekU79MxfniR4BvJzt/RAREREREZFjTrXjOH9MtUIuVqINGA68le19iWAwboW/miNjf48lik1uU3xym+KT2xSf3KXY5DbFJ7cpPrntWInPYGC/00UlOeeac3s7nLLmnyvc+j4AbzmOo/bnOUSxyW2KT25TfHKb4pO7FJvcpvjkNsUntx1D8Yn03TSwmIiIiIiIiEhEqkSLiIiIiIiIRKRKdGbeAT7p/ZXcotjkNsUntyk+uU3xyV2KTW5TfHKb4pPbFB+fnBtYTERERERERCRX6U60iIiIiIiISESqRIuIiIiIiIhEpEq0iIiIiIiISESqRIuIiIiIiIhEpEp0N5nZRWb2ezM7bGb7zKw12/t0tDGzHWbmBB7P+pYXmNntZvYXM3vbzL5hZpWBbdSa2YNm9lczO2BmN5lZ/8A6C8zs52b2jpn9zsy29NFXPKKY2Twz+66Z7fdisSqw3MzsU2b2ipn9zcz2mtnYwDplZnafmR0yszfNbLeZFQXWmWxmj3t563/N7KqQfTnNzJ711nnazFb0zrc+MkSIzZ6QvPRwYB3FppeY2XYz+y9Hg4KBAAAHq0lEQVQze8srhx4ws/GBdfqsPNPxq0PE2Dwakn92BtZRbHqBmV1gZk955dIhM3vCzJb7livfZFGE+Cjv5Agz+4iX/rf6XlP+yYTjOHqk+QBOxx3efSvQBOwC3gBi2d63o+kB7ACeAYb5HuW+5XcCfwDagGbgCeDHvuXHAU8D3wemAsuBPwHX+tYZBfwfcDPQCFwMvA8szfb3z7WHl36fAVYDDrAqsPxq4E3gVGAy8G3gBaDAt85DwC+BmcBc4LfA/b7lxcCrwL3ABGAD8FfgXN86s70YXenF7NPAu8DEbKdRDsdmj5f2/rw0JLCOYtN78XkY2OKl2xTgQeAloNC3Tp+UZ+j41Z3YPOqlkz//FCs2fRKfk4EVwFhgHPBZr0yZ4C1Xvsnt+Cjv5MADmAG8CPwKuNX3uvJPJuma7R04Eh/APuA23/N+wB+Bj2R7346mB24l+pdJlpV4BfU632sNuBWI473ny4G/A5W+dc4HDgJ53vMbgGcC2/4q8HC2v38uPwhU1AADXgGuCMToMLDBe97ova/Ft84y4B/AcO/5BcDr8fh4r10PPOt7/h/Afwb256fAzmynSy48grHxXtsDPJDiPYpN38aowkvved7zPivPdPxKLzbea4/iO/EMeY9i07cxeh04W/kmNx/x+Hj/K+9kPx5FwG+Axf54KP9k/lBz7jSZWR7u1Zq98dccx/mH93xWtvbrKDbW3CaqL5jb1LTWe70ZGEBiHJ7FvaIWj8Ms4GnHcV7zbe8R3DtqE3zr7CXRIyiW6RqFe4XZH4+DuAWnPx5vOo7zM9/79uJW1Gb61vmh4zjv+tZ5BBhvZkN86yhm6VvgNcV6zszuNLOhvmWKTd8q8f6+7v3tk/JMx69IgrGJ+4CZ/dnMnjGz68xskG+ZYtMHzOw4M9sAFOLeMVO+ySEh8YlT3smu24EHHccJpqHyT4b6d72KBJTjNm94LfD6a7hXcKTn7MNtZvccUAV8AnjczCbiVtjedRznzcB7XvOW4f0NixMR1ik2s4GO4/wt0y9xjIinZ1ha+tP6gH+h4zjvm9nrgXVeDNlGfNkbJI/ZMCSZh4Fv4qbtGOBa4CEzm+U4zt9RbPqMmfUDbsVtMveM93KflGfAEHT8SipJbADux23ivR+3q8oNwHhgjbdcselFZjYJt1JWALwNrHYc59dmNhXlm6xLFh9vsfJOFnkXNabjNucO0nEnQ6pES85yHOch39OnzGwfbmG8HlDlViQix3G+6nv6tJk9BTwPLAB+kJWdOnbdDkzE7XcuuSU0No7j7PI9fdrMXgF+YGZjHMd5vi938Bj1HG5/zBJgHXCPmc3P7i6JT2h8HMf5tfJO9phZDfAFYInjOIezvT9HIzXnTt+f8foHBF6vxB10R3qJd7XsN0A9blrnmVlpYDV/HF4lPE5EWOeQ7kKnJZ6eqfLFq0DMv9Ab4bGMnomZ8l9EjuO8gFuW1XsvKTZ9wMxuA04CFjqO87JvUV+VZzp+JZEiNmH2eX/9+Uex6SWO47zrOM7vHMd50nGc7biDI12G8k1OSBGfMMo7facZ97j+czN738zeB+YDl3r/v4byT0ZUiU6T1x/wSWBR/DWvCdgiEvuASA8zd7qdMbgDWD0JvEdiHMYDtXTE4Qlgkpn5KwdLgEPAr33rLCLREhTLdL2IWxj641GM25/WH49SM2v2va8Ntxza51tnnpkN8K2zBHjOcZw3fOsoZhkws2pgKG5eAsWmV5nrNtzR09scxwk2i++T8kzHr84ixCbMVO+vP/8oNn2nH5CP8k2uiscnjPJO3/kBMAk3zeOPnwH3+f5X/slEtkc2OxIfuEO1HwbOwh3V9ku4/QErs71vR9MD+BzuVbM63Klzvo87tH6Ft/xO3ObdC3GvuP0E+Inv/fGh+R/BnbpkKW6/z7Ch+W/E7ZtxIZriKlk8iugoiB3gQ97/td7yq718cApuwf0A4VNc/RxoBebgtizwT6NUglsZ/zLuoBWne/EJTqP0HnC5F7MdHOPTKKWKjbfsJuB4Ly8twj2g/QbIV2z6JD534E7/Np/EqV4G+tbpk/IMHb/Sig3uhduPeTGp88q354HHFJs+ic91wDwv7Sd5z/+B20RV+SaH46O8k3sPAqOlK/9kmJ7Z3oEj9YE7D9pLuPOe7QNmZnufjrYH7hD5+700ftl7Psa3vAC3D9vrXgb+JjAssI2RwPdw57P9E27FvH9gnQXAL7zPeR7Yku3vnosPL52ckMceb7kBn8KtaB3GHXlxXGAbZbgDjbyFO0XCXUBRYJ3JwOPeNl4Grg7Zl9Nw+2G9gzuX+Ipsp0+uxgYY6B0AD+BWaH+PO0djZWAbik3vxScsNo6/rOnL8gwdvyLHBqgBHgP+4v3uf4t7slgc2I5i0zvx2e2VWe94ZdhevAq0t1z5Jkfjo7yTew86V6KVfzJ4mPfFRERERERERKQL6hMtIiIiIiIiEpEq0SIiIiIiIiIRqRItIiIiIiIiEpEq0SIiIiIiIiIRqRItIiIiIiIiEpEq0SIiIiIiIiIRqRItIiIiIiIiEpEq0SIiIiIiIiIRqRItIiIiIiIiEpEq0SIiIiIiIiIRqRItIiIiIiIiEpEq0SIiIiIiIiIR/T/J5LCJNEjVpwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "13 pt_sex :  object\n",
            "nan %:  0.014277555682467162\n",
            "['Male' 'Female' nan]\n",
            "Number of unique class is  3\n",
            "Male      8307\n",
            "Female    5699\n",
            "Name: pt_sex, dtype: int64\n",
            "\n",
            "\n",
            "14 ps24 :  object\n",
            "nan %:  0.05711022272986865\n",
            "['PS 2-' 'PS 0-1' nan]\n",
            "Number of unique class is  3\n",
            "PS 0-1    13171\n",
            "PS 2-       829\n",
            "Name: ps24, dtype: int64\n",
            "\n",
            "\n",
            "15 abo_mismatch :  object\n",
            "nan %:  0.049971444888635065\n",
            "['Matched' 'Major mismatch' 'Major-Minor mismatch' 'Minor mismatch' nan]\n",
            "Number of unique class is  5\n",
            "Matched                 7521\n",
            "Minor mismatch          2729\n",
            "Major mismatch          2517\n",
            "Major-Minor mismatch    1234\n",
            "Name: abo_mismatch, dtype: int64\n",
            "\n",
            "\n",
            "16 tbi :  object\n",
            "nan %:  0.0\n",
            "['1' '0']\n",
            "Number of unique class is  2\n",
            "1    9397\n",
            "0    4611\n",
            "Name: tbi, dtype: int64\n",
            "\n",
            "\n",
            "17 gvhd_pro :  object\n",
            "nan %:  0.0\n",
            "['Tac+MTX' 'CyA+MTX' 'Tac±' 'CyA±']\n",
            "Number of unique class is  4\n",
            "Tac+MTX    7935\n",
            "CyA+MTX    4245\n",
            "Tac±       1504\n",
            "CyA±        324\n",
            "Name: gvhd_pro, dtype: int64\n",
            "\n",
            "\n",
            "18 gvhd_pro2 :  object\n",
            "nan %:  0.0\n",
            "['Tac-based' 'CyA-based']\n",
            "Number of unique class is  2\n",
            "Tac-based    9439\n",
            "CyA-based    4569\n",
            "Name: gvhd_pro2, dtype: int64\n",
            "\n",
            "\n",
            "19 hctci_arrythmia_cd :  object\n",
            "nan %:  0.1142204454597373\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13776\n",
            "1      216\n",
            "Name: hctci_arrythmia_cd, dtype: int64\n",
            "\n",
            "\n",
            "20 hctci_heart_cd :  object\n",
            "nan %:  0.1427755568246716\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13584\n",
            "1      404\n",
            "Name: hctci_heart_cd, dtype: int64\n",
            "\n",
            "\n",
            "21 hctci_cerebrovascular_cd :  object\n",
            "nan %:  0.221302113078241\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13793\n",
            "1      184\n",
            "Name: hctci_cerebrovascular_cd, dtype: int64\n",
            "\n",
            "\n",
            "22 hctci_dm_cd :  object\n",
            "nan %:  0.12849800114220444\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13155\n",
            "1      835\n",
            "Name: hctci_dm_cd, dtype: int64\n",
            "\n",
            "\n",
            "23 hctci_valve_cd :  object\n",
            "nan %:  0.13563677898343804\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13900\n",
            "1       89\n",
            "Name: hctci_valve_cd, dtype: int64\n",
            "\n",
            "\n",
            "24 hctci_liver_cd :  object\n",
            "nan %:  0.1142204454597373\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13055\n",
            "1      937\n",
            "Name: hctci_liver_cd, dtype: int64\n",
            "\n",
            "\n",
            "25 hctci_liver_severe_cd :  object\n",
            "nan %:  0.1142204454597373\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13799\n",
            "1      193\n",
            "Name: hctci_liver_severe_cd, dtype: int64\n",
            "\n",
            "\n",
            "26 hctci_inf_cd :  object\n",
            "nan %:  0.1070816676185037\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13134\n",
            "1      859\n",
            "Name: hctci_inf_cd, dtype: int64\n",
            "\n",
            "\n",
            "27 hctci_bowel_cd :  object\n",
            "nan %:  0.12135922330097086\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13932\n",
            "1       59\n",
            "Name: hctci_bowel_cd, dtype: int64\n",
            "\n",
            "\n",
            "28 hctci_obesity_cd :  object\n",
            "nan %:  0.1070816676185037\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13751\n",
            "1      242\n",
            "Name: hctci_obesity_cd, dtype: int64\n",
            "\n",
            "\n",
            "29 hctci_ulcer_cd :  object\n",
            "nan %:  0.1427755568246716\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13928\n",
            "1       60\n",
            "Name: hctci_ulcer_cd, dtype: int64\n",
            "\n",
            "\n",
            "30 hctci_mental_cd :  object\n",
            "nan %:  0.12849800114220444\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13594\n",
            "1      396\n",
            "Name: hctci_mental_cd, dtype: int64\n",
            "\n",
            "\n",
            "31 hctci_lung_cd :  object\n",
            "nan %:  0.47115933752141637\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    12651\n",
            "1     1291\n",
            "Name: hctci_lung_cd, dtype: int64\n",
            "\n",
            "\n",
            "32 hctci_lung_severe_cd :  object\n",
            "nan %:  0.3926327812678469\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13485\n",
            "1      468\n",
            "Name: hctci_lung_severe_cd, dtype: int64\n",
            "\n",
            "\n",
            "33 hctci_renal_cd :  object\n",
            "nan %:  0.1142204454597373\n",
            "['0' nan '1']\n",
            "Number of unique class is  3\n",
            "0    13921\n",
            "1       71\n",
            "Name: hctci_renal_cd, dtype: int64\n",
            "\n",
            "\n",
            "34 hctci_rhumatoid_cd :  object\n",
            "nan %:  0.12849800114220444\n",
            "['0' '1' nan]\n",
            "Number of unique class is  3\n",
            "0    13873\n",
            "1      117\n",
            "Name: hctci_rhumatoid_cd, dtype: int64\n",
            "\n",
            "\n",
            "35 hctci_st_cd :  object\n",
            "nan %:  0.1070816676185037\n",
            "['1' '0' nan]\n",
            "Number of unique class is  3\n",
            "0    13312\n",
            "1      681\n",
            "Name: hctci_st_cd, dtype: int64\n",
            "\n",
            "\n",
            "36 hctci_points_corr :  float64\n",
            "nan %:  0.6710451170759566\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "37 serora1 :  object\n",
            "nan %:  0.0\n",
            "['02' '11' '26' '33' '24' '31' '03' '01' '30' '19' '25' '32' '28' '22'\n",
            " '21' '12' '23']\n",
            "Number of unique class is  17\n",
            "02    5473\n",
            "24    5193\n",
            "11    1623\n",
            "26     870\n",
            "31     415\n",
            "33     191\n",
            "01     123\n",
            "03      93\n",
            "30      16\n",
            "19       2\n",
            "25       2\n",
            "23       2\n",
            "21       1\n",
            "22       1\n",
            "28       1\n",
            "32       1\n",
            "12       1\n",
            "Name: serora1, dtype: int64\n",
            "\n",
            "\n",
            "38 serora2 :  object\n",
            "nan %:  0.0\n",
            "['24' '31' '33' '26' '02' '11' '01' '30' '03' '36' '22' '0' '19' '66' '68'\n",
            " '32' '34' '06' '21' '23' '29' '25' '10' '04' '12' '46' '53' '91']\n",
            "Number of unique class is  28\n",
            "24    5937\n",
            "26    2226\n",
            "33    1906\n",
            "31    1845\n",
            "02    1084\n",
            "11     867\n",
            "30      43\n",
            "03      38\n",
            "01      14\n",
            "32       8\n",
            "23       6\n",
            "29       6\n",
            "34       5\n",
            "21       3\n",
            "10       3\n",
            "68       3\n",
            "22       2\n",
            "25       2\n",
            "36       1\n",
            "53       1\n",
            "0        1\n",
            "46       1\n",
            "06       1\n",
            "91       1\n",
            "19       1\n",
            "04       1\n",
            "66       1\n",
            "12       1\n",
            "Name: serora2, dtype: int64\n",
            "\n",
            "\n",
            "39 seroda1 :  object\n",
            "nan %:  0.0\n",
            "['26' '11' '02' '33' '24' '31' '03' '30' '01' '19' '25' '32' '28' '21'\n",
            " '12' '23' '10']\n",
            "Number of unique class is  17\n",
            "02    5602\n",
            "24    5248\n",
            "11    1734\n",
            "26     730\n",
            "31     319\n",
            "33     140\n",
            "01     121\n",
            "03      86\n",
            "30      16\n",
            "25       3\n",
            "21       2\n",
            "23       2\n",
            "19       1\n",
            "10       1\n",
            "28       1\n",
            "32       1\n",
            "12       1\n",
            "Name: seroda1, dtype: int64\n",
            "\n",
            "\n",
            "40 seroda2 :  object\n",
            "nan %:  0.0\n",
            "['02' '24' '31' '33' '26' '11' '32' '30' '03' '07' '22' '0' '19' '01' '66'\n",
            " '68' '34' '06' '21' '29' '10' '04' '12' '23' '25' '48' '53' '91']\n",
            "Number of unique class is  28\n",
            "24    5986\n",
            "26    2327\n",
            "33    1986\n",
            "31    1918\n",
            "02     905\n",
            "11     763\n",
            "30      43\n",
            "03      32\n",
            "32       9\n",
            "01       8\n",
            "29       5\n",
            "34       3\n",
            "23       3\n",
            "68       3\n",
            "21       2\n",
            "10       2\n",
            "06       2\n",
            "25       1\n",
            "53       1\n",
            "48       1\n",
            "91       1\n",
            "0        1\n",
            "19       1\n",
            "04       1\n",
            "22       1\n",
            "07       1\n",
            "66       1\n",
            "12       1\n",
            "Name: seroda2, dtype: int64\n",
            "\n",
            "\n",
            "41 serorb1 :  object\n",
            "nan %:  0.0\n",
            "['40' '54' '07' '62' '44' '61' '52' '35' '46' '51' '13' '60' '39' '75'\n",
            " '48' '71' '59' '67' '15' '27' '38' '18' '56' '37' '55' '58' '12' '64'\n",
            " '26' '08' '49' '04' '14' '70' '81' '45' '01' '22' '31' '77' '33' '63'\n",
            " '72' '29' '06' '10']\n",
            "Number of unique class is  46\n",
            "61    1740\n",
            "07    1653\n",
            "35    1623\n",
            "62    1561\n",
            "51     866\n",
            "52     862\n",
            "60     763\n",
            "54     743\n",
            "46     724\n",
            "39     701\n",
            "44     666\n",
            "13     358\n",
            "48     309\n",
            "71     253\n",
            "15     215\n",
            "40     189\n",
            "75     173\n",
            "55     141\n",
            "59     133\n",
            "37      96\n",
            "38      62\n",
            "27      48\n",
            "56      41\n",
            "67      41\n",
            "58      13\n",
            "08       6\n",
            "18       4\n",
            "49       2\n",
            "45       2\n",
            "63       2\n",
            "64       2\n",
            "04       2\n",
            "33       1\n",
            "14       1\n",
            "31       1\n",
            "26       1\n",
            "06       1\n",
            "70       1\n",
            "29       1\n",
            "01       1\n",
            "77       1\n",
            "81       1\n",
            "72       1\n",
            "22       1\n",
            "10       1\n",
            "12       1\n",
            "Name: serorb1, dtype: int64\n",
            "\n",
            "\n",
            "42 serorb2 :  object\n",
            "nan %:  0.0\n",
            "['40' '54' '51' '61' '56' '46' '59' '60' '44' '58' '52' '15' '55' '67'\n",
            " '35' '07' '62' '39' '71' '48' '37' '65' '75' '13' '32' '45' '22' '05'\n",
            " '57' '42' '38' '27' '50' '26' '33' '09' '53' '04' '76' '29' '64' '70'\n",
            " '41' '01' '14' '47']\n",
            "Number of unique class is  46\n",
            "52    2898\n",
            "61    1607\n",
            "51    1573\n",
            "54    1498\n",
            "44    1317\n",
            "46     613\n",
            "35     590\n",
            "55     541\n",
            "59     448\n",
            "62     448\n",
            "60     448\n",
            "48     396\n",
            "39     350\n",
            "67     274\n",
            "56     211\n",
            "58     187\n",
            "40     130\n",
            "07      99\n",
            "71      95\n",
            "75      64\n",
            "15      45\n",
            "13      44\n",
            "37      42\n",
            "38      25\n",
            "27      13\n",
            "22      11\n",
            "57       6\n",
            "05       5\n",
            "45       4\n",
            "50       4\n",
            "64       3\n",
            "04       3\n",
            "29       2\n",
            "42       2\n",
            "32       1\n",
            "47       1\n",
            "14       1\n",
            "33       1\n",
            "26       1\n",
            "53       1\n",
            "70       1\n",
            "65       1\n",
            "09       1\n",
            "01       1\n",
            "76       1\n",
            "41       1\n",
            "Name: serorb2, dtype: int64\n",
            "\n",
            "\n",
            "43 serodb1 :  object\n",
            "nan %:  0.0\n",
            "['62' '54' '07' '44' '61' '52' '35' '51' '13' '60' '15' '39' '75' '38'\n",
            " '48' '71' '46' '59' '40' '55' '27' '18' '56' '37' '67' '58' '12' '50'\n",
            " '65' '64' '26' '49' '04' '14' '70' '08' '45' '01' '22' '31' '77' '72'\n",
            " '29' '63' '11' '16' '10']\n",
            "Number of unique class is  47\n",
            "61    1786\n",
            "35    1682\n",
            "07    1673\n",
            "62    1582\n",
            "52     957\n",
            "44     909\n",
            "51     889\n",
            "60     774\n",
            "39     707\n",
            "46     654\n",
            "54     474\n",
            "13     365\n",
            "48     304\n",
            "71     238\n",
            "15     215\n",
            "40     189\n",
            "75     174\n",
            "37      92\n",
            "55      83\n",
            "59      68\n",
            "38      59\n",
            "27      44\n",
            "56      23\n",
            "67      15\n",
            "58      15\n",
            "18       5\n",
            "08       4\n",
            "49       3\n",
            "04       3\n",
            "45       2\n",
            "26       2\n",
            "65       2\n",
            "22       2\n",
            "14       1\n",
            "63       1\n",
            "31       1\n",
            "70       1\n",
            "29       1\n",
            "11       1\n",
            "01       1\n",
            "50       1\n",
            "16       1\n",
            "77       1\n",
            "72       1\n",
            "64       1\n",
            "10       1\n",
            "12       1\n",
            "Name: serodb1, dtype: int64\n",
            "\n",
            "\n",
            "44 serodb2 :  object\n",
            "nan %:  0.0\n",
            "['60' '52' '51' '61' '56' '46' '54' '59' '44' '58' '15' '55' '67' '48'\n",
            " '40' '07' '62' '71' '35' '39' '14' '75' '13' '04' '37' '32' '45' '22'\n",
            " '05' '57' '42' '38' '50' '26' '33' '09' '27' '53' '76' '64' '29' '70'\n",
            " '41' '18' '47']\n",
            "Number of unique class is  45\n",
            "52    2884\n",
            "54    1777\n",
            "51    1557\n",
            "61    1554\n",
            "44    1101\n",
            "46     703\n",
            "55     573\n",
            "59     523\n",
            "35     505\n",
            "62     415\n",
            "48     399\n",
            "60     397\n",
            "39     338\n",
            "67     297\n",
            "56     221\n",
            "58     182\n",
            "40     137\n",
            "71      93\n",
            "07      89\n",
            "75      63\n",
            "15      49\n",
            "37      37\n",
            "13      36\n",
            "38      19\n",
            "22      10\n",
            "27       9\n",
            "05       7\n",
            "57       6\n",
            "50       3\n",
            "42       3\n",
            "45       3\n",
            "04       3\n",
            "64       2\n",
            "14       2\n",
            "33       1\n",
            "76       1\n",
            "32       1\n",
            "09       1\n",
            "47       1\n",
            "53       1\n",
            "18       1\n",
            "70       1\n",
            "29       1\n",
            "26       1\n",
            "41       1\n",
            "Name: serodb2, dtype: int64\n",
            "\n",
            "\n",
            "45 serorc1 :  object\n",
            "nan %:  13.163906339234725\n",
            "[nan '01' '07' '09' '12' '10' '08' '04' '03' '15' '05' '14' '06' '02' '11'\n",
            " '13' '61' '17']\n",
            "Number of unique class is  18\n",
            "01    3562\n",
            "09    1972\n",
            "07    1924\n",
            "10    1662\n",
            "08     782\n",
            "12     710\n",
            "04     550\n",
            "14     438\n",
            "03     320\n",
            "06     106\n",
            "15      80\n",
            "05      51\n",
            "02       3\n",
            "17       1\n",
            "13       1\n",
            "11       1\n",
            "61       1\n",
            "Name: serorc1, dtype: int64\n",
            "\n",
            "\n",
            "46 serorc2 :  object\n",
            "nan %:  13.192461450599657\n",
            "[nan '01' '14' '10' '09' '15' '12' '07' '04' '08' '03' '05' '06' '02' '84'\n",
            " '11' '55' '24' '16']\n",
            "Number of unique class is  19\n",
            "14    2753\n",
            "12    2510\n",
            "07    1456\n",
            "08    1268\n",
            "10    1187\n",
            "01     909\n",
            "09     793\n",
            "15     536\n",
            "04     459\n",
            "03     157\n",
            "06      85\n",
            "05      34\n",
            "02       8\n",
            "11       1\n",
            "84       1\n",
            "16       1\n",
            "55       1\n",
            "24       1\n",
            "Name: serorc2, dtype: int64\n",
            "\n",
            "\n",
            "47 serodc1 :  object\n",
            "nan %:  15.01284980011422\n",
            "[nan '07' '09' '01' '12' '10' '08' '04' '03' '02' '05' '14' '15' '06' '13'\n",
            " '70' '61']\n",
            "Number of unique class is  17\n",
            "01    3724\n",
            "09    1979\n",
            "10    1683\n",
            "07    1671\n",
            "12     731\n",
            "08     727\n",
            "04     521\n",
            "14     372\n",
            "03     301\n",
            "06      99\n",
            "05      50\n",
            "15      39\n",
            "02       4\n",
            "13       2\n",
            "61       1\n",
            "70       1\n",
            "Name: serodc1, dtype: int64\n",
            "\n",
            "\n",
            "48 serodc2 :  object\n",
            "nan %:  15.048543689320388\n",
            "[nan '14' '10' '01' '12' '15' '08' '09' '07' '04' '05' '06' '03' '11' '02'\n",
            " '84' '55' '16']\n",
            "Number of unique class is  18\n",
            "14    2821\n",
            "12    2507\n",
            "07    1619\n",
            "08    1240\n",
            "10    1134\n",
            "09     716\n",
            "01     675\n",
            "15     503\n",
            "04     418\n",
            "03     152\n",
            "06      77\n",
            "05      29\n",
            "02       4\n",
            "11       2\n",
            "16       1\n",
            "55       1\n",
            "84       1\n",
            "Name: serodc2, dtype: int64\n",
            "\n",
            "\n",
            "49 serordr1 :  object\n",
            "nan %:  0.0\n",
            "['08' '04' '03' '09' '14' '13' '01' '12' '15' '16' '11' '07' '10' '06'\n",
            " '17' '59' '18' '21' '53' '02' '52']\n",
            "Number of unique class is  21\n",
            "04    5000\n",
            "08    2193\n",
            "09    2085\n",
            "01    1642\n",
            "15     910\n",
            "14     655\n",
            "12     566\n",
            "13     469\n",
            "11     287\n",
            "10      73\n",
            "07      60\n",
            "17      26\n",
            "16      20\n",
            "03      12\n",
            "06       4\n",
            "18       1\n",
            "21       1\n",
            "52       1\n",
            "59       1\n",
            "53       1\n",
            "02       1\n",
            "Name: serordr1, dtype: int64\n",
            "\n",
            "\n",
            "50 serordr2 :  object\n",
            "nan %:  0.0\n",
            "['15' '04' '05' '11' '09' '08' '13' '12' '14' '01' '16' '17' '10' '07'\n",
            " '06' '03' '51' '02' '52' '19']\n",
            "Number of unique class is  20\n",
            "15    4635\n",
            "09    1951\n",
            "14    1654\n",
            "04    1461\n",
            "13    1422\n",
            "08    1219\n",
            "12     843\n",
            "11     356\n",
            "16     211\n",
            "01     128\n",
            "10      59\n",
            "07      36\n",
            "05      12\n",
            "17       7\n",
            "06       6\n",
            "03       3\n",
            "51       2\n",
            "19       1\n",
            "52       1\n",
            "02       1\n",
            "Name: serordr2, dtype: int64\n",
            "\n",
            "\n",
            "51 seroddr1 :  object\n",
            "nan %:  0.0\n",
            "['08' '04' '03' '09' '14' '13' '01' '12' '15' '16' '11' '05' '07' '10'\n",
            " '06' '17' '59' '53' '02' '52']\n",
            "Number of unique class is  20\n",
            "04    5161\n",
            "08    2119\n",
            "09    2062\n",
            "01    1672\n",
            "15     808\n",
            "14     634\n",
            "13     582\n",
            "12     546\n",
            "11     263\n",
            "10      59\n",
            "07      48\n",
            "17      24\n",
            "03      14\n",
            "16       9\n",
            "06       2\n",
            "05       1\n",
            "52       1\n",
            "53       1\n",
            "02       1\n",
            "59       1\n",
            "Name: seroddr1, dtype: int64\n",
            "\n",
            "\n",
            "52 seroddr2 :  object\n",
            "nan %:  0.0\n",
            "['15' '05' '11' '09' '08' '14' '13' '12' '04' '01' '16' '17' '10' '06'\n",
            " '07' '03' '51' '52']\n",
            "Number of unique class is  18\n",
            "15    4870\n",
            "09    2045\n",
            "14    1576\n",
            "13    1346\n",
            "04    1338\n",
            "08    1289\n",
            "12     797\n",
            "11     311\n",
            "16     204\n",
            "01     101\n",
            "10      67\n",
            "07      36\n",
            "05      12\n",
            "17       5\n",
            "06       5\n",
            "03       3\n",
            "51       2\n",
            "52       1\n",
            "Name: seroddr2, dtype: int64\n",
            "\n",
            "\n",
            "53 genora1 :  object\n",
            "nan %:  8.266704740148487\n",
            "['0201' '1101' '0206' '2603' nan '2402' '0207' '3101' '2601' '2602' '0301'\n",
            " '0203' '0101' '3303' '0210' '0302' '2420' '2404' '3001' '0102' '1102'\n",
            " '0202' '0242' '3301' '2425' '2408' '2605' '2401' '3300' '0218' '2407'\n",
            " '1120' '0205' '0259' '4601' '2010' '2101' '0208' '0224' '2406' '0260'\n",
            " '2446' '0272' '2433' '3309' '1103' '2901' '3201' '2301' '0303' '2403']\n",
            "Number of unique class is  51\n",
            "2402    4745\n",
            "0201    2508\n",
            "0206    1763\n",
            "1101    1484\n",
            "0207     669\n",
            "2601     513\n",
            "3101     334\n",
            "3303     132\n",
            "2603     131\n",
            "0101     123\n",
            "2602     117\n",
            "0301      74\n",
            "0210      72\n",
            "2420      40\n",
            "1102      28\n",
            "0302      15\n",
            "3001      15\n",
            "0203      14\n",
            "0218      13\n",
            "2401       7\n",
            "0202       5\n",
            "0102       4\n",
            "2408       4\n",
            "3301       3\n",
            "2406       3\n",
            "2605       3\n",
            "0242       3\n",
            "2425       2\n",
            "2407       2\n",
            "2101       2\n",
            "0205       2\n",
            "2403       2\n",
            "3201       1\n",
            "0260       1\n",
            "2446       1\n",
            "2433       1\n",
            "1103       1\n",
            "1120       1\n",
            "0224       1\n",
            "0208       1\n",
            "3309       1\n",
            "2301       1\n",
            "0303       1\n",
            "3300       1\n",
            "2901       1\n",
            "0272       1\n",
            "2010       1\n",
            "2404       1\n",
            "0259       1\n",
            "4601       1\n",
            "Name: genora1, dtype: int64\n",
            "\n",
            "\n",
            "54 genora2 :  object\n",
            "nan %:  8.309537407195888\n",
            "['2402' '3101' nan '2601' '3303' '0207' '0101' '0206' '2603' '2420' '3103'\n",
            " '2602' '0201' '1101' '1102' '2410' '3001' '0301' '2461' '0210' '0203'\n",
            " '3301' '5201' '2408' '0218' '2407' '6601' '6801' '0215' '2424' '0402'\n",
            " '2428' '2605' '3004' '3300' '3401' '3201' '2411' '0240' '3313' '0242'\n",
            " '2503' '0261' '2901' '4601' '3102' '2060' '3601' '2501' '2405' '3110'\n",
            " '0303' '2401' '3302' '3306' '0247' '0208' '3203' '0102' '0260' '1107'\n",
            " '2404' '2001' '0302' '4002' '2902' '0204' '2433' '2301' '2403' '2406'\n",
            " '1106' '0213' '3003' '2423']\n",
            "Number of unique class is  75\n",
            "2402    5335\n",
            "3303    1778\n",
            "3101    1702\n",
            "2601    1289\n",
            "1101     730\n",
            "        ... \n",
            "0213       1\n",
            "3601       1\n",
            "2411       1\n",
            "5201       1\n",
            "4601       1\n",
            "Name: genora2, Length: 74, dtype: int64\n",
            "\n",
            "\n",
            "55 genoda1 :  object\n",
            "nan %:  9.25185608223872\n",
            "[nan '1101' '0206' '2603' '2402' '0207' '0201' '3101' '2601' '2602' '0301'\n",
            " '0203' '3303' '0302' '3001' '0101' '0210' '2404' '0102' '1102' '0202'\n",
            " '0242' '3301' '0218' '2422' '2420' '2401' '2408' '3300' '2425' '2407'\n",
            " '2605' '3103' '0259' '4601' '2501' '2101' '0208' '0224' '2406' '0260'\n",
            " '0272' '2403' '3309' '3201' '2301' '0303']\n",
            "Number of unique class is  47\n",
            "2402    4772\n",
            "0201    2550\n",
            "0206    1730\n",
            "1101    1552\n",
            "0207     680\n",
            "2601     431\n",
            "3101     269\n",
            "2603     116\n",
            "3303     115\n",
            "0101     115\n",
            "2602      94\n",
            "0301      66\n",
            "0210      64\n",
            "2420      29\n",
            "1102      24\n",
            "3001      15\n",
            "0302      14\n",
            "0218      11\n",
            "0203      11\n",
            "2401      10\n",
            "3301       4\n",
            "2403       3\n",
            "0202       3\n",
            "2408       3\n",
            "2406       3\n",
            "0242       3\n",
            "2407       2\n",
            "0102       2\n",
            "0259       2\n",
            "2605       2\n",
            "2101       2\n",
            "3103       1\n",
            "2404       1\n",
            "0260       1\n",
            "2422       1\n",
            "3201       1\n",
            "3300       1\n",
            "0272       1\n",
            "2425       1\n",
            "0303       1\n",
            "0224       1\n",
            "2501       1\n",
            "0208       1\n",
            "3309       1\n",
            "2301       1\n",
            "4601       1\n",
            "Name: genoda1, dtype: int64\n",
            "\n",
            "\n",
            "56 genoda2 :  object\n",
            "nan %:  9.27327241576242\n",
            "[nan '2402' '3101' '2601' '0206' '3303' '0207' '2603' '3103' '3201' '2602'\n",
            " '0201' '1101' '2410' '3001' '0210' '0301' '1102' '2420' '2461' '3301'\n",
            " '2401' '2422' '2408' '0203' '6601' '6801' '0101' '2424' '0402' '3004'\n",
            " '2605' '3300' '3102' '3401' '2404' '2407' '0215' '3313' '6802' '0242'\n",
            " '0261' '2901' '0601' '4601' '0302' '3601' '2801' '2405' '3110' '0303'\n",
            " '0208' '3203' '0102' '0260' '1107' '3901' '2001' '2501' '4801' '2902'\n",
            " '0204' '1106' '3002' '2403' '2301' '2406' '0240' '3302' '0218' '3003'\n",
            " '2423']\n",
            "Number of unique class is  72\n",
            "2402    5338\n",
            "3303    1806\n",
            "3101    1723\n",
            "2601    1335\n",
            "1101     653\n",
            "        ... \n",
            "3601       1\n",
            "6802       1\n",
            "3313       1\n",
            "3003       1\n",
            "4601       1\n",
            "Name: genoda2, Length: 71, dtype: int64\n",
            "\n",
            "\n",
            "57 genorb1 :  object\n",
            "nan %:  8.409480296973157\n",
            "['4001' '5401' '0702' '1501' nan '4002' '5201' '1507' '4601' '5102' '1301'\n",
            " '3501' '3901' '1511' '3904' '4006' '1518' '5901' '6701' '5101' '4403'\n",
            " '4801' '2704' '4402' '3802' '1802' '3701' '1525' '4003' '5601' '1302'\n",
            " '2705' '5502' '1528' '3902' '5801' '5010' '4802' '1702' '3905' '4050'\n",
            " '5603' '5504' '3923' '3801' '1801' '4101' '3567' '1527' '1515' '4401'\n",
            " '0801' '5001' '1502' '4007' '1401' '1521' '3503' '1535' '1510' '0270'\n",
            " '5301' '2706' '4052' '4803' '0353' '0101' '3509' '0705' '4506' '1538'\n",
            " '5512' '7020' '4060' '1307' '5202' '1517' '0601' '4072' '2703' '1508'\n",
            " '3502' '4901' '5106' '4005' '5902' '4404' '1593' '1402' '3592' '4041'\n",
            " '2901' '5403' '5107' '3510']\n",
            "Number of unique class is  95\n",
            "0702    1530\n",
            "3501    1497\n",
            "1501    1451\n",
            "4002    1077\n",
            "4001     768\n",
            "        ... \n",
            "1517       1\n",
            "2703       1\n",
            "5902       1\n",
            "1802       1\n",
            "4005       1\n",
            "Name: genorb1, Length: 94, dtype: int64\n",
            "\n",
            "\n",
            "58 genorb2 :  object\n",
            "nan %:  8.409480296973157\n",
            "['4001' '5401' '5101' '4002' nan '4601' '5901' '5801' '5201' '4006' '5502'\n",
            " '4403' '6701' '3501' '0702' '1501' '3902' '4402' '1518' '4801' '5404'\n",
            " '5601' '4003' '5603' '3701' '3904' '3901' '1402' '1527' '1511' '1507'\n",
            " '5102' '3702' '1302' '5701' '1301' '3802' '5504' '5202' '1528' '4008'\n",
            " '3543' '3505' '5251' '3526' '6707' '2704' '3923' '4040' '1502' '4054'\n",
            " '4802' '1521' '2705' '0303' '5301' '1525' '3564' '3101' '5100' '0520'\n",
            " '5106' '5010' '4050' '4614' '2501' '0801' '4493' '0701' '4506' '3503'\n",
            " '5501' '4418' '0150' '1401' '1403' '0201' '5403' '1526' '5103' '5105'\n",
            " '0102' '5818' '4005' '5402' '4501' '5550' '3502' '9502' '5116' '5001'\n",
            " '4018' '1538' '3801' '5110' '4800' '5512' '1103' '4010']\n",
            "Number of unique class is  99\n",
            "5201    2722\n",
            "5101    1419\n",
            "5401    1416\n",
            "4403    1204\n",
            "4002     887\n",
            "        ... \n",
            "4493       1\n",
            "1103       1\n",
            "2501       1\n",
            "0701       1\n",
            "4005       1\n",
            "Name: genorb2, Length: 98, dtype: int64\n",
            "\n",
            "\n",
            "59 genodb1 :  object\n",
            "nan %:  9.380354083380924\n",
            "[nan '5401' '0702' '1501' '4002' '5201' '5102' '1301' '4001' '3501' '3901'\n",
            " '1507' '1511' '3802' '4006' '1518' '4601' '5901' '4403' '4801' '5101'\n",
            " '3902' '5502' '2704' '4402' '3904' '1802' '4003' '5601' '3701' '1302'\n",
            " '2705' '3923' '1528' '6701' '1502' '4802' '5801' '1702' '3905' '5603'\n",
            " '5001' '3543' '3801' '1801' '4101' '1527' '1515' '4401' '1401' '4007'\n",
            " '1521' '5110' '3503' '1535' '5504' '0270' '5301' '2706' '0353' '0101'\n",
            " '3509' '0801' '0705' '3564' '1538' '5512' '7020' '4060' '4005' '1307'\n",
            " '5202' '4000' '0601' '1514' '1508' '3502' '4901' '5106' '1510' '5902'\n",
            " '4010' '3303' '4050' '5209' '4018' '1402' '4041' '2901' '5403' '5107'\n",
            " '3567' '5501' '3510']\n",
            "Number of unique class is  94\n",
            "0702    1540\n",
            "3501    1506\n",
            "1501    1474\n",
            "4002    1133\n",
            "5201     855\n",
            "        ... \n",
            "5403       1\n",
            "5209       1\n",
            "1802       1\n",
            "2706       1\n",
            "1401       1\n",
            "Name: genodb1, Length: 93, dtype: int64\n",
            "\n",
            "\n",
            "60 genodb2 :  object\n",
            "nan %:  9.373215305539691\n",
            "[nan '5201' '5101' '4002' '4601' '5401' '5901' '5801' '4001' '4006' '5502'\n",
            " '4403' '6701' '4801' '0702' '4402' '1518' '1501' '3501' '5601' '4003'\n",
            " '5603' '1511' '3904' '3901' '1402' '1507' '3701' '5102' '3702' '3902'\n",
            " '1302' '5701' '3802' '5504' '5202' '1301' '1528' '5251' '3526' '6707'\n",
            " '4202' '4040' '1502' '0401' '3923' '0303' '2704' '3505' '3101' '1508'\n",
            " '5100' '0520' '1510' '3551' '5106' '1525' '4050' '4803' '2501' '0801'\n",
            " '4493' '0701' '5501' '0150' '1401' '5209' '1403' '0201' '5105' '1527'\n",
            " '4005' '5402' '4501' '5550' '3502' '5001' '1538' '1801' '5110' '0460'\n",
            " '4800' '5301' '1515' '5902' '1103' '4010']\n",
            "Number of unique class is  87\n",
            "5201    2680\n",
            "5401    1669\n",
            "5101    1404\n",
            "4403     981\n",
            "4002     825\n",
            "        ... \n",
            "5110       1\n",
            "1402       1\n",
            "5100       1\n",
            "1527       1\n",
            "4005       1\n",
            "Name: genodb2, Length: 86, dtype: int64\n",
            "\n",
            "\n",
            "61 genordr1 :  object\n",
            "nan %:  7.938320959451742\n",
            "['0803' '0405' '0302' nan '0403' '0901' '1454' '0802' '1405' '0407' '1201'\n",
            " '1502' '1501' '1302' '0101' '1202' '0410' '0406' '1602' '1101' '0401'\n",
            " '1106' '1406' '0707' '1001' '0801' '1010' '1401' '1301' '1403' '1303'\n",
            " '0102' '0701' '0404' '0450' '0301' '0310' '1212' '0603' '0809' '0461'\n",
            " '1901' '1040' '0304' '0902' '1404' '1407' '1402' '1506' '096:' '0408'\n",
            " '1104' '4505' '1210' '1307' '9010' '0402' '1051' '0900' '1000' '0903'\n",
            " '1412' '1120' '1090' '0820' '1545' '0602']\n",
            "Number of unique class is  67\n",
            "0405    2672\n",
            "0901    1896\n",
            "0101    1551\n",
            "0803    1310\n",
            "0802     681\n",
            "        ... \n",
            "9010       1\n",
            "0408       1\n",
            "1303       1\n",
            "0310       1\n",
            "0603       1\n",
            "Name: genordr1, Length: 66, dtype: int64\n",
            "\n",
            "\n",
            "62 genordr2 :  object\n",
            "nan %:  7.981153626499143\n",
            "['1501' '0405' '0501' nan '0901' '1401' '1302' '1201' '1502' '1101' '1454'\n",
            " '1406' '0101' '0802' '1202' '0406' '1301' '1412' '0803' '1405' '1602'\n",
            " '1403' '0410' '0301' '0403' '1001' '0102' '0701' '0407' '0404' '0304'\n",
            " '0152' '0702' '1402' '0801' '1407' '1464' '0401' '1307' '1210' '1205'\n",
            " '0502' '1110' '1106' '0809' '096:' '1505' '1108' '1418' '1545' '0201'\n",
            " '8030' '0303' '1404' '0402' '1051' '1123' '1429' '0602' '0830' '0105'\n",
            " '1510' '1104' '1203' '0302' '0112']\n",
            "Number of unique class is  66\n",
            "1502    2827\n",
            "0901    1803\n",
            "1501    1490\n",
            "1302    1245\n",
            "0405     835\n",
            "        ... \n",
            "1404       1\n",
            "1110       1\n",
            "0105       1\n",
            "0304       1\n",
            "8030       1\n",
            "Name: genordr2, Length: 65, dtype: int64\n",
            "\n",
            "\n",
            "63 genoddr1 :  object\n",
            "nan %:  8.859223300970873\n",
            "[nan '0405' '0302' '0403' '0803' '0901' '0802' '1405' '0410' '0407' '1454'\n",
            " '1201' '1502' '1501' '0101' '1401' '1302' '0406' '1202' '0401' '1602'\n",
            " '1101' '1403' '1406' '0102' '0701' '0801' '1010' '0404' '1303' '1001'\n",
            " '1203' '0909' '0450' '1210' '0301' '0408' '0310' '1301' '1212' '0603'\n",
            " '0809' '0461' '0823' '1415' '1040' '0304' '1404' '1407' '1506' '1402'\n",
            " '1104' '1106' '0702' '1307' '1901' '0411' '0402' '1051' '4505' '1000'\n",
            " '0910' '1412' '1120' '0906' '1090' '0820' '1545' '0903' '1123' '1102']\n",
            "Number of unique class is  71\n",
            "0405    2852\n",
            "0901    1876\n",
            "0101    1546\n",
            "0803    1276\n",
            "0403     635\n",
            "        ... \n",
            "0909       1\n",
            "0408       1\n",
            "0310       1\n",
            "0461       1\n",
            "0603       1\n",
            "Name: genoddr1, Length: 70, dtype: int64\n",
            "\n",
            "\n",
            "64 genoddr2 :  object\n",
            "nan %:  8.887778412335807\n",
            "[nan '1502' '0501' '1501' '0901' '1401' '1454' '1302' '1201' '1101' '0405'\n",
            " '1403' '0101' '1405' '1202' '0803' '1301' '0403' '1406' '0406' '1602'\n",
            " '0401' '0301' '0404' '0410' '0802' '1001' '0407' '0304' '0152' '0702'\n",
            " '0801' '1303' '1402' '1464' '1407' '1307' '0701' '0502' '0102' '1110'\n",
            " '1412' '1145' '1404' '1505' '0809' '0910' '1418' '1506' '1210' '0201'\n",
            " '1600' '0822' '0830' '8030' '0303' '0902' '1429' '1051' '1119' '1123'\n",
            " '1545' '0602' '0808' '0105' '1601' '1104' '0302' '0112']\n",
            "Number of unique class is  69\n",
            "1502    2992\n",
            "0901    1863\n",
            "1501    1516\n",
            "1302    1154\n",
            "0803     834\n",
            "        ... \n",
            "0112       1\n",
            "0702       1\n",
            "0102       1\n",
            "1601       1\n",
            "8030       1\n",
            "Name: genoddr2, Length: 68, dtype: int64\n",
            "\n",
            "\n",
            "65 genorc1 :  object\n",
            "nan %:  15.005711022272985\n",
            "[nan '0102' '0702' '0303' '1202' '0302' '0304' '0801' '0401' '0501' '1402'\n",
            " '0803' '0704' '1502' '1403' '0822' '0103' '0701' '0602' '1401' '0323'\n",
            " '0405' '3030' '0329' '1002' '1201' '0901' '0403' '0802' '3501' '1102'\n",
            " '0202' '0404' '0101' '1313' '1302' '0307' '0763' '0482' '0301' '4002'\n",
            " '0727' '1001' '1203' '1204' '0201']\n",
            "Number of unique class is  46\n",
            "0102    3432\n",
            "0303    2124\n",
            "0702    1758\n",
            "0304    1655\n",
            "1202     705\n",
            "0801     634\n",
            "0401     512\n",
            "1402     282\n",
            "1403     150\n",
            "0302     105\n",
            "0602     103\n",
            "0803      92\n",
            "0704      90\n",
            "1502      81\n",
            "0103      52\n",
            "0501      51\n",
            "0822      23\n",
            "0701      13\n",
            "1201       5\n",
            "0405       4\n",
            "0802       4\n",
            "0403       3\n",
            "1002       2\n",
            "0301       2\n",
            "3030       2\n",
            "0101       2\n",
            "0323       2\n",
            "0404       1\n",
            "1001       1\n",
            "0763       1\n",
            "1302       1\n",
            "0307       1\n",
            "1203       1\n",
            "0329       1\n",
            "0482       1\n",
            "0727       1\n",
            "1401       1\n",
            "0201       1\n",
            "0202       1\n",
            "3501       1\n",
            "0901       1\n",
            "1313       1\n",
            "1102       1\n",
            "4002       1\n",
            "1204       1\n",
            "Name: genorc1, dtype: int64\n",
            "\n",
            "\n",
            "66 genorc2 :  object\n",
            "nan %:  15.05568246716162\n",
            "[nan '0102' '1402' '0304' '0303' '1510' '1502' '1202' '0702' '0401' '0801'\n",
            " '1403' '0803' '0501' '0602' '0403' '0802' '0704' '0302' '0103' '0405'\n",
            " '1501' '3040' '0822' '1203' '0701' '1201' '3303' '0193' '0706' '4006'\n",
            " '1102' '0810' '1505' '5201' '0402' '0406' '1020' '0101' '5502' '0201'\n",
            " '0202' '1601' '0120' '1204' '1413' '0404' '1408' '1001' '1302']\n",
            "Number of unique class is  50\n",
            "1202    2557\n",
            "1403    1480\n",
            "1402    1319\n",
            "0702    1279\n",
            "0304    1080\n",
            "0801    1000\n",
            "0102     815\n",
            "0303     791\n",
            "1502     537\n",
            "0401     423\n",
            "0803     203\n",
            "0704     128\n",
            "0602      83\n",
            "0302      66\n",
            "0501      32\n",
            "0822      25\n",
            "1203      16\n",
            "0103      14\n",
            "0701      10\n",
            "0403       6\n",
            "1201       3\n",
            "1501       2\n",
            "1505       2\n",
            "1204       2\n",
            "0802       2\n",
            "3303       1\n",
            "1408       1\n",
            "0706       1\n",
            "1302       1\n",
            "0405       1\n",
            "1001       1\n",
            "0193       1\n",
            "0406       1\n",
            "0404       1\n",
            "5502       1\n",
            "0101       1\n",
            "4006       1\n",
            "0201       1\n",
            "1413       1\n",
            "1601       1\n",
            "0202       1\n",
            "3040       1\n",
            "0120       1\n",
            "5201       1\n",
            "0810       1\n",
            "1510       1\n",
            "1102       1\n",
            "1020       1\n",
            "0402       1\n",
            "Name: genorc2, dtype: int64\n",
            "\n",
            "\n",
            "67 genodc1 :  object\n",
            "nan %:  17.233009708737864\n",
            "[nan '0702' '0303' '0102' '1202' '0302' '0304' '0801' '0202' '0501' '1402'\n",
            " '0401' '0803' '0704' '1403' '1502' '0701' '1203' '0602' '0103' '1401'\n",
            " '0405' '3030' '0822' '1002' '1201' '0901' '0406' '3501' '0403' '0802'\n",
            " '1313' '1302' '0301' '3004' '4002' '0340' '0101' '0727' '0903' '1204'\n",
            " '0323' '0201']\n",
            "Number of unique class is  43\n",
            "0102    3589\n",
            "0303    2107\n",
            "0304    1656\n",
            "0702    1514\n",
            "1202     719\n",
            "0801     599\n",
            "0401     481\n",
            "1402     231\n",
            "1403     137\n",
            "0302     114\n",
            "0602      94\n",
            "0803      92\n",
            "0704      83\n",
            "0501      46\n",
            "0103      44\n",
            "1502      36\n",
            "0701      11\n",
            "1201       5\n",
            "0822       3\n",
            "0405       3\n",
            "1302       2\n",
            "0403       2\n",
            "3030       2\n",
            "0101       2\n",
            "1203       2\n",
            "1002       2\n",
            "0202       2\n",
            "0802       2\n",
            "0201       1\n",
            "1401       1\n",
            "0323       1\n",
            "0903       1\n",
            "0727       1\n",
            "0901       1\n",
            "0340       1\n",
            "1313       1\n",
            "3004       1\n",
            "3501       1\n",
            "0301       1\n",
            "4002       1\n",
            "0406       1\n",
            "1204       1\n",
            "Name: genodc1, dtype: int64\n",
            "\n",
            "\n",
            "68 genodc2 :  object\n",
            "nan %:  17.275842375785267\n",
            "[nan '1402' '0304' '0102' '1202' '1510' '1502' '0803' '0303' '0702' '0801'\n",
            " '1403' '0401' '0501' '0602' '0802' '0704' '0103' '1101' '0302' '3040'\n",
            " '1501' '1203' '0323' '0701' '0403' '1454' '0901' '1201' '0706' '4006'\n",
            " '0402' '0810' '0822' '1505' '0816' '0604' '4010' '0106' '3303' '0406'\n",
            " '0142' '1020' '0101' '3004' '5502' '0201' '1601' '0120' '1435' '1413'\n",
            " '0404' '1408' '1302' '1304' '1401']\n",
            "Number of unique class is  56\n",
            "1202    2559\n",
            "1403    1474\n",
            "0702    1430\n",
            "1402    1385\n",
            "0304    1036\n",
            "0801     989\n",
            "0303     705\n",
            "0102     592\n",
            "1502     497\n",
            "0401     385\n",
            "0803     190\n",
            "0704     118\n",
            "0602      73\n",
            "0302      47\n",
            "0501      29\n",
            "1203      14\n",
            "0103       7\n",
            "0701       7\n",
            "0822       4\n",
            "1501       3\n",
            "0403       3\n",
            "0323       3\n",
            "1201       2\n",
            "0106       2\n",
            "0706       2\n",
            "0802       2\n",
            "0402       2\n",
            "1435       1\n",
            "1101       1\n",
            "1302       1\n",
            "4006       1\n",
            "3303       1\n",
            "3004       1\n",
            "0404       1\n",
            "0816       1\n",
            "1408       1\n",
            "0101       1\n",
            "1505       1\n",
            "1401       1\n",
            "1020       1\n",
            "1413       1\n",
            "4010       1\n",
            "3040       1\n",
            "0142       1\n",
            "5502       1\n",
            "1304       1\n",
            "1510       1\n",
            "0201       1\n",
            "0810       1\n",
            "0901       1\n",
            "0120       1\n",
            "1454       1\n",
            "0604       1\n",
            "1601       1\n",
            "0406       1\n",
            "Name: genodc2, dtype: int64\n",
            "\n",
            "\n",
            "69 serogvhmisa :  object\n",
            "nan %:  0.0\n",
            "['1' '0' '2']\n",
            "Number of unique class is  3\n",
            "0    13015\n",
            "1      985\n",
            "2        8\n",
            "Name: serogvhmisa, dtype: int64\n",
            "\n",
            "\n",
            "70 serogvhmisb :  object\n",
            "nan %:  0.0\n",
            "['1' '0' '2']\n",
            "Number of unique class is  3\n",
            "0    12524\n",
            "1     1463\n",
            "2       21\n",
            "Name: serogvhmisb, dtype: int64\n",
            "\n",
            "\n",
            "71 serogvhmisc :  object\n",
            "nan %:  15.4554540262707\n",
            "[nan '0' '1' '2']\n",
            "Number of unique class is  4\n",
            "0    9708\n",
            "1    2041\n",
            "2      94\n",
            "Name: serogvhmisc, dtype: int64\n",
            "\n",
            "\n",
            "72 serogvhmisdr :  object\n",
            "nan %:  0.0\n",
            "['0' '1' '2']\n",
            "Number of unique class is  3\n",
            "0    11530\n",
            "1     2468\n",
            "2       10\n",
            "Name: serogvhmisdr, dtype: int64\n",
            "\n",
            "\n",
            "73 serogvhmis6abdr :  object\n",
            "nan %:  0.0\n",
            "['2' '0' '3' '1' '4']\n",
            "Number of unique class is  5\n",
            "0    10685\n",
            "1     2098\n",
            "2      788\n",
            "3      428\n",
            "4        9\n",
            "Name: serogvhmis6abdr, dtype: int64\n",
            "\n",
            "\n",
            "74 serogvhmis8abcdr :  object\n",
            "nan %:  15.4554540262707\n",
            "[nan '0' '4' '1' '2' '3' '6' '5']\n",
            "Number of unique class is  8\n",
            "0    8044\n",
            "1    2332\n",
            "2     637\n",
            "3     519\n",
            "4     302\n",
            "5       8\n",
            "6       1\n",
            "Name: serogvhmis8abcdr, dtype: int64\n",
            "\n",
            "\n",
            "75 genogvhmisa :  object\n",
            "nan %:  9.70873786407767\n",
            "[nan '0' '1' '2']\n",
            "Number of unique class is  4\n",
            "0    11228\n",
            "1     1390\n",
            "2       30\n",
            "Name: genogvhmisa, dtype: int64\n",
            "\n",
            "\n",
            "76 genogvhmisb :  object\n",
            "nan %:  9.822958309537407\n",
            "[nan '0' '1' '2']\n",
            "Number of unique class is  4\n",
            "0    11160\n",
            "1     1424\n",
            "2       48\n",
            "Name: genogvhmisb, dtype: int64\n",
            "\n",
            "\n",
            "77 genogvhmisc :  object\n",
            "nan %:  17.72558537978298\n",
            "[nan '0' '1' '2']\n",
            "Number of unique class is  4\n",
            "0    9217\n",
            "1    2191\n",
            "2     117\n",
            "Name: genogvhmisc, dtype: int64\n",
            "\n",
            "\n",
            "78 genogvhmisdr :  object\n",
            "nan %:  9.366076527698459\n",
            "[nan '0' '1' '2']\n",
            "Number of unique class is  4\n",
            "0    9121\n",
            "1    3452\n",
            "2     123\n",
            "Name: genogvhmisdr, dtype: int64\n",
            "\n",
            "\n",
            "79 genogvhmis6abdr :  object\n",
            "nan %:  9.987150199885779\n",
            "[nan '0' '1' '3' '2' '4' '5' '6']\n",
            "Number of unique class is  8\n",
            "0    8251\n",
            "1    2864\n",
            "2     766\n",
            "3     671\n",
            "4      50\n",
            "5       6\n",
            "6       1\n",
            "Name: genogvhmis6abdr, dtype: int64\n",
            "\n",
            "\n",
            "80 genogvhmis8abcdr :  object\n",
            "nan %:  18.046830382638493\n",
            "[nan '0' '1' '4' '2' '3' '5' '6' '7']\n",
            "Number of unique class is  9\n",
            "0    6739\n",
            "1    2833\n",
            "2     803\n",
            "3     555\n",
            "4     484\n",
            "5      56\n",
            "6       8\n",
            "7       2\n",
            "Name: genogvhmis8abcdr, dtype: int64\n",
            "\n",
            "\n",
            "81 cond_regimen :  object\n",
            "nan %:  0.028555111364934323\n",
            "['+mel+flu' '+cy' '+cy+bu_iv' '+mel+flu+dexa' '+flu+bu_iv' '+flu'\n",
            " '+cy+flu' '+mel' '+cy+vp16' '+cy+ca' '+mel+flu+bu_iv' '+mel+flu+bu_iv+ca'\n",
            " '+mel+vp16+flu+bu_iv' '+cy+bu_oral' '+mel+flu+ca' '+flu+bu_iv+ca'\n",
            " '+flu+bu_oral' '+cy+flu+ca' '+cy+ca+rituximab' '+ca' '+cy+flu+rituximab'\n",
            " '+vp16+flu+bu_iv+ca' '+mel+vp16+flu+ca' '+flu+ca' '+cy+bu_iv+gemtuzumab'\n",
            " '+cy+bu_iv+ca' '+cy+flu+campath' '+bu_oral+bu_iv' '+mel+vp16+flu'\n",
            " '+cy+ca+ida' '+cy+vp16+flu' '+mel+flu+bu_oral' '+mel+flu+acnu'\n",
            " '+mel+flu+rituximab' '+cy+bu_iv+rituximab' '+cy+vp16+bu_iv+ca+mit'\n",
            " '+vp16+flu' '+vp16+flu+bu_iv' '+cy+flu+bu_iv' '+mel+flu+campath'\n",
            " '+mel+ca' '+vp16+ca' '+cy+vp16+bu_iv+mit' '+flu+bu_iv+dexa' nan '+vp16'\n",
            " '+bu_oral' '+mel+bu_iv' '+cy+mel+flu' '+cy+vp16+bu_iv' '+mel+vp16'\n",
            " '+cy+dexa' '+mel+mcnu' '+mel+vp16+flu+ca+mcnu' '+mel+flu+ca+rituximab'\n",
            " '+cy+campath' '+flu+bu_oral+ca' '+cy+vp16+dexa' '+flu+bu_iv+campath'\n",
            " '+flu+bu_oral+ca+imatinib' '+cy+vp16+flu+bu_iv' '+flu+bu_iv+mit'\n",
            " '+mel+flu+adr' '+cy+mel+flu+bu_iv' '+cy+flu+bu_iv+ca' '+flu+rituximab'\n",
            " '+cy+flu+dexa' '+mel+bu_oral+bu_iv' '+mel+flu+bu_iv+dexa'\n",
            " '+cy+mel+flu+ca' '+cy+vp16+vcr' '+mel+vp16+flu+ca+mcnu+rituximab'\n",
            " '+flu+mcnu' '+cy+vp16+ca+mit' '+vp16+flu+ca+mcnu+cbdca'\n",
            " '+mel+flu+ca+mit+rituximab' '+mel+vp16+ca+mcnu+rituximab'\n",
            " '+flu+bu_iv+ca+gemtuzumab' '+flu+bu_iv+ida' '+vp16+flu+ca'\n",
            " '+cy+flu+ca+rituximab' '+flu+bu_iv+rituximab' '+cy+mel+vp16'\n",
            " '+cy+ca+dexa' '+cy+flu+thiotepa' '+cy+flu+bcnu' '+cy+ida' '+cy+mel'\n",
            " '+mel+flu+ca+mit' '+cy+mcnu' '+bu_iv' '+mel+flu+bu_iv+rituximab'\n",
            " '+cy+bu_iv+mcnu' '+flu+bu_oral+rituximab' '+vp16+bu_iv' '+cy+gemtuzumab'\n",
            " '+cy+vp16+ca' '+cy+mel+bu_iv' '+cy+vp16+mcnu+cbdca' '+cy+thiotepa'\n",
            " '+mel+flu+ida' '+mel+vp16+bu_iv' '+cy+bu_iv+dexa' '+mel+bu_iv+ca'\n",
            " '+cy+mel+vp16+flu+bu_oral+ca+adr+vcr' '+cy+flu+bu_iv+ca+adr+dexa+vcr'\n",
            " '+flu+cbdca' '+cy+vp16+rituximab' '+cy+bu_iv+bcnu' '+mel+flu+gemtuzumab'\n",
            " '+cy+rituximab' '+mel+flu+mit' '+vp16+flu+cbdca+ifo' '+cy+flu+ca+mit'\n",
            " '+cy+bu_oral+ca' '+cy+vp16+flu+adr+rituximab+vcr' '+mel+flu+bu_iv+ca+mit'\n",
            " '+cy+bu_iv+adr' '+cy+bu_iv+ca+mcnu' '+flu+ca+rituximab'\n",
            " '+mel+vp16+flu+bu_iv+ca' '+flu+bu_iv+gemtuzumab']\n",
            "Number of unique class is  122\n",
            "\n",
            "\n",
            "82 hctci_high :  object\n",
            "nan %:  0.6710451170759566\n",
            "['3-' '-2' nan]\n",
            "Number of unique class is  3\n",
            "-2    12048\n",
            "3-     1866\n",
            "Name: hctci_high, dtype: int64\n",
            "\n",
            "\n",
            "83 gvhd_pro3 :  object\n",
            "nan %:  0.0\n",
            "['Tac+MTX' 'CyA+MTX' 'Tac+MMF' 'Tac+_' 'CyA+_' 'CyA+MMF']\n",
            "Number of unique class is  6\n",
            "Tac+MTX    7935\n",
            "CyA+MTX    4245\n",
            "Tac+MMF     837\n",
            "Tac+_       667\n",
            "CyA+MMF     172\n",
            "CyA+_       152\n",
            "Name: gvhd_pro3, dtype: int64\n",
            "\n",
            "\n",
            "84 MLevent_agvhd24 :  object\n",
            "nan %:  0.0\n",
            "['Y' 'N']\n",
            "Number of unique class is  2\n",
            "N    8265\n",
            "Y    5743\n",
            "Name: MLevent_agvhd24, dtype: int64\n",
            "\n",
            "\n",
            "85 MLevent_agvhd34 :  object\n",
            "nan %:  0.0\n",
            "['Y' 'N']\n",
            "Number of unique class is  2\n",
            "N    11861\n",
            "Y     2147\n",
            "Name: MLevent_agvhd34, dtype: int64\n",
            "\n",
            "\n",
            "86 bmi2 :  object\n",
            "nan %:  0.0\n",
            "['<22' '>=22']\n",
            "Number of unique class is  2\n",
            "<22     7769\n",
            ">=22    6239\n",
            "Name: bmi2, dtype: int64\n",
            "\n",
            "\n",
            "87 bm_tnc_total_harvest_kg :  float64\n",
            "nan %:  43.418046830382636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "88 pb_tnc_total_cryop_kg :  float64\n",
            "nan %:  95.24557395773844\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "89 pb_cd34_total_kg :  float64\n",
            "nan %:  78.04111936036551\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "90 ks_tx2 :  float64\n",
            "nan %:  0.23557966876070818\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "91 diagnosis3 :  object\n",
            "nan %:  0.0\n",
            "['AML/MDS' 'ALL' 'ATL' 'others' 'NHL' 'CML' 'AA']\n",
            "Number of unique class is  7\n",
            "AML/MDS    7841\n",
            "ALL        2835\n",
            "NHL        1030\n",
            "others      826\n",
            "ATL         722\n",
            "AA          385\n",
            "CML         369\n",
            "Name: diagnosis3, dtype: int64\n",
            "\n",
            "\n",
            "92 stage4 :  object\n",
            "nan %:  0.0\n",
            "['advanced' 'standard']\n",
            "Number of unique class is  2\n",
            "standard    8045\n",
            "advanced    5963\n",
            "Name: stage4, dtype: int64\n",
            "\n",
            "\n",
            "93 ric_mac_tbi :  object\n",
            "nan %:  0.0\n",
            "['RICwTBI' 'MACwTBI' 'MACwoTBI' 'RICwoTBI']\n",
            "Number of unique class is  4\n",
            "MACwTBI     6316\n",
            "RICwTBI     3081\n",
            "MACwoTBI    2884\n",
            "RICwoTBI    1727\n",
            "Name: ric_mac_tbi, dtype: int64\n",
            "\n",
            "\n",
            "94 MLevent_agvhd24_manip :  object\n",
            "nan %:  0.0\n",
            "['1' '0']\n",
            "Number of unique class is  2\n",
            "0    8265\n",
            "1    5743\n",
            "Name: MLevent_agvhd24_manip, dtype: int64\n",
            "\n",
            "\n",
            "95 MLevent_agvhd34_manip :  object\n",
            "nan %:  0.0\n",
            "['1' '0']\n",
            "Number of unique class is  2\n",
            "0    11861\n",
            "1     2147\n",
            "Name: MLevent_agvhd34_manip, dtype: int64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RR5aK1oGgb2T"
      },
      "source": [
        "# Make new features\n",
        "df_cleaned = df_cleaned_raw.copy()\n",
        "df_cleaned.loc[:,'MLevent_agvhd2434'] = df_cleaned.loc[:,'MLevent_agvhd24'] + df_cleaned.loc[:,'MLevent_agvhd34'] \n",
        "\n",
        "# \n",
        "df_cleaned['bm_tnc_total_harvest_kg_bool'] = (df_cleaned['bm_tnc_total_harvest_kg']>0.05).astype(str)\n",
        "df_cleaned['pb_tnc_total_cryop_kg_bool'] = df_cleaned['pb_tnc_total_cryop_kg'].isnull().astype(str)\n",
        "df_cleaned['pb_cd34_total_kg_bool'] = df_cleaned['pb_cd34_total_kg'].isnull().astype(str)\n",
        "\n",
        "# Feature \n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Bucket\n",
        "bucket_columns = {\n",
        "    'hctci_points_corr': [2,4,6,8,1000],\n",
        "    'ks_tx2': [20,40,60,80,1000],\n",
        "    \"do_age\": [10,20,30,40,50,60,70,80,90,1000],\n",
        "    \"dx_to_sct\": [7,30,90,180,360,1000,10000],\n",
        "    'tyear': [2004,2006,2008,2010,2012,2014],\n",
        "    'pt_age':   [10,20,30,40,50,60,70,80,90]\n",
        "}\n",
        "\n",
        "# Make independent features tablue\n",
        "onehot_variables = sorted([\n",
        "    'do_sex',\n",
        "    'pt_sex',\n",
        "    'abo_mismatch',\n",
        "    'tbi',\n",
        "    'ps24',\n",
        "    'ps_tx',\n",
        "    'plt_transfusion',\n",
        "    'rbc_transfusion',\n",
        "    'dx_to_sct_group',\n",
        "    'hctci_high',\n",
        "    'bmi2',\n",
        "    'bm_tnc_total_harvest_kg_bool',\n",
        "    'pb_tnc_total_cryop_kg_bool',\n",
        "    'pb_cd34_total_kg_bool',\n",
        "    'pt_abo',\n",
        "    'do_abo',\n",
        "    'sct_type',\n",
        "    'gvhd_pro',\n",
        "    'gvhd_pro2',\n",
        "    'gvhd_pro3',\n",
        "    'cond_regimen',\n",
        "    'stage4',\n",
        "    'diagnosis3',\n",
        "    'ric_mac_tbi',\n",
        "])\n",
        "\n",
        "# embeded_variables = sorted([\n",
        "#     'pt_abo',\n",
        "#     'do_abo',\n",
        "#     'sct_type',\n",
        "#     'gvhd_pro',\n",
        "#     'gvhd_pro2',\n",
        "#     'gvhd_pro3',\n",
        "#     'cond_regimen',\n",
        "#     'stage4',\n",
        "#     'diagnosis3',\n",
        "#     'ric_mac_tbi',\n",
        "# ])\n",
        "\n",
        "hctci_onehot_variables = sorted([\n",
        "    'hctci_arrythmia_cd',\n",
        "    'hctci_heart_cd',\n",
        "    'hctci_cerebrovascular_cd',\n",
        "    'hctci_dm_cd',\n",
        "    'hctci_valve_cd',\n",
        "    'hctci_liver_cd',\n",
        "    'hctci_liver_severe_cd',\n",
        "    'hctci_inf_cd',\n",
        "    'hctci_bowel_cd',\n",
        "    'hctci_obesity_cd',\n",
        "    'hctci_ulcer_cd',\n",
        "    'hctci_mental_cd',\n",
        "    'hctci_lung_cd',\n",
        "    'hctci_lung_severe_cd',\n",
        "    'hctci_renal_cd',\n",
        "    'hctci_rhumatoid_cd',\n",
        "    'hctci_st_cd',\n",
        "])\n",
        "\n",
        "serogeno_onehot_variables = sorted([\n",
        "    'serogvhmisa',\n",
        "    'serogvhmisb',\n",
        "    'serogvhmisc',\n",
        "    'serogvhmisdr',\n",
        "    'serogvhmis6abdr',\n",
        "    'serogvhmis8abcdr',\n",
        "    'genogvhmisa',\n",
        "    'genogvhmisb',\n",
        "    'genogvhmisc',\n",
        "    'genogvhmisdr',\n",
        "    'genogvhmis6abdr',\n",
        "    'genogvhmis8abcdr',\n",
        "])\n",
        "\n",
        "serogeno_embeded_variables = [\n",
        "        'genora1','genora2',\n",
        "        'genoda1', 'genoda2',\n",
        "        'genorb1', 'genorb2',\n",
        "        'genodb1', 'genodb2',\n",
        "        'genordr1', 'genordr2',\n",
        "        'genoddr1', 'genoddr2',\n",
        "        'genorc1', 'genorc2',\n",
        "        'genodc1', 'genodc2',\n",
        "        'serora1', 'serora2',\n",
        "        'seroda1', 'seroda2',\n",
        "        'serorb1', 'serorb2',\n",
        "        'serodb1', 'serodb2',\n",
        "        'serorc1', 'serorc2',\n",
        "        'serodc1', 'serodc2',\n",
        "        'serordr1', 'serordr2',\n",
        "        'seroddr1', 'seroddr2'\n",
        "]\n",
        "\n",
        "shared_embeded_variables = [\n",
        "        ['genora1', 'genora2', 'genoda1', 'genoda2'],\n",
        "        ['genorb1', 'genorb2', 'genodb1', 'genodb2'],\n",
        "        ['genordr1', 'genordr2','genoddr1', 'genoddr2'],\n",
        "        ['genorc1', 'genorc2', 'genodc1', 'genodc2'],\n",
        "        ['serora1', 'serora2', 'seroda1', 'seroda2'],\n",
        "        ['serorb1', 'serorb2', 'serodb1', 'serodb2'],\n",
        "        ['serorc1', 'serorc2', 'serodc1', 'serodc2'],\n",
        "        ['serordr1', 'serordr2', 'seroddr1', 'seroddr2']\n",
        "]\n",
        "\n",
        "\n",
        "# # Minor class as others\n",
        "# for c1 in embeded_variables:\n",
        "#   column_name = c1\n",
        "#   # print(df_cleaned.loc[:,column_name].value_counts())\n",
        "#   unique_column = df_cleaned.loc[:,column_name].unique()\n",
        "#   for c in unique_column:\n",
        "#     target_index = df_cleaned.index[df_cleaned.loc[:,column_name]==c]\n",
        "#     if len(\n",
        "#       target_index)/df_cleaned.shape[0] < 0.01:\n",
        "#       df_cleaned.loc[target_index,column_name] = 'others'\n",
        "\n",
        "# Minor class as others\n",
        "for c1 in serogeno_embeded_variables:\n",
        "  column_name = c1\n",
        "  # print(df_cleaned.loc[:,column_name].value_counts())\n",
        "  unique_column = df_cleaned.loc[:,column_name].unique()\n",
        "  for c in unique_column:\n",
        "    target_index = df_cleaned.index[df_cleaned.loc[:,column_name]==c]\n",
        "    if len(target_index)/df_cleaned.shape[0] < 0.01:\n",
        "      df_cleaned.loc[target_index,column_name] = 'others'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aZmixtlhtWA",
        "outputId": "b0373353-dec3-4698-8198-37f128e4fafb"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split parameter\n",
        "TRAINING_RATIO = 0.8\n",
        "TEST_RATIO = 1.0 - TRAINING_RATIO\n",
        "VALIDATION_RATIO = 1.0 - TRAINING_RATIO\n",
        "\n",
        "# Deterministic, no need to take care of random seeed\n",
        "df_cleaned_cp = df_cleaned.copy()\n",
        "\n",
        "# Time series split\n",
        "df_cleaned_train_tmp = df_cleaned_cp.drop(df_cleaned_cp.index[df_cleaned_cp['tyear']>df_cleaned_cp['tyear'].quantile(TRAINING_RATIO)].values,axis=0)\n",
        "df_cleaned_test = df_cleaned_cp.drop(df_cleaned_cp.index[df_cleaned_cp['tyear']<=df_cleaned_cp['tyear'].quantile(TRAINING_RATIO)].values,axis=0)\n",
        "df_cleaned_train = df_cleaned_train_tmp.drop(df_cleaned_train_tmp.index[df_cleaned_train_tmp['tyear']>df_cleaned_train_tmp['tyear'].quantile(TRAINING_RATIO)].values,axis=0)\n",
        "df_cleaned_valid = df_cleaned_train_tmp.drop(df_cleaned_train_tmp.index[df_cleaned_train_tmp['tyear']<=df_cleaned_train_tmp['tyear'].quantile(TRAINING_RATIO)].values,axis=0)\n",
        "\n",
        "# Random split\n",
        "train_index, test_index = train_test_split(df_cleaned_cp.index, test_size=TEST_RATIO, random_state=0)\n",
        "train_index, valid_index = train_test_split(train_index, test_size=VALIDATION_RATIO, random_state=0)\n",
        "df_cleaned_train = df_cleaned_cp.loc[train_index,:]\n",
        "df_cleaned_valid = df_cleaned_cp.loc[valid_index,:]\n",
        "df_cleaned_test = df_cleaned_cp.loc[test_index,:]\n",
        "\n",
        "# Change tyear in valid/test data to maximum value in training\n",
        "df_cleaned_test.loc[:,'tyear'] = df_cleaned_train.loc[:,'tyear'].max()\n",
        "df_cleaned_valid.loc[:,'tyear'] = df_cleaned_train.loc[:,'tyear'].max()\n",
        "\n",
        "# # Classes that are not in training is treated as others\n",
        "# for c1 in embeded_variables:\n",
        "#   column_name = c1\n",
        "#   unique_column_train = df_cleaned_train.loc[:,column_name].unique()\n",
        "#   unique_column_valid = df_cleaned_valid.loc[:,column_name].unique()\n",
        "#   unique_column_test = df_cleaned_test.loc[:,column_name].unique()\n",
        "#   for c in set(unique_column_valid).difference(set(unique_column_train)):\n",
        "#     target_index = df_cleaned_valid.index[df_cleaned_valid.loc[:,column_name]==c]\n",
        "#     df_cleaned_valid.loc[target_index,column_name] = 'others'\n",
        "#   for c in set(unique_column_test).difference(set(unique_column_train)):\n",
        "#     target_index = df_cleaned_test.index[df_cleaned_test.loc[:,column_name]==c]\n",
        "#     df_cleaned_test.loc[target_index,column_name] = 'others'\n",
        "\n",
        "# Classes that are not in training is treated as others\n",
        "for c1 in onehot_variables:\n",
        "  column_name = c1\n",
        "  unique_column_train = df_cleaned_train.loc[:,column_name].unique()\n",
        "  unique_column_valid = df_cleaned_valid.loc[:,column_name].unique()\n",
        "  unique_column_test = df_cleaned_test.loc[:,column_name].unique()\n",
        "  for c in set(unique_column_valid).difference(set(unique_column_train)):\n",
        "    target_index = df_cleaned_valid.index[df_cleaned_valid.loc[:,column_name]==c]\n",
        "    df_cleaned_valid.loc[target_index,column_name] = 'others'\n",
        "  for c in set(unique_column_test).difference(set(unique_column_train)):\n",
        "    target_index = df_cleaned_test.index[df_cleaned_test.loc[:,column_name]==c]\n",
        "    df_cleaned_test.loc[target_index,column_name] = 'others'\n",
        "\n",
        "# Classes that are not in training is treated as others\n",
        "for c1 in serogeno_embeded_variables:\n",
        "  column_name = c1\n",
        "  unique_column_train = df_cleaned_train.loc[:,column_name].unique()\n",
        "  unique_column_valid = df_cleaned_valid.loc[:,column_name].unique()\n",
        "  unique_column_test = df_cleaned_test.loc[:,column_name].unique()\n",
        "  for c in set(unique_column_valid).difference(set(unique_column_train)):\n",
        "    target_index = df_cleaned_valid.index[df_cleaned_valid.loc[:,column_name]==c]\n",
        "    df_cleaned_valid.loc[target_index,column_name] = 'others'\n",
        "  for c in set(unique_column_test).difference(set(unique_column_train)):\n",
        "    target_index = df_cleaned_test.index[df_cleaned_test.loc[:,column_name]==c]\n",
        "    df_cleaned_test.loc[target_index,column_name] = 'others'\n",
        "\n",
        "df_cleaned_train.shape,df_cleaned_valid.shape,df_cleaned_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8964, 100), (2242, 100), (2802, 100))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Mi51G2liF72"
      },
      "source": [
        "# Label Encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8a2tZ_oiDbZ"
      },
      "source": [
        "import json\n",
        "import sklearn\n",
        "\n",
        "# Label Encoding\n",
        "original = {}\n",
        "categorical_names = {}\n",
        "for c1,c2,c3,c4 in shared_embeded_variables:\n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c1])\n",
        "    df_cleaned.loc[:, c1] = le.transform(df_cleaned.loc[:, c1])\n",
        "    original[c1] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c1] = le.classes_.astype(str)\n",
        "    \n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c2])\n",
        "    df_cleaned.loc[:, c2] = le.transform(df_cleaned.loc[:, c2])\n",
        "    original[c2] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c2] = le.classes_.astype(str)\n",
        "    \n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c3])\n",
        "    df_cleaned.loc[:, c3] = le.transform(df_cleaned.loc[:, c3])\n",
        "    original[c3] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c3] = le.classes_.astype(str)\n",
        "    \n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c4])\n",
        "    df_cleaned.loc[:, c4] = le.transform(df_cleaned.loc[:, c4])\n",
        "    original[c4] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c4] = le.classes_.astype(str)\n",
        "\n",
        "for c1 in serogeno_onehot_variables:\n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c1])\n",
        "    df_cleaned.loc[:, c1] = le.transform(df_cleaned.loc[:, c1])\n",
        "    original[c1] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c1] = le.classes_.astype(str)\n",
        "    \n",
        "for c1 in onehot_variables:\n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c1])\n",
        "    df_cleaned.loc[:, c1] = le.transform(df_cleaned.loc[:, c1])\n",
        "    original[c1] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c1] = le.classes_.astype(str)\n",
        "    \n",
        "# for c1 in embeded_variables:\n",
        "#     le = sklearn.preprocessing.LabelEncoder()\n",
        "#     le.fit(df_cleaned.loc[:,c1])\n",
        "#     df_cleaned.loc[:, c1] = le.transform(df_cleaned.loc[:, c1])\n",
        "#     original[c1] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "#     categorical_names[c1] = le.classes_.astype(str)\n",
        "\n",
        "for c1 in hctci_onehot_variables:\n",
        "    le = sklearn.preprocessing.LabelEncoder()\n",
        "    le.fit(df_cleaned.loc[:,c1])\n",
        "    df_cleaned.loc[:, c1] = le.transform(df_cleaned.loc[:, c1])\n",
        "    original[c1] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "    categorical_names[c1] = le.classes_.astype(str)\n",
        "    \n",
        "# for c1 in list(bucket_columns.keys()):\n",
        "#     original[c1] = dict(zip(le.classes_,[i for i in range(len(le.classes_))]))\n",
        "#     categorical_names[c1] = le.classes_.astype(str)\n",
        "    \n",
        "# Split\n",
        "df_cleaned_train_index = df_cleaned_train.index\n",
        "df_cleaned_valid_index = df_cleaned_valid.index\n",
        "df_cleaned_test_index = df_cleaned_test.index\n",
        "df_cleaned_train = df_cleaned.loc[df_cleaned_train_index,:]\n",
        "df_cleaned_valid = df_cleaned.loc[df_cleaned_valid_index,:]\n",
        "df_cleaned_test = df_cleaned.loc[df_cleaned_test_index,:]\n",
        "\n",
        "# print(json.dump(original,f, indent=4))\n",
        "with open('original-large.json', 'w') as f:\n",
        "    json.dump(original,f, indent=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj4CUtG1iKy3"
      },
      "source": [
        "with open('original-large-random.json', 'w') as f:\n",
        "    json.dump(original,f, indent=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEJR0ERoiMiR",
        "outputId": "a478bcd3-7b42-4907-f1e9-093e937f555f"
      },
      "source": [
        "feature_columns = []\n",
        "feature_layer_inputs = {}\n",
        "\n",
        "# Bucketized\n",
        "print('Number of bucketized: ',len(bucket_columns))\n",
        "for header in bucket_columns:\n",
        "  numeric = tf.feature_column.numeric_column(header)\n",
        "  buckets = tf.feature_column.bucketized_column(numeric, boundaries=bucket_columns[header])\n",
        "  feature_columns.append(buckets)\n",
        "  feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header)\n",
        "\n",
        "# Onehot\n",
        "print('Number of onehot: ',len(onehot_variables))\n",
        "for header in onehot_variables:\n",
        "  tmp = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      header, list(df_cleaned.loc[:,header].unique()))\n",
        "  one_hot = tf.feature_column.indicator_column(tmp)\n",
        "  feature_columns.append(one_hot)\n",
        "  feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header, dtype=tf.int16)\n",
        "    \n",
        "# serogeno Onehot\n",
        "serogeno_feature_columns = []\n",
        "serogeno_feature_layer_inputs = {}\n",
        "print('Number of serogenoonehot: ',len(serogeno_onehot_variables))\n",
        "for header in serogeno_onehot_variables:\n",
        "  tmp = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      header, list(df_cleaned.loc[:,header].unique()))\n",
        "  one_hot = tf.feature_column.indicator_column(tmp)\n",
        "  feature_columns.append(one_hot)\n",
        "  feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header, dtype=tf.int16)\n",
        "\n",
        "# # Embedding\n",
        "# embed_feature_columns = []\n",
        "# embed_feature_layer_inputs = {}\n",
        "# print('Number of embedding: ',len(embeded_variables))\n",
        "# for header in embeded_variables:\n",
        "#   tmp = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "#       header, sorted(list(df_cleaned.loc[:,header].unique())))\n",
        "#   embedding = tf.feature_column.embedding_column(tmp, dimension=int(len(list(df_cleaned.loc[:,header].unique()))**0.25))\n",
        "#   embed_feature_columns.append(embedding)\n",
        "#   embed_feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header, dtype=tf.int16)\n",
        "\n",
        "hctci_feature_columns = []\n",
        "hctci_feature_layer_inputs = {}\n",
        "\n",
        "#  hctci_onehot_variables\n",
        "print('Number of hctci_onehot_variables: ',len(hctci_onehot_variables))\n",
        "for header in hctci_onehot_variables:\n",
        "  tmp = tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "      header, list(df_cleaned.loc[:,header].unique()))\n",
        "  one_hot = tf.feature_column.indicator_column(tmp)\n",
        "  feature_columns.append(one_hot)\n",
        "  feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header, dtype=tf.int16)\n",
        "\n",
        "# Shared Embedding\n",
        "shared_embedding_columns = []\n",
        "shared_layer_inputs = {}\n",
        "print('Number of shared embedding: ',len(shared_embeded_variables))\n",
        "for header1,header2,header3,header4 in shared_embeded_variables:\n",
        "  input1 = tf.keras.Input(shape=(1,), name=header1,dtype=tf.int16)\n",
        "  input2 = tf.keras.Input(shape=(1,), name=header2,dtype=tf.int16)\n",
        "  input3 = tf.keras.Input(shape=(1,), name=header3,dtype=tf.int16)\n",
        "  input4 = tf.keras.Input(shape=(1,), name=header4,dtype=tf.int16)\n",
        "  unique_column = list(set(df_cleaned.loc[:,header1].unique()) | set(df_cleaned.loc[:,header2].unique()) | set(df_cleaned.loc[:,header3].unique()) | set(df_cleaned.loc[:,header4].unique()))\n",
        "  print(len(unique_column)+1,int(len(unique_column)**0.5),'-'.join([header1,header2]))\n",
        "  embedding_layer = tf.keras.layers.Embedding(len(unique_column)+1,int(len(unique_column)**0.5),name='-'.join([header1,header2,header3,header4 ]))\n",
        "  embedding_1 = embedding_layer(input1)\n",
        "  embedding_2 = embedding_layer(input2)\n",
        "  embedding_3 = embedding_layer(input3)\n",
        "  embedding_4 = embedding_layer(input4)\n",
        "  flatten_1 = tf.keras.layers.Flatten(name='flatten-'+header1)(embedding_1)\n",
        "  flatten_2 = tf.keras.layers.Flatten(name='flatten-'+header2)(embedding_2)\n",
        "  flatten_3 = tf.keras.layers.Flatten(name='flatten-'+header3)(embedding_3)\n",
        "  flatten_4 = tf.keras.layers.Flatten(name='flatten-'+header4)(embedding_4)\n",
        "  x = tf.keras.layers.Concatenate(axis=1,name='concat-'+'-'.join([header1,header2,header3,header4 ]))([flatten_1,flatten_2,flatten_3,flatten_4])\n",
        "#   x = tf.keras.layers.Dense(16, activation='relu',name='dense-'+'-'.join([header1,header2,header3,header4 ]))(concat)\n",
        "#   x = tf.keras.layers.BatchNormalization()(x)\n",
        "#   x = tf.keras.layers.Dense(32, activation='relu',name='dense2-'+'-'.join([header1,header2,header3,header4 ]))(x)\n",
        "#   x = tf.keras.layers.BatchNormalization()(x)\n",
        "#   x = tf.keras.layers.Dense(32, activation='relu',name='dense3-'+'-'.join([header1,header2,header3,header4 ]))(x)\n",
        "#   x = tf.keras.layers.BatchNormalization()(x)\n",
        "#   x = tf.keras.layers.Dropout(0.2)(x)\n",
        "  shared_embedding_columns.append(x)\n",
        "\n",
        "  shared_layer_inputs[header1] = input1\n",
        "  shared_layer_inputs[header2] = input2\n",
        "  shared_layer_inputs[header3] = input3\n",
        "  shared_layer_inputs[header4] = input4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of bucketized:  6\n",
            "Number of onehot:  24\n",
            "Number of serogenoonehot:  12\n",
            "Number of hctci_onehot_variables:  17\n",
            "Number of shared embedding:  8\n",
            "13 3 genora1-genora2\n",
            "20 4 genorb1-genorb2\n",
            "20 4 genordr1-genordr2\n",
            "14 3 genorc1-genorc2\n",
            "8 2 serora1-serora2\n",
            "20 4 serorb1-serorb2\n",
            "13 3 serorc1-serorc2\n",
            "11 3 serordr1-serordr2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OpuS6zPliS5H"
      },
      "source": [
        "# Make dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "vPzj7zcViNyM",
        "outputId": "82aad7b5-e7e3-45ef-f77e-ea63cc9745bc"
      },
      "source": [
        "import itertools\n",
        "\n",
        "# generatof\n",
        "def gen(df,label_name,drops,batch_size=32,mask=False,shuffle=False,label_smoothing=False):\n",
        "  \n",
        "  while True:\n",
        "    df_gen = df.copy()\n",
        "\n",
        "    if shuffle:\n",
        "      print('Shuffle!')\n",
        "      df_gen = df_gen.sample(frac=1)\n",
        "    labels = my_utility.df_category_to_onehot(df_gen[[label_name]],label_name)\n",
        "    if label_smoothing:\n",
        "      print(labels.shape)\n",
        "      labels = labels-0.1+(0.1/labels.shape[1])\n",
        "\n",
        "    if mask:\n",
        "      comb = list(itertools.product(list(df_gen.index),df_gen.columns))\n",
        "      comb = np.array([list(comb[i]) for i in np.random.choice(len(comb),int(df_gen.shape[1]*df_gen.shape[0]*0.5),replace=False)])\n",
        "      u_column = np.unique(comb[:,1])\n",
        "      for c in u_column:\n",
        "        inds = comb[comb[:,1]==c,0]\n",
        "        if not type(df_gen.loc[inds.astype(int)[0],c])==type(1):\n",
        "          df_gen.loc[inds.astype(int),c] = 'nan' if type(df_gen.loc[inds.astype(int)[0],c])==type(\"\") else np.nan\n",
        "    for i in zip(*[iter(list(df_gen.index))]*batch_size):\n",
        "      yield dict([(k,tf.convert_to_tensor(v)) for k,v in dict(df_gen.loc[list(i)]).items()]),tf.convert_to_tensor(labels.loc[list(i)].values)\n",
        "\n",
        "# Make dataset\n",
        "# 0: N, 1: Y\n",
        "batch_size = 32\n",
        "label_name = 'MLevent_agvhd24'\n",
        "drops = ['tid', 'MLevent_agvhd24', 'MLevent_agvhd34', 'MLevent_agvhd2434','MLevent_agvhd24_manip', 'MLevent_agvhd34_manip']\n",
        "\n",
        "train_ds =gen(df_cleaned_train,label_name,drops,batch_size=batch_size,mask=False,shuffle=False,label_smoothing=True)\n",
        "valid_ds = gen(df_cleaned_valid,label_name,drops,batch_size=batch_size)\n",
        "test_ds = gen(df_cleaned_test,label_name,drops,batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-389c634e8028>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mdrops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'tid'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MLevent_agvhd24'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MLevent_agvhd34'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MLevent_agvhd2434'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'MLevent_agvhd24_manip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'MLevent_agvhd34_manip'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0mtrain_ds\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_cleaned_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel_smoothing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0mvalid_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_cleaned_valid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0mtest_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_cleaned_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df_cleaned_train' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_jxK1-sXBld"
      },
      "source": [
        "for t in train_ds:\n",
        "  break"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCiB9AGRieKT"
      },
      "source": [
        "# Define model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JlvTwV3ddvZ",
        "outputId": "4e496553-b26d-4f02-a0ac-8543ede74721"
      },
      "source": [
        "# bottleneck resnet\n",
        "def resnext_bottleneck(filters,kernel_size,stride,do_rate,x_in,name):\n",
        "  filters1, filters2, filters3 = filters\n",
        "\n",
        "  x = tf.keras.layers.Conv1D(filters1,1,padding='same',name=name+'-conv1')(x_in)\n",
        "  x = tf.keras.layers.BatchNormalization(name=name+\"-bn1\")(x)\n",
        "  x = tf.keras.layers.Activation('relu',name=name+\"-relu1\")(x)\n",
        "  x = tf.keras.layers.Conv1D(filters2,kernel_size,stride,padding='same',name=name+'-conv2')(x)\n",
        "  x = tf.keras.layers.BatchNormalization(name=name+\"-bn2\")(x)\n",
        "  x = tf.keras.layers.Activation('relu',name=name+\"-relu2\")(x)\n",
        "  x = tf.keras.layers.Conv1D(filters3,1,padding='same',name=name+'-conv3')(x)\n",
        "  x = tf.keras.layers.BatchNormalization(name=name+\"-bn3\")(x)\n",
        "\n",
        "  shortcut = tf.keras.layers.Conv1D(filters3,1,stride,padding='same',name=name+'-convS')(x_in)\n",
        "  shortcut = tf.keras.layers.BatchNormalization(name=name+\"-bnS\")(shortcut)\n",
        "\n",
        "  x = tf.keras.layers.Concatenate(axis=1,name=name+\"-concat\")([x,shortcut])\n",
        "  x = tf.keras.layers.Activation('relu',name=name+\"-relu3\")(x)\n",
        "  if do_rate > 0:\n",
        "    x = tf.keras.layers.Dropout(do_rate,name=name+'-do')(x)\n",
        "  return x\n",
        "\n",
        "# Input layers\n",
        "x0 = tf.keras.layers.DenseFeatures(feature_columns,name='dense_features_feature_columns')(feature_layer_inputs) \n",
        "x_hla = tf.keras.layers.Concatenate(axis=1)(shared_embedding_columns)\n",
        "x = tf.keras.layers.Concatenate(axis=1)([x0,x_hla])\n",
        "x = tf.keras.layers.Reshape((470,1))(x)\n",
        "x = tf.keras.layers.Dense(512, name=\"dense-0\")(x)\n",
        "x = tf.keras.layers.BatchNormalization(name=\"bn0\")(x)\n",
        "x = tf.keras.layers.Activation('relu',name=\"relu0\")(x)\n",
        "\n",
        "# Initial conv\n",
        "x = tf.keras.layers.Conv1D(4,7,2,padding='same',name='conv1')(x)\n",
        "x = tf.keras.layers.BatchNormalization(name=\"bn1\")(x)\n",
        "x = tf.keras.layers.Activation('relu',name=\"relu1\")(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=3,strides=2)(x)\n",
        "\n",
        "# resnet\n",
        "x = resnext_bottleneck((4,4,16),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet00-1\")\n",
        "x = resnext_bottleneck((4,4,16),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet00-2\")\n",
        "x = resnext_bottleneck((4,4,16),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet00-3\")\n",
        "x = resnext_bottleneck((4,4,16),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet00-4\")\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = resnext_bottleneck((8,8,32),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet0-1\")\n",
        "x = resnext_bottleneck((8,8,32),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet0-2\")\n",
        "x = resnext_bottleneck((8,8,32),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet0-3\")\n",
        "x = resnext_bottleneck((8,8,32),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet0-4\")\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = resnext_bottleneck((16,16,64),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet1-1\")\n",
        "x = resnext_bottleneck((16,16,64),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet1-2\")\n",
        "x = resnext_bottleneck((16,16,64),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet1-3\")\n",
        "x = resnext_bottleneck((16,16,64),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet1-4\")\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = resnext_bottleneck((32,32,128),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet2-1\")\n",
        "x = resnext_bottleneck((32,32,128),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet2-2\")\n",
        "x = resnext_bottleneck((32,32,128),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet2-3\")\n",
        "x = resnext_bottleneck((32,32,128),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet2-4\")\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = resnext_bottleneck((64,64,256),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet3-1\")\n",
        "x = resnext_bottleneck((64,64,256),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet3-2\")\n",
        "x = resnext_bottleneck((64,64,256),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet3-3\")\n",
        "x = resnext_bottleneck((64,64,256),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet3-4\")\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = resnext_bottleneck((128,128,512),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet4-1\")\n",
        "x = resnext_bottleneck((128,128,512),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet4-2\")\n",
        "x = resnext_bottleneck((128,128,512),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet4-3\")\n",
        "x = resnext_bottleneck((128,128,512),kernel_size=3,stride=2,do_rate=0.,x_in=x,name=\"resnet4-4\")\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "\n",
        "# output layers\n",
        "x = tf.keras.layers.GlobalAveragePooling1D(name='final-gap')(x)\n",
        "# x = tf.keras.layers.Flatten(name='final-flatten')(x)\n",
        "# x = tf.keras.layers.BatchNormalization(name='final-bn')(x)\n",
        "x = tf.keras.layers.Dropout(0.4,name='final-do')(x)\n",
        "output_layer = tf.keras.layers.Dense(2, activation=\"softmax\",name='final-dense',kernel_regularizer=tf.keras.regularizers.l2(0.01),bias_regularizer=tf.keras.regularizers.l2(0.01))(x)\n",
        "\n",
        "# finalize model\n",
        "model = tf.keras.models.Model(inputs=[v for v in shared_layer_inputs.values()]+\n",
        "                              [v for v in serogeno_feature_layer_inputs.values()]+\n",
        "                              [v for v in feature_layer_inputs.values()]+\n",
        "                              [v for v in hctci_feature_layer_inputs.values()], outputs=output_layer)\n",
        "\n",
        "# See model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "genora1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genora2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genoda1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genoda2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genorb1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genorb2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genodb1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genodb2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genordr1 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genordr2 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genoddr1 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genoddr2 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genorc1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genorc2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genodc1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genodc2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serora1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serora2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "seroda1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "seroda2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serorb1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serorb2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serodb1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serodb2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serorc1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serorc2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serodc1 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serodc2 (InputLayer)            [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serordr1 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serordr2 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "seroddr1 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "seroddr2 (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genora1-genora2-genoda1-genoda2 (None, 1, 3)         39          genora1[0][0]                    \n",
            "                                                                 genora2[0][0]                    \n",
            "                                                                 genoda1[0][0]                    \n",
            "                                                                 genoda2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "genorb1-genorb2-genodb1-genodb2 (None, 1, 4)         80          genorb1[0][0]                    \n",
            "                                                                 genorb2[0][0]                    \n",
            "                                                                 genodb1[0][0]                    \n",
            "                                                                 genodb2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "genordr1-genordr2-genoddr1-geno (None, 1, 4)         80          genordr1[0][0]                   \n",
            "                                                                 genordr2[0][0]                   \n",
            "                                                                 genoddr1[0][0]                   \n",
            "                                                                 genoddr2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "genorc1-genorc2-genodc1-genodc2 (None, 1, 3)         42          genorc1[0][0]                    \n",
            "                                                                 genorc2[0][0]                    \n",
            "                                                                 genodc1[0][0]                    \n",
            "                                                                 genodc2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "serora1-serora2-seroda1-seroda2 (None, 1, 2)         16          serora1[0][0]                    \n",
            "                                                                 serora2[0][0]                    \n",
            "                                                                 seroda1[0][0]                    \n",
            "                                                                 seroda2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "serorb1-serorb2-serodb1-serodb2 (None, 1, 4)         80          serorb1[0][0]                    \n",
            "                                                                 serorb2[0][0]                    \n",
            "                                                                 serodb1[0][0]                    \n",
            "                                                                 serodb2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "serorc1-serorc2-serodc1-serodc2 (None, 1, 3)         39          serorc1[0][0]                    \n",
            "                                                                 serorc2[0][0]                    \n",
            "                                                                 serodc1[0][0]                    \n",
            "                                                                 serodc2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "serordr1-serordr2-seroddr1-sero (None, 1, 3)         33          serordr1[0][0]                   \n",
            "                                                                 serordr2[0][0]                   \n",
            "                                                                 seroddr1[0][0]                   \n",
            "                                                                 seroddr2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "flatten-genora1 (Flatten)       (None, 3)            0           genora1-genora2-genoda1-genoda2[0\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genora2 (Flatten)       (None, 3)            0           genora1-genora2-genoda1-genoda2[1\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genoda1 (Flatten)       (None, 3)            0           genora1-genora2-genoda1-genoda2[2\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genoda2 (Flatten)       (None, 3)            0           genora1-genora2-genoda1-genoda2[3\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genorb1 (Flatten)       (None, 4)            0           genorb1-genorb2-genodb1-genodb2[0\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genorb2 (Flatten)       (None, 4)            0           genorb1-genorb2-genodb1-genodb2[1\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genodb1 (Flatten)       (None, 4)            0           genorb1-genorb2-genodb1-genodb2[2\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genodb2 (Flatten)       (None, 4)            0           genorb1-genorb2-genodb1-genodb2[3\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genordr1 (Flatten)      (None, 4)            0           genordr1-genordr2-genoddr1-genodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genordr2 (Flatten)      (None, 4)            0           genordr1-genordr2-genoddr1-genodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genoddr1 (Flatten)      (None, 4)            0           genordr1-genordr2-genoddr1-genodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genoddr2 (Flatten)      (None, 4)            0           genordr1-genordr2-genoddr1-genodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genorc1 (Flatten)       (None, 3)            0           genorc1-genorc2-genodc1-genodc2[0\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genorc2 (Flatten)       (None, 3)            0           genorc1-genorc2-genodc1-genodc2[1\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genodc1 (Flatten)       (None, 3)            0           genorc1-genorc2-genodc1-genodc2[2\n",
            "__________________________________________________________________________________________________\n",
            "flatten-genodc2 (Flatten)       (None, 3)            0           genorc1-genorc2-genodc1-genodc2[3\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serora1 (Flatten)       (None, 2)            0           serora1-serora2-seroda1-seroda2[0\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serora2 (Flatten)       (None, 2)            0           serora1-serora2-seroda1-seroda2[1\n",
            "__________________________________________________________________________________________________\n",
            "flatten-seroda1 (Flatten)       (None, 2)            0           serora1-serora2-seroda1-seroda2[2\n",
            "__________________________________________________________________________________________________\n",
            "flatten-seroda2 (Flatten)       (None, 2)            0           serora1-serora2-seroda1-seroda2[3\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serorb1 (Flatten)       (None, 4)            0           serorb1-serorb2-serodb1-serodb2[0\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serorb2 (Flatten)       (None, 4)            0           serorb1-serorb2-serodb1-serodb2[1\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serodb1 (Flatten)       (None, 4)            0           serorb1-serorb2-serodb1-serodb2[2\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serodb2 (Flatten)       (None, 4)            0           serorb1-serorb2-serodb1-serodb2[3\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serorc1 (Flatten)       (None, 3)            0           serorc1-serorc2-serodc1-serodc2[0\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serorc2 (Flatten)       (None, 3)            0           serorc1-serorc2-serodc1-serodc2[1\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serodc1 (Flatten)       (None, 3)            0           serorc1-serorc2-serodc1-serodc2[2\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serodc2 (Flatten)       (None, 3)            0           serorc1-serorc2-serodc1-serodc2[3\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serordr1 (Flatten)      (None, 3)            0           serordr1-serordr2-seroddr1-serodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-serordr2 (Flatten)      (None, 3)            0           serordr1-serordr2-seroddr1-serodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-seroddr1 (Flatten)      (None, 3)            0           serordr1-serordr2-seroddr1-serodd\n",
            "__________________________________________________________________________________________________\n",
            "flatten-seroddr2 (Flatten)      (None, 3)            0           serordr1-serordr2-seroddr1-serodd\n",
            "__________________________________________________________________________________________________\n",
            "abo_mismatch (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bm_tnc_total_harvest_kg_bool (I [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bmi2 (InputLayer)               [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "cond_regimen (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "diagnosis3 (InputLayer)         [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "do_abo (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "do_age (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "do_sex (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dx_to_sct (InputLayer)          [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dx_to_sct_group (InputLayer)    [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genogvhmis6abdr (InputLayer)    [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genogvhmis8abcdr (InputLayer)   [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genogvhmisa (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genogvhmisb (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genogvhmisc (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "genogvhmisdr (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "gvhd_pro (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "gvhd_pro2 (InputLayer)          [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "gvhd_pro3 (InputLayer)          [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_arrythmia_cd (InputLayer) [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_bowel_cd (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_cerebrovascular_cd (Input [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_dm_cd (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_heart_cd (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_high (InputLayer)         [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_inf_cd (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_liver_cd (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_liver_severe_cd (InputLay [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_lung_cd (InputLayer)      [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_lung_severe_cd (InputLaye [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_mental_cd (InputLayer)    [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_obesity_cd (InputLayer)   [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_points_corr (InputLayer)  [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_renal_cd (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_rhumatoid_cd (InputLayer) [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_st_cd (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_ulcer_cd (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "hctci_valve_cd (InputLayer)     [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ks_tx2 (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "pb_cd34_total_kg_bool (InputLay [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "pb_tnc_total_cryop_kg_bool (Inp [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "plt_transfusion (InputLayer)    [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ps24 (InputLayer)               [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ps_tx (InputLayer)              [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "pt_abo (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "pt_age (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "pt_sex (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rbc_transfusion (InputLayer)    [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "ric_mac_tbi (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "sct_type (InputLayer)           [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serogvhmis6abdr (InputLayer)    [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serogvhmis8abcdr (InputLayer)   [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serogvhmisa (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serogvhmisb (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serogvhmisc (InputLayer)        [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "serogvhmisdr (InputLayer)       [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "stage4 (InputLayer)             [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "tbi (InputLayer)                [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "tyear (InputLayer)              [(None, 1)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "concat-genora1-genora2-genoda1- (None, 12)           0           flatten-genora1[0][0]            \n",
            "                                                                 flatten-genora2[0][0]            \n",
            "                                                                 flatten-genoda1[0][0]            \n",
            "                                                                 flatten-genoda2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concat-genorb1-genorb2-genodb1- (None, 16)           0           flatten-genorb1[0][0]            \n",
            "                                                                 flatten-genorb2[0][0]            \n",
            "                                                                 flatten-genodb1[0][0]            \n",
            "                                                                 flatten-genodb2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concat-genordr1-genordr2-genodd (None, 16)           0           flatten-genordr1[0][0]           \n",
            "                                                                 flatten-genordr2[0][0]           \n",
            "                                                                 flatten-genoddr1[0][0]           \n",
            "                                                                 flatten-genoddr2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "concat-genorc1-genorc2-genodc1- (None, 12)           0           flatten-genorc1[0][0]            \n",
            "                                                                 flatten-genorc2[0][0]            \n",
            "                                                                 flatten-genodc1[0][0]            \n",
            "                                                                 flatten-genodc2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concat-serora1-serora2-seroda1- (None, 8)            0           flatten-serora1[0][0]            \n",
            "                                                                 flatten-serora2[0][0]            \n",
            "                                                                 flatten-seroda1[0][0]            \n",
            "                                                                 flatten-seroda2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concat-serorb1-serorb2-serodb1- (None, 16)           0           flatten-serorb1[0][0]            \n",
            "                                                                 flatten-serorb2[0][0]            \n",
            "                                                                 flatten-serodb1[0][0]            \n",
            "                                                                 flatten-serodb2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concat-serorc1-serorc2-serodc1- (None, 12)           0           flatten-serorc1[0][0]            \n",
            "                                                                 flatten-serorc2[0][0]            \n",
            "                                                                 flatten-serodc1[0][0]            \n",
            "                                                                 flatten-serodc2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "concat-serordr1-serordr2-serodd (None, 12)           0           flatten-serordr1[0][0]           \n",
            "                                                                 flatten-serordr2[0][0]           \n",
            "                                                                 flatten-seroddr1[0][0]           \n",
            "                                                                 flatten-seroddr2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_features_feature_columns  (None, 366)          0           abo_mismatch[0][0]               \n",
            "                                                                 bm_tnc_total_harvest_kg_bool[0][0\n",
            "                                                                 bmi2[0][0]                       \n",
            "                                                                 cond_regimen[0][0]               \n",
            "                                                                 diagnosis3[0][0]                 \n",
            "                                                                 do_abo[0][0]                     \n",
            "                                                                 do_age[0][0]                     \n",
            "                                                                 do_sex[0][0]                     \n",
            "                                                                 dx_to_sct[0][0]                  \n",
            "                                                                 dx_to_sct_group[0][0]            \n",
            "                                                                 genogvhmis6abdr[0][0]            \n",
            "                                                                 genogvhmis8abcdr[0][0]           \n",
            "                                                                 genogvhmisa[0][0]                \n",
            "                                                                 genogvhmisb[0][0]                \n",
            "                                                                 genogvhmisc[0][0]                \n",
            "                                                                 genogvhmisdr[0][0]               \n",
            "                                                                 gvhd_pro[0][0]                   \n",
            "                                                                 gvhd_pro2[0][0]                  \n",
            "                                                                 gvhd_pro3[0][0]                  \n",
            "                                                                 hctci_arrythmia_cd[0][0]         \n",
            "                                                                 hctci_bowel_cd[0][0]             \n",
            "                                                                 hctci_cerebrovascular_cd[0][0]   \n",
            "                                                                 hctci_dm_cd[0][0]                \n",
            "                                                                 hctci_heart_cd[0][0]             \n",
            "                                                                 hctci_high[0][0]                 \n",
            "                                                                 hctci_inf_cd[0][0]               \n",
            "                                                                 hctci_liver_cd[0][0]             \n",
            "                                                                 hctci_liver_severe_cd[0][0]      \n",
            "                                                                 hctci_lung_cd[0][0]              \n",
            "                                                                 hctci_lung_severe_cd[0][0]       \n",
            "                                                                 hctci_mental_cd[0][0]            \n",
            "                                                                 hctci_obesity_cd[0][0]           \n",
            "                                                                 hctci_points_corr[0][0]          \n",
            "                                                                 hctci_renal_cd[0][0]             \n",
            "                                                                 hctci_rhumatoid_cd[0][0]         \n",
            "                                                                 hctci_st_cd[0][0]                \n",
            "                                                                 hctci_ulcer_cd[0][0]             \n",
            "                                                                 hctci_valve_cd[0][0]             \n",
            "                                                                 ks_tx2[0][0]                     \n",
            "                                                                 pb_cd34_total_kg_bool[0][0]      \n",
            "                                                                 pb_tnc_total_cryop_kg_bool[0][0] \n",
            "                                                                 plt_transfusion[0][0]            \n",
            "                                                                 ps24[0][0]                       \n",
            "                                                                 ps_tx[0][0]                      \n",
            "                                                                 pt_abo[0][0]                     \n",
            "                                                                 pt_age[0][0]                     \n",
            "                                                                 pt_sex[0][0]                     \n",
            "                                                                 rbc_transfusion[0][0]            \n",
            "                                                                 ric_mac_tbi[0][0]                \n",
            "                                                                 sct_type[0][0]                   \n",
            "                                                                 serogvhmis6abdr[0][0]            \n",
            "                                                                 serogvhmis8abcdr[0][0]           \n",
            "                                                                 serogvhmisa[0][0]                \n",
            "                                                                 serogvhmisb[0][0]                \n",
            "                                                                 serogvhmisc[0][0]                \n",
            "                                                                 serogvhmisdr[0][0]               \n",
            "                                                                 stage4[0][0]                     \n",
            "                                                                 tbi[0][0]                        \n",
            "                                                                 tyear[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_10 (Concatenate)    (None, 104)          0           concat-genora1-genora2-genoda1-ge\n",
            "                                                                 concat-genorb1-genorb2-genodb1-ge\n",
            "                                                                 concat-genordr1-genordr2-genoddr1\n",
            "                                                                 concat-genorc1-genorc2-genodc1-ge\n",
            "                                                                 concat-serora1-serora2-seroda1-se\n",
            "                                                                 concat-serorb1-serorb2-serodb1-se\n",
            "                                                                 concat-serorc1-serorc2-serodc1-se\n",
            "                                                                 concat-serordr1-serordr2-seroddr1\n",
            "__________________________________________________________________________________________________\n",
            "concatenate_11 (Concatenate)    (None, 470)          0           dense_features_feature_columns[0]\n",
            "                                                                 concatenate_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "reshape_5 (Reshape)             (None, 470, 1)       0           concatenate_11[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dense-0 (Dense)                 (None, 470, 512)     1024        reshape_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn0 (BatchNormalization)        (None, 470, 512)     2048        dense-0[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "relu0 (Activation)              (None, 470, 512)     0           bn0[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv1D)                  (None, 235, 4)       14340       relu0[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "bn1 (BatchNormalization)        (None, 235, 4)       16          conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "relu1 (Activation)              (None, 235, 4)       0           bn1[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_23 (MaxPooling1D) (None, 117, 4)       0           relu1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-conv1 (Conv1D)       (None, 117, 4)       20          max_pooling1d_23[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-bn1 (BatchNormalizat (None, 117, 4)       16          resnet00-1-conv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-relu1 (Activation)   (None, 117, 4)       0           resnet00-1-bn1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-conv2 (Conv1D)       (None, 59, 4)        52          resnet00-1-relu1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-bn2 (BatchNormalizat (None, 59, 4)        16          resnet00-1-conv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-relu2 (Activation)   (None, 59, 4)        0           resnet00-1-bn2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-conv3 (Conv1D)       (None, 59, 16)       80          resnet00-1-relu2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-convS (Conv1D)       (None, 59, 16)       80          max_pooling1d_23[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-bn3 (BatchNormalizat (None, 59, 16)       64          resnet00-1-conv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-bnS (BatchNormalizat (None, 59, 16)       64          resnet00-1-convS[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-concat (Concatenate) (None, 118, 16)      0           resnet00-1-bn3[0][0]             \n",
            "                                                                 resnet00-1-bnS[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-1-relu3 (Activation)   (None, 118, 16)      0           resnet00-1-concat[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-conv1 (Conv1D)       (None, 118, 4)       68          resnet00-1-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-bn1 (BatchNormalizat (None, 118, 4)       16          resnet00-2-conv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-relu1 (Activation)   (None, 118, 4)       0           resnet00-2-bn1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-conv2 (Conv1D)       (None, 59, 4)        52          resnet00-2-relu1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-bn2 (BatchNormalizat (None, 59, 4)        16          resnet00-2-conv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-relu2 (Activation)   (None, 59, 4)        0           resnet00-2-bn2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-conv3 (Conv1D)       (None, 59, 16)       80          resnet00-2-relu2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-convS (Conv1D)       (None, 59, 16)       272         resnet00-1-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-bn3 (BatchNormalizat (None, 59, 16)       64          resnet00-2-conv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-bnS (BatchNormalizat (None, 59, 16)       64          resnet00-2-convS[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-concat (Concatenate) (None, 118, 16)      0           resnet00-2-bn3[0][0]             \n",
            "                                                                 resnet00-2-bnS[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-2-relu3 (Activation)   (None, 118, 16)      0           resnet00-2-concat[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-conv1 (Conv1D)       (None, 118, 4)       68          resnet00-2-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-bn1 (BatchNormalizat (None, 118, 4)       16          resnet00-3-conv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-relu1 (Activation)   (None, 118, 4)       0           resnet00-3-bn1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-conv2 (Conv1D)       (None, 59, 4)        52          resnet00-3-relu1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-bn2 (BatchNormalizat (None, 59, 4)        16          resnet00-3-conv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-relu2 (Activation)   (None, 59, 4)        0           resnet00-3-bn2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-conv3 (Conv1D)       (None, 59, 16)       80          resnet00-3-relu2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-convS (Conv1D)       (None, 59, 16)       272         resnet00-2-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-bn3 (BatchNormalizat (None, 59, 16)       64          resnet00-3-conv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-bnS (BatchNormalizat (None, 59, 16)       64          resnet00-3-convS[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-concat (Concatenate) (None, 118, 16)      0           resnet00-3-bn3[0][0]             \n",
            "                                                                 resnet00-3-bnS[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-3-relu3 (Activation)   (None, 118, 16)      0           resnet00-3-concat[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-conv1 (Conv1D)       (None, 118, 4)       68          resnet00-3-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-bn1 (BatchNormalizat (None, 118, 4)       16          resnet00-4-conv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-relu1 (Activation)   (None, 118, 4)       0           resnet00-4-bn1[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-conv2 (Conv1D)       (None, 59, 4)        52          resnet00-4-relu1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-bn2 (BatchNormalizat (None, 59, 4)        16          resnet00-4-conv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-relu2 (Activation)   (None, 59, 4)        0           resnet00-4-bn2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-conv3 (Conv1D)       (None, 59, 16)       80          resnet00-4-relu2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-convS (Conv1D)       (None, 59, 16)       272         resnet00-3-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-bn3 (BatchNormalizat (None, 59, 16)       64          resnet00-4-conv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-bnS (BatchNormalizat (None, 59, 16)       64          resnet00-4-convS[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-concat (Concatenate) (None, 118, 16)      0           resnet00-4-bn3[0][0]             \n",
            "                                                                 resnet00-4-bnS[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "resnet00-4-relu3 (Activation)   (None, 118, 16)      0           resnet00-4-concat[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_24 (MaxPooling1D) (None, 59, 16)       0           resnet00-4-relu3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-conv1 (Conv1D)        (None, 59, 8)        136         max_pooling1d_24[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-bn1 (BatchNormalizati (None, 59, 8)        32          resnet0-1-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-relu1 (Activation)    (None, 59, 8)        0           resnet0-1-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-conv2 (Conv1D)        (None, 30, 8)        200         resnet0-1-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-bn2 (BatchNormalizati (None, 30, 8)        32          resnet0-1-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-relu2 (Activation)    (None, 30, 8)        0           resnet0-1-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-conv3 (Conv1D)        (None, 30, 32)       288         resnet0-1-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-convS (Conv1D)        (None, 30, 32)       544         max_pooling1d_24[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-bn3 (BatchNormalizati (None, 30, 32)       128         resnet0-1-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-bnS (BatchNormalizati (None, 30, 32)       128         resnet0-1-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-concat (Concatenate)  (None, 60, 32)       0           resnet0-1-bn3[0][0]              \n",
            "                                                                 resnet0-1-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-1-relu3 (Activation)    (None, 60, 32)       0           resnet0-1-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-conv1 (Conv1D)        (None, 60, 8)        264         resnet0-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-bn1 (BatchNormalizati (None, 60, 8)        32          resnet0-2-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-relu1 (Activation)    (None, 60, 8)        0           resnet0-2-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-conv2 (Conv1D)        (None, 30, 8)        200         resnet0-2-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-bn2 (BatchNormalizati (None, 30, 8)        32          resnet0-2-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-relu2 (Activation)    (None, 30, 8)        0           resnet0-2-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-conv3 (Conv1D)        (None, 30, 32)       288         resnet0-2-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-convS (Conv1D)        (None, 30, 32)       1056        resnet0-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-bn3 (BatchNormalizati (None, 30, 32)       128         resnet0-2-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-bnS (BatchNormalizati (None, 30, 32)       128         resnet0-2-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-concat (Concatenate)  (None, 60, 32)       0           resnet0-2-bn3[0][0]              \n",
            "                                                                 resnet0-2-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-2-relu3 (Activation)    (None, 60, 32)       0           resnet0-2-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-conv1 (Conv1D)        (None, 60, 8)        264         resnet0-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-bn1 (BatchNormalizati (None, 60, 8)        32          resnet0-3-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-relu1 (Activation)    (None, 60, 8)        0           resnet0-3-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-conv2 (Conv1D)        (None, 30, 8)        200         resnet0-3-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-bn2 (BatchNormalizati (None, 30, 8)        32          resnet0-3-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-relu2 (Activation)    (None, 30, 8)        0           resnet0-3-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-conv3 (Conv1D)        (None, 30, 32)       288         resnet0-3-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-convS (Conv1D)        (None, 30, 32)       1056        resnet0-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-bn3 (BatchNormalizati (None, 30, 32)       128         resnet0-3-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-bnS (BatchNormalizati (None, 30, 32)       128         resnet0-3-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-concat (Concatenate)  (None, 60, 32)       0           resnet0-3-bn3[0][0]              \n",
            "                                                                 resnet0-3-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-3-relu3 (Activation)    (None, 60, 32)       0           resnet0-3-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-conv1 (Conv1D)        (None, 60, 8)        264         resnet0-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-bn1 (BatchNormalizati (None, 60, 8)        32          resnet0-4-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-relu1 (Activation)    (None, 60, 8)        0           resnet0-4-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-conv2 (Conv1D)        (None, 30, 8)        200         resnet0-4-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-bn2 (BatchNormalizati (None, 30, 8)        32          resnet0-4-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-relu2 (Activation)    (None, 30, 8)        0           resnet0-4-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-conv3 (Conv1D)        (None, 30, 32)       288         resnet0-4-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-convS (Conv1D)        (None, 30, 32)       1056        resnet0-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-bn3 (BatchNormalizati (None, 30, 32)       128         resnet0-4-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-bnS (BatchNormalizati (None, 30, 32)       128         resnet0-4-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-concat (Concatenate)  (None, 60, 32)       0           resnet0-4-bn3[0][0]              \n",
            "                                                                 resnet0-4-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet0-4-relu3 (Activation)    (None, 60, 32)       0           resnet0-4-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_25 (MaxPooling1D) (None, 30, 32)       0           resnet0-4-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-conv1 (Conv1D)        (None, 30, 16)       528         max_pooling1d_25[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-bn1 (BatchNormalizati (None, 30, 16)       64          resnet1-1-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-relu1 (Activation)    (None, 30, 16)       0           resnet1-1-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-conv2 (Conv1D)        (None, 15, 16)       784         resnet1-1-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-bn2 (BatchNormalizati (None, 15, 16)       64          resnet1-1-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-relu2 (Activation)    (None, 15, 16)       0           resnet1-1-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-conv3 (Conv1D)        (None, 15, 64)       1088        resnet1-1-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-convS (Conv1D)        (None, 15, 64)       2112        max_pooling1d_25[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-bn3 (BatchNormalizati (None, 15, 64)       256         resnet1-1-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-bnS (BatchNormalizati (None, 15, 64)       256         resnet1-1-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-concat (Concatenate)  (None, 30, 64)       0           resnet1-1-bn3[0][0]              \n",
            "                                                                 resnet1-1-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-1-relu3 (Activation)    (None, 30, 64)       0           resnet1-1-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-conv1 (Conv1D)        (None, 30, 16)       1040        resnet1-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-bn1 (BatchNormalizati (None, 30, 16)       64          resnet1-2-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-relu1 (Activation)    (None, 30, 16)       0           resnet1-2-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-conv2 (Conv1D)        (None, 15, 16)       784         resnet1-2-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-bn2 (BatchNormalizati (None, 15, 16)       64          resnet1-2-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-relu2 (Activation)    (None, 15, 16)       0           resnet1-2-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-conv3 (Conv1D)        (None, 15, 64)       1088        resnet1-2-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-convS (Conv1D)        (None, 15, 64)       4160        resnet1-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-bn3 (BatchNormalizati (None, 15, 64)       256         resnet1-2-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-bnS (BatchNormalizati (None, 15, 64)       256         resnet1-2-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-concat (Concatenate)  (None, 30, 64)       0           resnet1-2-bn3[0][0]              \n",
            "                                                                 resnet1-2-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-2-relu3 (Activation)    (None, 30, 64)       0           resnet1-2-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-conv1 (Conv1D)        (None, 30, 16)       1040        resnet1-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-bn1 (BatchNormalizati (None, 30, 16)       64          resnet1-3-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-relu1 (Activation)    (None, 30, 16)       0           resnet1-3-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-conv2 (Conv1D)        (None, 15, 16)       784         resnet1-3-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-bn2 (BatchNormalizati (None, 15, 16)       64          resnet1-3-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-relu2 (Activation)    (None, 15, 16)       0           resnet1-3-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-conv3 (Conv1D)        (None, 15, 64)       1088        resnet1-3-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-convS (Conv1D)        (None, 15, 64)       4160        resnet1-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-bn3 (BatchNormalizati (None, 15, 64)       256         resnet1-3-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-bnS (BatchNormalizati (None, 15, 64)       256         resnet1-3-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-concat (Concatenate)  (None, 30, 64)       0           resnet1-3-bn3[0][0]              \n",
            "                                                                 resnet1-3-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-3-relu3 (Activation)    (None, 30, 64)       0           resnet1-3-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-conv1 (Conv1D)        (None, 30, 16)       1040        resnet1-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-bn1 (BatchNormalizati (None, 30, 16)       64          resnet1-4-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-relu1 (Activation)    (None, 30, 16)       0           resnet1-4-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-conv2 (Conv1D)        (None, 15, 16)       784         resnet1-4-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-bn2 (BatchNormalizati (None, 15, 16)       64          resnet1-4-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-relu2 (Activation)    (None, 15, 16)       0           resnet1-4-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-conv3 (Conv1D)        (None, 15, 64)       1088        resnet1-4-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-convS (Conv1D)        (None, 15, 64)       4160        resnet1-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-bn3 (BatchNormalizati (None, 15, 64)       256         resnet1-4-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-bnS (BatchNormalizati (None, 15, 64)       256         resnet1-4-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-concat (Concatenate)  (None, 30, 64)       0           resnet1-4-bn3[0][0]              \n",
            "                                                                 resnet1-4-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet1-4-relu3 (Activation)    (None, 30, 64)       0           resnet1-4-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_26 (MaxPooling1D) (None, 15, 64)       0           resnet1-4-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-conv1 (Conv1D)        (None, 15, 32)       2080        max_pooling1d_26[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-bn1 (BatchNormalizati (None, 15, 32)       128         resnet2-1-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-relu1 (Activation)    (None, 15, 32)       0           resnet2-1-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-conv2 (Conv1D)        (None, 8, 32)        3104        resnet2-1-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-bn2 (BatchNormalizati (None, 8, 32)        128         resnet2-1-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-relu2 (Activation)    (None, 8, 32)        0           resnet2-1-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-conv3 (Conv1D)        (None, 8, 128)       4224        resnet2-1-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-convS (Conv1D)        (None, 8, 128)       8320        max_pooling1d_26[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-bn3 (BatchNormalizati (None, 8, 128)       512         resnet2-1-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-bnS (BatchNormalizati (None, 8, 128)       512         resnet2-1-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-concat (Concatenate)  (None, 16, 128)      0           resnet2-1-bn3[0][0]              \n",
            "                                                                 resnet2-1-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-1-relu3 (Activation)    (None, 16, 128)      0           resnet2-1-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-conv1 (Conv1D)        (None, 16, 32)       4128        resnet2-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-bn1 (BatchNormalizati (None, 16, 32)       128         resnet2-2-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-relu1 (Activation)    (None, 16, 32)       0           resnet2-2-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-conv2 (Conv1D)        (None, 8, 32)        3104        resnet2-2-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-bn2 (BatchNormalizati (None, 8, 32)        128         resnet2-2-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-relu2 (Activation)    (None, 8, 32)        0           resnet2-2-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-conv3 (Conv1D)        (None, 8, 128)       4224        resnet2-2-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-convS (Conv1D)        (None, 8, 128)       16512       resnet2-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-bn3 (BatchNormalizati (None, 8, 128)       512         resnet2-2-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-bnS (BatchNormalizati (None, 8, 128)       512         resnet2-2-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-concat (Concatenate)  (None, 16, 128)      0           resnet2-2-bn3[0][0]              \n",
            "                                                                 resnet2-2-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-2-relu3 (Activation)    (None, 16, 128)      0           resnet2-2-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-conv1 (Conv1D)        (None, 16, 32)       4128        resnet2-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-bn1 (BatchNormalizati (None, 16, 32)       128         resnet2-3-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-relu1 (Activation)    (None, 16, 32)       0           resnet2-3-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-conv2 (Conv1D)        (None, 8, 32)        3104        resnet2-3-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-bn2 (BatchNormalizati (None, 8, 32)        128         resnet2-3-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-relu2 (Activation)    (None, 8, 32)        0           resnet2-3-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-conv3 (Conv1D)        (None, 8, 128)       4224        resnet2-3-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-convS (Conv1D)        (None, 8, 128)       16512       resnet2-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-bn3 (BatchNormalizati (None, 8, 128)       512         resnet2-3-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-bnS (BatchNormalizati (None, 8, 128)       512         resnet2-3-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-concat (Concatenate)  (None, 16, 128)      0           resnet2-3-bn3[0][0]              \n",
            "                                                                 resnet2-3-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-3-relu3 (Activation)    (None, 16, 128)      0           resnet2-3-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-conv1 (Conv1D)        (None, 16, 32)       4128        resnet2-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-bn1 (BatchNormalizati (None, 16, 32)       128         resnet2-4-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-relu1 (Activation)    (None, 16, 32)       0           resnet2-4-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-conv2 (Conv1D)        (None, 8, 32)        3104        resnet2-4-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-bn2 (BatchNormalizati (None, 8, 32)        128         resnet2-4-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-relu2 (Activation)    (None, 8, 32)        0           resnet2-4-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-conv3 (Conv1D)        (None, 8, 128)       4224        resnet2-4-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-convS (Conv1D)        (None, 8, 128)       16512       resnet2-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-bn3 (BatchNormalizati (None, 8, 128)       512         resnet2-4-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-bnS (BatchNormalizati (None, 8, 128)       512         resnet2-4-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-concat (Concatenate)  (None, 16, 128)      0           resnet2-4-bn3[0][0]              \n",
            "                                                                 resnet2-4-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet2-4-relu3 (Activation)    (None, 16, 128)      0           resnet2-4-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_27 (MaxPooling1D) (None, 8, 128)       0           resnet2-4-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-conv1 (Conv1D)        (None, 8, 64)        8256        max_pooling1d_27[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-bn1 (BatchNormalizati (None, 8, 64)        256         resnet3-1-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-relu1 (Activation)    (None, 8, 64)        0           resnet3-1-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-conv2 (Conv1D)        (None, 4, 64)        12352       resnet3-1-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-bn2 (BatchNormalizati (None, 4, 64)        256         resnet3-1-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-relu2 (Activation)    (None, 4, 64)        0           resnet3-1-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-conv3 (Conv1D)        (None, 4, 256)       16640       resnet3-1-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-convS (Conv1D)        (None, 4, 256)       33024       max_pooling1d_27[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-bn3 (BatchNormalizati (None, 4, 256)       1024        resnet3-1-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-bnS (BatchNormalizati (None, 4, 256)       1024        resnet3-1-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-concat (Concatenate)  (None, 8, 256)       0           resnet3-1-bn3[0][0]              \n",
            "                                                                 resnet3-1-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-1-relu3 (Activation)    (None, 8, 256)       0           resnet3-1-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-conv1 (Conv1D)        (None, 8, 64)        16448       resnet3-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-bn1 (BatchNormalizati (None, 8, 64)        256         resnet3-2-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-relu1 (Activation)    (None, 8, 64)        0           resnet3-2-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-conv2 (Conv1D)        (None, 4, 64)        12352       resnet3-2-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-bn2 (BatchNormalizati (None, 4, 64)        256         resnet3-2-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-relu2 (Activation)    (None, 4, 64)        0           resnet3-2-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-conv3 (Conv1D)        (None, 4, 256)       16640       resnet3-2-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-convS (Conv1D)        (None, 4, 256)       65792       resnet3-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-bn3 (BatchNormalizati (None, 4, 256)       1024        resnet3-2-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-bnS (BatchNormalizati (None, 4, 256)       1024        resnet3-2-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-concat (Concatenate)  (None, 8, 256)       0           resnet3-2-bn3[0][0]              \n",
            "                                                                 resnet3-2-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-2-relu3 (Activation)    (None, 8, 256)       0           resnet3-2-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-conv1 (Conv1D)        (None, 8, 64)        16448       resnet3-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-bn1 (BatchNormalizati (None, 8, 64)        256         resnet3-3-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-relu1 (Activation)    (None, 8, 64)        0           resnet3-3-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-conv2 (Conv1D)        (None, 4, 64)        12352       resnet3-3-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-bn2 (BatchNormalizati (None, 4, 64)        256         resnet3-3-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-relu2 (Activation)    (None, 4, 64)        0           resnet3-3-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-conv3 (Conv1D)        (None, 4, 256)       16640       resnet3-3-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-convS (Conv1D)        (None, 4, 256)       65792       resnet3-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-bn3 (BatchNormalizati (None, 4, 256)       1024        resnet3-3-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-bnS (BatchNormalizati (None, 4, 256)       1024        resnet3-3-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-concat (Concatenate)  (None, 8, 256)       0           resnet3-3-bn3[0][0]              \n",
            "                                                                 resnet3-3-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-3-relu3 (Activation)    (None, 8, 256)       0           resnet3-3-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-conv1 (Conv1D)        (None, 8, 64)        16448       resnet3-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-bn1 (BatchNormalizati (None, 8, 64)        256         resnet3-4-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-relu1 (Activation)    (None, 8, 64)        0           resnet3-4-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-conv2 (Conv1D)        (None, 4, 64)        12352       resnet3-4-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-bn2 (BatchNormalizati (None, 4, 64)        256         resnet3-4-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-relu2 (Activation)    (None, 4, 64)        0           resnet3-4-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-conv3 (Conv1D)        (None, 4, 256)       16640       resnet3-4-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-convS (Conv1D)        (None, 4, 256)       65792       resnet3-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-bn3 (BatchNormalizati (None, 4, 256)       1024        resnet3-4-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-bnS (BatchNormalizati (None, 4, 256)       1024        resnet3-4-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-concat (Concatenate)  (None, 8, 256)       0           resnet3-4-bn3[0][0]              \n",
            "                                                                 resnet3-4-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet3-4-relu3 (Activation)    (None, 8, 256)       0           resnet3-4-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_28 (MaxPooling1D) (None, 4, 256)       0           resnet3-4-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-conv1 (Conv1D)        (None, 4, 128)       32896       max_pooling1d_28[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-bn1 (BatchNormalizati (None, 4, 128)       512         resnet4-1-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-relu1 (Activation)    (None, 4, 128)       0           resnet4-1-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-conv2 (Conv1D)        (None, 2, 128)       49280       resnet4-1-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-bn2 (BatchNormalizati (None, 2, 128)       512         resnet4-1-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-relu2 (Activation)    (None, 2, 128)       0           resnet4-1-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-conv3 (Conv1D)        (None, 2, 512)       66048       resnet4-1-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-convS (Conv1D)        (None, 2, 512)       131584      max_pooling1d_28[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-bn3 (BatchNormalizati (None, 2, 512)       2048        resnet4-1-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-bnS (BatchNormalizati (None, 2, 512)       2048        resnet4-1-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-concat (Concatenate)  (None, 4, 512)       0           resnet4-1-bn3[0][0]              \n",
            "                                                                 resnet4-1-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-1-relu3 (Activation)    (None, 4, 512)       0           resnet4-1-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-conv1 (Conv1D)        (None, 4, 128)       65664       resnet4-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-bn1 (BatchNormalizati (None, 4, 128)       512         resnet4-2-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-relu1 (Activation)    (None, 4, 128)       0           resnet4-2-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-conv2 (Conv1D)        (None, 2, 128)       49280       resnet4-2-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-bn2 (BatchNormalizati (None, 2, 128)       512         resnet4-2-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-relu2 (Activation)    (None, 2, 128)       0           resnet4-2-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-conv3 (Conv1D)        (None, 2, 512)       66048       resnet4-2-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-convS (Conv1D)        (None, 2, 512)       262656      resnet4-1-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-bn3 (BatchNormalizati (None, 2, 512)       2048        resnet4-2-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-bnS (BatchNormalizati (None, 2, 512)       2048        resnet4-2-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-concat (Concatenate)  (None, 4, 512)       0           resnet4-2-bn3[0][0]              \n",
            "                                                                 resnet4-2-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-2-relu3 (Activation)    (None, 4, 512)       0           resnet4-2-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-conv1 (Conv1D)        (None, 4, 128)       65664       resnet4-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-bn1 (BatchNormalizati (None, 4, 128)       512         resnet4-3-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-relu1 (Activation)    (None, 4, 128)       0           resnet4-3-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-conv2 (Conv1D)        (None, 2, 128)       49280       resnet4-3-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-bn2 (BatchNormalizati (None, 2, 128)       512         resnet4-3-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-relu2 (Activation)    (None, 2, 128)       0           resnet4-3-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-conv3 (Conv1D)        (None, 2, 512)       66048       resnet4-3-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-convS (Conv1D)        (None, 2, 512)       262656      resnet4-2-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-bn3 (BatchNormalizati (None, 2, 512)       2048        resnet4-3-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-bnS (BatchNormalizati (None, 2, 512)       2048        resnet4-3-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-concat (Concatenate)  (None, 4, 512)       0           resnet4-3-bn3[0][0]              \n",
            "                                                                 resnet4-3-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-3-relu3 (Activation)    (None, 4, 512)       0           resnet4-3-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-conv1 (Conv1D)        (None, 4, 128)       65664       resnet4-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-bn1 (BatchNormalizati (None, 4, 128)       512         resnet4-4-conv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-relu1 (Activation)    (None, 4, 128)       0           resnet4-4-bn1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-conv2 (Conv1D)        (None, 2, 128)       49280       resnet4-4-relu1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-bn2 (BatchNormalizati (None, 2, 128)       512         resnet4-4-conv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-relu2 (Activation)    (None, 2, 128)       0           resnet4-4-bn2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-conv3 (Conv1D)        (None, 2, 512)       66048       resnet4-4-relu2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-convS (Conv1D)        (None, 2, 512)       262656      resnet4-3-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-bn3 (BatchNormalizati (None, 2, 512)       2048        resnet4-4-conv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-bnS (BatchNormalizati (None, 2, 512)       2048        resnet4-4-convS[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-concat (Concatenate)  (None, 4, 512)       0           resnet4-4-bn3[0][0]              \n",
            "                                                                 resnet4-4-bnS[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "resnet4-4-relu3 (Activation)    (None, 4, 512)       0           resnet4-4-concat[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling1d_29 (MaxPooling1D) (None, 2, 512)       0           resnet4-4-relu3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "final-gap (GlobalAveragePooling (None, 512)          0           max_pooling1d_29[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "final-do (Dropout)              (None, 512)          0           final-gap[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "final-dense (Dense)             (None, 2)            1026        final-do[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 2,209,503\n",
            "Trainable params: 2,188,311\n",
            "Non-trainable params: 21,192\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "fue_uOUWicJS",
        "outputId": "372c00c8-d37d-4f51-9903-e49202e211fb"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, to_file='full_model.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tcmalloc: large alloc 2167472128 bytes == 0x55b925dfa000 @  0x7f3b69dce001 0x7f3b660651fa 0x7f3b660652ad 0x7f3b671f06df 0x7f3b676e7261 0x7f3b69b64468 0x7f3b69b66d53 0x55b924ac8092 0x7f3b69519bf7 0x55b924ac812a\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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clDkXG2NK19CPi6WI27uI6+ybpLcR4x1y4Au2U3JoW/exjZJBu7orzeMTF/sBc1jpc7EfFF3R+2u1PmlSH/PUdmTTT3k7t+QL2g6+CLufuuOOO2T27Nly1113yZ577ildunSRQYMGyZw5c2TnnXcO9d3Mg1ZHDYEvwtaQG264Qbbeems555xzpKmpScaMGSOTJk2SF154QebOnRvqu6kh0VFj4JM8janjILfwSZTc5nm/Tn7hC/a5m5BZ+ITcmiHXbnJlu3CfBWzjuoJN6IfZov3toS0RRpH7y7///W85//zz5bvf/a6MGTMm68XJXN6fuRYX/aU22/3nmWeekVNOOUX22Wcfefjhh2X8+PHSp08fqa+vlxEjRsgRRxwhF110kaxfvz6BtbEjy/pa5NoOAMhG3vc9JnNkRTxeNGkXxhn0H1NZ9CfGHf5+NwAA8BfHENUxLgiH8WY89Ld4GJ92xPgUAJAnRd63cJxYinFHbfSX2hg3dMS4AQBQJHnf9xTxWJDxQTG3ezmO8zviOB8AAPgiD8cORb43hfuXNityPyjHfUjRMI4DAAC+yfsxRFGO39sryvitliJu93KM1zpivAYAAFxU9OMEjt07YkwXjH4TjDFhR4wJAQAiIp3S+qKuXbum9VW5c9NNN8ny5csj/W1eX3oep01ghyuZdqEvxFmGrJbf59qQVZupqtx1110yffr0SH/vSmZ85ULWiyyL/nvfffdJ586dS3625ZZbiohIc3Nz6stjG3262FzZD8fdtyEYWXdPkvnzLVP33nuvNDQ0lPxsm222ERGRtWvXZrFIVpE/d7iy32NMRi5c5Eo+yvm2T4uDXLjH1VwUBZlwSxp5cH2bu758YeRpXfKAfNlfviIdQ6bN9b5UNK72dRvL5dKx+PXXXy+77rqrjB49OvZnRZ0Tcqk9kkJ9cQv1JR1Lly6VQYMGlSzT4MGDRURk8eLFoT4rqTlnl9orKdQf91CDEAeZdgt5Tk+ezz+Ta/eQbcRBpt1CnrPj+vLZQN7dklWuitDXs0LG3EPOquOePKSFHFbH/TpIWtwcFGUb1lpPH2qJLXla16L0Xdf5WIPyfF4nKmoDbPOxNmSFmuQn6iZsykPN9CkTPi1rEBe2PfzNcFZZSHt9yTxsKnrea50HylPW2iN3bvA1e1G99dZbqc4V5Cm/vm3rPCpaXn1AxmEL+UYWqGGwjVpmJu37nMg6bCPrZly9p9Fn1DPYQh0Lz4d1pkYgCXmuF1lcU+lqe1A/YFuea0fSirzuLmE7uIFa4jaOH2BT3vLuQz7SaLO028GHdjflWp8usjzVJ+6viM6l7VhUecpiHpF32Ebm/UINgG/ibGeeswpXZP3soEq1P+zzT9etWyfnnHOOXHXVVbGWJU6/9anPx70OM+s+4zuf+kpR2ezjLmzvotS2vKJmZ4sM+CtvtS/t53jAzX4Ac2HnXGyNKV1DPy6WIm7vIq6zb5LeRhwfkQNfpL2dinQfPhlwH9soGbSru9I8PnGxHzCHlT4X+wGKrVqfLFptIJt+ytu5JV/QdmiV1bvkk3L99dfLrrvuKqNHj479WVH3oy61R1KoIWiVtxqydOlSGTRoUMkyDR48WEREFi9eHOqzkjoWd6m9kkKNQXt5qzN5RW7RXh5zm+c5NvKLVnnMbh6RWbRXhNy6vnw2kGs32doucXLKfRZIAtcVbEI/zBbtbw9tiTDi9hef75P4/Oc/L/fcc48cc8wxUl9fb+UzfW6PJJ655mp7RBlTFqm/RGkf2/1nypQpsnHjRrn00kulS5cuFX9n/Pjxctppp4X+7LRkuT/mWAAAkLa8jytM5siKdLzYyqRdGGfQf8rFaSfGHR0x7gAAAL7J+/ixXJj59iKNC6oJ016MN6P/nkix+lsS7cf4tCPGpwCAPCnauKW9Ih0nmmDcUVuR+gvX09nBuAEAUCR53/ckcSzouiTGB1lyZQyQJY7z7eA4HwAA+CIPxw5Z3ZvigqzuX8qSa/coZYn7kOxiHAcAAHyT92OIvB2/myjK+K2WvG13zrvZwXgNAAC4KI/HCVnfj++irJ/xkCVfnuOQJZ7VkA7GhAAAEZFONj7k8ccfl89+9rPS1NQkDQ0NMnr0aPnLX/5S8jv/+c9/ZIcddpCePXtK9+7dZZ999pEnnnii5HdUVX75y1/KjjvuKPX19dK3b185+OCD5eWXXw69TFdddZX07NlTOnXqJF/4whdkwIAB0rVrV+nZs6fsuuuuss8++8jgwYOloaFB+vTpI+eee27b3z7xxBMyZMgQqaurk2uvvbbt53//+99ljz32kB49ekjv3r1l9OjRsmbNmljfFdR+Z5xxhpx11lny2muvSV1dnYwaNart72655RbZbbfdpKGhQXr27CnDhg2TSy65pO3fO3XqJH/+85/lgAMOkKamJhk0aJD85je/KfnuBx98UHr37i1Tp04N3ca/+tWvpGfPntKjRw+ZM2eOHHDAAdK7d2/ZdtttZebMmW2/d/XVV0tDQ4P0799fTj31VBk0aJA0NDTI2LFjZe7cuW2/N3HiROnWrZsMHDiw7Wff//73pWfPnlJXVycrV64MbJMwarX7SSedJHV1dVJXVycjR46U559/XkRETjjhBOnRo4c0NTXJH//4RxER2bhxo1x00UUyZMgQ6d69u3zuc5+TWbNmiYjIz3/+c+nRo4f06tVLli9fLmeddZZss8028sorr0RaZpcEZTpuLkxU6gum/bJVUI6iLIOIWT2r9rcmNTUOk9pQaxmq9etOnTpJXV1d2zZvHWide+65bZ/zu9/9TkRq50aker21lf9W7777rgwbNky6dOki+++/f9vPN27cKNOmTZPPfOYz0r17d9lyyy1l+PDhMm3aNDn88MMjfZfN/WCc/SV1n7ofRZI1v9pxVyX//e9/pXv37jJ8+PC2n9Gn6dNR1Nr/m9TYMMcbqiqXX365fOYzn5H6+nppamqSc845J9Jy19oeQftWk31bpTzGPaartVxh2pGsk/UoTPJXbT8UdExukqmwtcLk+K7acXLYeYxqFi1aJH369JGhQ4e2/Yz8kb8wTPd7NucARdKfmyQX5CIKV/NhOgcSdLxJLshFFFnlotYxUq3t8Lvf/U4aGxulrq5O+vbtK7Nnz5Znn31Whg4dKp07d5ajjz667TvIBJkIyyQPYfqgiTjnN8IIGpdUm6eptnzki3yFRb7M82XK5BiyVl8yaQfTfJnmVYT6Qf0Iz8Z4KYk5QpPl2nHHHY3P3YsEX0OQ5jVY69evl2eeeUbGjBkTuI1szgkF7Stq9XHqC/UlLOpLNvVFRGTEiBEdbmBYtmxZ27+1slVfRILPTVN/qD9powZlV4OCaguZJtNhkefs8ixidixArsl1FGQ722zXOn4n02Q6LPKcXZ4Zb5P3tGWRKxG79ymY9sm45wBF2KeSsWjIWbicVcI9eaXIY3jkMH4OuV+nFDmMziQ7ItGuHav2N2HuA68kynMrTjzxxIo/32effYz6R631zGIca/P5IXHPQYtQf6g/0Ur0XVMAACAASURBVPlYg0Ts3Ve42267tfWRz33uc7J06dKK33fxxRdLv379pKGhQaZMmSIiwfdlxUVtoDZkydfaUCs3UbelSdaDalLcMUmYsVnQMUrUc2umGa4myvFj2CxRN6mbWfGxZppkIo3nTQatp+myipBf8hudjxk2Xe6g45go++cDDjggdr8l82Q+K3nNe7XvCPPsXZ5zXhu5i8e37FX6W9M+/NBDD8moUaPknXfekd///vdSV1cnjY2NoduMfSWZzYpvea2k0ueFmVtLY7uTcTKeBfKd/DaP+vy7NNbB5jlNahg1LEt5qGWmfS7MfUdJvHuHrJP1LJH1dLLOsQv1zAT1LBrf6pgL7yzK6j2P1AhqRNZ8qxci4Z69bHKdd9R3nVZaj0rtEaYvhkH9oH5kybfaEeb54iY1Js653GrrHvb9b9XEfW9m2HvxeIYKtSQO32pJ1HmEMHMvjD82IfP5y3ye8m6yPqZ9M+58SK0+GeddNtXazuazOVpRl6hLWfOtPolUHzvY6Fdkkkxmxbcsxr3HudZyiXD9KHnPd95F/Mu8zWcrxPlsrmGgBuSlBrR69913ZdiwYdKlSxfZf//9234ed6680naO+/5YnrNaigxEE+ZclYj5O+tM2aj9lVR6/mmryZMny/e//33ZaqutIi93nDmurK4Daq9arUvimmtbz8AL83uVUB+pj6ayqouVtneYc3kitWtq1GUQcbu2VVvnan3S9Jm+pusdtr9EQc0Oh5pdrJrdKk4W42y7Sn9bq31rbd8wNb/a+LTaczzCzBuHuSZVxCzrYdaN/JP/MMJkP+y8TqU5l1ZBY0r6Mf04iiT7c7m485T0cfp4GKbbMu42Mhl72hzvkANyEJYrWbDxbmERMkAGwkmr/0d9LgTHQWbo+9EErZPp+YUk5v6Zw2IOi3qQnmrrc9NNN1W9bjBu7pO8H8jWMtbqk0H1sVrWySbZDCvqO8lsn1sy3UZh5hAZ11VHXqKJOuay8W70LN4ln8S7nKu14fr16+WZZ56RMWPGBG4Hm/OgQddl1tp21BBqSFjUkORqiIjIiBEjZPny5SXrvWzZsrZ/a2WrhojEv/abGlMZNSY66kyydSaofpBbchsFuU02tyJm+3XyS37DIrvJZ7fWsTaZJbNRkNtkc8sYmFynLeyz3dK6nrAV91lUR5+PhusK6IcuiPtOj6AaWmu7cBxRGX05nrjvh7D57Isoz6yuxsZxTZh1i/MujaDrdG0oQnvUuv/Xh/Zo/T2TZ2oVsb8k3T5Rn9m3fv16efjhh2WLLbaQPfbYo+Z3hF3nrOtrGu8wifLdAAA3Ma5w4zhaxP4zYYveLkUaZ8RppzCK3E6MOxh3AAAAxo+ujJPiXuMWF+1VrPEm/c299mN8yvgUAFAb4xY3jsOD2iauIrRHkcYdUdonjCK2D+MGxg0AgHjyOq7I6hkFWd6Lk5d1DjM+yPJZFK7cU5PXNuA4n+N8AACKjHFaemMWkXTuTcnbuudt3BanLcLIe1swjmMcBwAAGM+lPaZJ8hqsvKxzHsdvedzuSbcB4zXGawAAFAVjsvTGJ1nfj1+UNsnbmK6o/SbrNmJMyJgQACAiWmbWrFla4cc13XXXXXrxxRfr6tWrddWqVbrXXnvpFlts0fbvX/7yl3XEiBH6xhtvaEtLiy5YsED33HNPbWho0FdffbXt9y666CLt1q2b3nLLLfr+++/rvHnzdNddd9Utt9xSly1bFmqZVFV//OMfq4jo3Llz9aOPPtKVK1fq/vvvryKif/7zn3XFihX60Ucf6cSJE1VE9IUXXmj726VLl6qI6DXXXKOqqmvXrtXevXvrZZddpuvWrdNly5bpIYccoitWrIj9XUHtd+ihh+rIkSNL1u3KK69UEdFLL71UV61apatXr9YbbrhBjznmGFVVnTx5soqIPvzww/r+++/r6tWr9Wtf+5rW19frRx991PY59913n/bq1Ut/+tOfBrbn+PHjVUT0vffea/tZ++/54IMPdPny5brPPvtoz549df369W2/d8opp2jPnj114cKF+vHHH+uLL76ou+++u/bq1UuXLFnS9nvHHHOMDhgwoOR7L7/8chWRtrau1iaqqiNHjtSmpqbAdVE1a/fOnTvrf//735K/O/roo/WPf/xj2/+fffbZWl9fr3fffbe+9957+sMf/lA7deqk//znP0va6PTTT9drrrlGDznkEH3ppZeMljFKHsOaMGGCTpgwIdTfmGY6Ti5MVeoLpv0yKEdxlsG0nlX626C+uWjRIhURvf7660Mtp6p5bQhahkr9esGCBTps2DAdMmSIbtiwoeR7zzzzTL3yyivb/r9WboLqbbX8m5g5c6aKiD7//POqqrp+/Xo99NBDdc6cOSW/N3XqVO3cubPOmTNHm5ub9bnnntMBAwbovvvuG+l7be8HTX+vUl+h7he77kf5/DRqfvlxVyUfffSR9urVSydOnFjyc/q0231aVVVEdNasWca/H1bYzw/a/5vWWNO+M3nyZK2rq9Nf/OIX+t5772lzc7Ned911JfujMKptj6DtaLpvq5THOPk27V9B7UjW3c66q+MW0/xV6vdB7W6aKdO+Z1J7go6Tw85jtFq/fr2+9dZbes0112h9fb3ecsstJf9O/tzOn2v7WdPc2Z4DTHtukly4nQvf90tp58N0nxa0zcgFufAlF0HHSEHbYeHChdqjRw897rjj2j7zggsu0BtvvLHke8iE25mI0l/DSuo4yrQPmopzfsNEUOaC5mkqLR/5cjtfqv6OU8iXubDzIuV9ybQdTPNlmlfqB/Uj7OfbGi/ZnCM0Xa4NGzYYn7s3vYYgrWuw3njjDRURHTNmjO677746cOBAra+v1x122EGvvfZa/fTTT9s+19ackOm+olofp75QX6gvm7heX1RVH330Ue3atateffXVumbNGl2wYIHuuOOOOn78+JJ1sVVfTK7xpP5sRv0JL8q8HzUouxoU1G/JtNuZdvG6PvKcXZ5NjwXItdu5Vk3+fEGUzyfb2WU76PidTLudafJMntvnmfF2KfIeTlJ5TyJXtu9TMO2Tcc8BkjG3M+bqdWbkLFzOynFPXkc+5NHH81vksGMOuV+nOh9y6OK8sGl2olw7FvQ3JveBVxPluRUvvfRS1Z+b9o+g55CkNY41aQNTts5BU3+KXX9Uo9//4WMNsn1f4d57762DBw8uuY7tT3/6k26//fYl33v11Vfr1KlT2/4/qM/EeY4QtaEYtUHVzetQfK0NQZ8ddVsG/V2Y61qjjknCjM1q9f2459ZMM1yp/kU9frS1/ambwaib0T7f15ppmok0njdJfsmvLUU67jFd7qBtGnX/HOceRjJfjMwzD5t+3it9R5RnqfGc88p8yJ2qm9ff+Jq9Sn9r2odVVQcMGFDybIMw2FcWI7Pk1V5eK81DlX9emLm1pGs1GS9GxpM+Hi7K/JVP+Y7z/Ls01iHo70xRw4pRw1QZ1yddy0z7nGlft/3uHbJenKwzLilu1jl2oZ7lqZ5x/YCdOubCO4vSfs8jNaIYNUKV67dt1wvT67JNr6mM867TSutRqT1M+6Ip6kdx6gfXOdmrHab90LTGxDmXW77upu9/C2L7vZmm9+JRS9yvJa6+N8XHWhJ1HsG0nzP+IPPt28jV44ei5910fUz7Zpz5EJM+GfVdNtWOdWw+m4O6VJy6xLyIvfoUNHbg/goyGYR5BnvHCnHucU7qHnRT5L0YeWf/ay/ztp6toNqxJti89sEUNaAYNcDF+cCZM2eW9KP169froYceqnPmzGn7HVtz5ZW2c9T3x/Kc1Y58yICqm9clmm5P0/PcpmzV/lZBzz9VVX3iiSf0oIMOUlXVFStWqIjo5MmTQy+7arw5rrSvAzKpdar2r7m2/Qw809+r1FbUR7fro2vnD7Kqi0HPkK3V94JqapxlcLW2mexHKvVJ0z5vut6m/cUUNZua7XrNVnXzvjrT7Vmpz8U5h1Grdpe3r+kzrGr1XZPxaaXneJj04yjXpIa95isol+Tf7fy7OK9jsi3DzutUm3NRNRtT0o/d7seq7p6nSKI/VxNnnpI+7nYfd/G8u+m2jHNMFrRtbI93yIHbOVD1ex4+ySzYercwGXA7A74et6tG7/9xnguhynGQCR/6vqvH+dXWyfT4JMm5f+awmMNqlbd64OK4qNr6VOqvtnKf5P1ASdYm0/pYqe3IptvZdG2cFvedZLbPLZluI9P+x7iuOvJiPy9R28L0GC2rd8nbfJdzrTZ84403VER0zJgxuu++++rAgQO1vr5ed9hhB7322mtL3hVrax7U9LpMnt/RkQ81xLXrOaghydYQVdVHH31Uu3btqldffbWuWbNGFyxYoDvuuKOOHz++ZF1s1RBb135TYzryocaoujfups4kX2eC+ia5Jbfk1r3cmu7Xya/b+XXtvC/ZTT67QcfaZNbtzKoyr1fE3DIGLpW3XPs2zxblvk5b1xOqcp9FEB/6vKp740dVriuo1Q6q9MMobPVDW+1leq0exxGb+dKXXRvXq9p/P0TcZ19EeWZ1NbaOa0zXLe67NILWvb0999xTP//5zxu1QxHaQ7X2/b++tYfJmLLI/cV2+6jGe2bfq6++qiKie+21l1njRljnrOprWu8wifLdptK4nwQA8ibKuIVxhTvH0VGefZi340Vb7aJavHEG/Se5dlJl3KGa33FH0vPXAAC4ivFjKZePcyuNk+Je49Ze3sYFSbeXavHGm/Q3d9pPlfGpKuNTACgarqfbzOXjokrHMWGeZ5e348S47aFavHFHkfuL7fZRZdygmt9xA9fTAUA0jCs2yfoZBVnci5OHdVYNNz5wYZ2zvqcmj22gynG+Ksf5AADkBdetbebCsWtW9zblad1V8zluK0o/4D4kxnFc5wUAgDnGc5u5cCyb9jV7eVhn1XyO3/K+3bm+jvFaGIzXAAB5FuX6EsZk6Y5PsrwfvwhtoprPMV3R+w3PanCn3uZhTJjG+0MAIEfu7DC6sHFR+7Rp01REdPny5aq66aKb8gOUefPmqYjo2Wefraqqzc3N2tjYqEceeWTJ7/3jH/9QETF6eVe51pedfvjhh20/+/3vf68iovPnz+/wHXfccUfbz8pfirdgwQIVEb3vvvusf1e58vYr33GuX79e+/Tpo+PGjSv5uw0bNuhVV12lqpt32uvWrWv795tvvllFRBcsWFD1u2up9XK09t/TepD2n//8p+1np5xySoeXlv3zn/9UEdGf/OQnbT9L8+Vo5crb/W9/+5uKiE6ZMqXtdz744APdbrvt2l7Iu27dOu3Ro0dJv21ubtb6+nr93ve+p6qV28hUGjeZRDl4Msm0qt1cVFOpL5j0S5McRV2GMPXMZBKkvG8uWrRIRUSvv/76UMupGr02lC9DtX7dOki5884723720Ucf6ZAhQ/SDDz5Q1eDcBNXbMA/nKTdz5sy2QWRLS4seddRR+sADD3T4vd1331332GOPkp+dfPLJ2qlTJ/3kk09Cf6/N/WCY/hWnr6hS91XzV/ejfH4aNb/Sy4jLTZ48Wbfffntds2ZNqOVvjz6dfp9WTf7kfZjPD9r/h6mxJn2nublZe/TooV/5yldKPq/9/iisSt9rsh1N922V8hg131H7V6UMhkHW87f/Ug0/bgmTP5P9UHm7m2bKtFaY1J6g4+Sw8xitBgwYoCKiW2yxhf7v//5vyUR/WOQv/fy5tJ81zV0Sc4DlXJmbVCUXquyXVN3Oh8k+zWSbhUEuyIVqdrmodYxk2tdvuOEGFRG99dZb9fbbb9dJkyaFWoZyZCL9TKRxUUkSx1GtbPbBOOc3TNTKnMl5mjjnA1TJlyrzgeQrmXzFmRcJ0w6m+TLNaxjUD+qHqr3xks05QtPlUjU7d19t+SpdQ5DWNVjz589XEdGvfOUr+uSTT+qqVav0/fff1/PPP7+tRlcTdU7IdF8Rt4+rUl9UqS+q1Jes6kurCy+8UEWk7b9tt91Wly5dWvX3VaPVF5Nz09SfUtSf8KLM+1GDsq1B7ZX32zDIdP7m2cnzJr7k2fSayjDIdTb76qTPF0T5fLKdTbZt3luiSqZV83f+jzxv4kOeGW+T97iSyruq3VwlcZ+CaZ+Mew4wDDKWv/GvKjnLImfckxfMxTwmPb8c9vPJYbQccr+OORdz6OK8sEl2olw7ZvI3JvffmTJ9bkW1n5v0j0rrWe0zkx7HmrSBCZvnoMOg/uSv/qhGOy73tQbZvq9wxowZKiL6yCOPtP1swoQJKiL61FNPtf1s77331sWLF6uqWZ+J+mwYakNxaoOqm9eh+Fgbgj476rY0+TvTc81xxySmY7Ny7ft+3HNrphk2qX+mx48mqJvUTZvCfr6PNTNMJpJ+3iT5Jb82FeW4x3S5ozxLwnT/HPUeRjJfnMwzD5tu3qt9R5x7hnnOeW0u5k7VzetvfM1epb8N82zNAQMG6HHHHRf6e9lXFiez5NVeXivNQ1X6PBvP1q/WTqbIeHEynvTxcBHmr1T9ynfc598luQ62njVJDStODVNlXF/+N7ZrmWmfM+3rNt+9Q9aLlXXGJcXNOscu1LM81bOkzz8WZfzlwjuL0nzPIzWiODVCleu3K/1NnOuNTK91NLmmMmj7h32Hk2rlvm16n4oJ6kex6kfSxxlFmh8x6Ydhakycc7nl6x72+YKVJP3ezFo/D4Naks/5F9XijFuiziNEfY8q4w8yHwXzFMnl3XR9VM37ps37JMr7ZJx32ZS3ne06QV0qVl1iXqTj30StT0FjB+6vIJNBmGewd6wQ9R7npO5BN0Xei5N39r8d/yZO5m08W0G18rje1rUPJqgBxakBLs4Htu9HLS0tetRRR+kDDzxQ8js25spVq2/nKO+P5TmrwVzMgKqb1yWabk+b76lJovYHPf+0ublZd9ttN33rrbdUVXXFihUqIjp58uRQy94qzhxXmtcBqZrVOlW712Gq2n0GXpj2jdNWqtRH1fzNC4T9/Czqoqr5M2TL+57N9w35UttM1jnOM33DrLftd8pTs8OhZufz/EqUzzfdnpX6XNRzGNX+1rR9TZ5hVd53TcanlZ7jYdKPw16TGveaL5NrU2oh/8zrqJpty7DzOtXmXGyPKVXpx6r53I+pJjc/4+I8ZS308fyNr5M8TotzTFaufNvYfk9DGOSAefhWLmTB1ruFwyADHLerJt//4z4XQpXjoLBc7PuuHudXWyeT45Mk5/5rLVs55rCYw6rFxXrg4rio2vqU91ebuU/qfqCka5Pp+de47xMlm/k7Tg37+XHfSWb73JLp8z5M+h/junDIS7C4Y6647yHM4l3yqnbf5VyrDefPn68iol/5ylf0ySef1FWrVun777+v559/fts4tZqo86Cm12Xy/I5gLtYQ167noIYkW0NaXXjhhSoibf9tu+22unTp0qq/rxqthti89psaE8zFGqPq3ribOpNOnWmvvG+GQW7JrSq5TSO3tu9xUCW/qvmbH1ANVx/IbrLZtTn3rkpmVbleSZXcJp1bxsD5z7VP82yq0e7rtHU9IfdZhOdin1d1b/yoynUFreiH9tjqh7baq5zJtXph0ZfzOT5QDZeXpN8PYePZF0HPrK7F1nGNybrZeJdG0Lq3t+eee3bY7wXJc3u0fk6YZ6652B5xnqdYhP6SVPu0LmfUZ/Y9++yzKiL6P//zP8Z/40N9tbV9k/puU2ncTwIAeRN23MK4wq3j6CjXBeTleLHa56lGv16iaOOMIvefpNupdXkZd+Rz3JH0/DUAAK5i/FjKxePcap9n+xq3vIwLqn1eEtcEFmm8SX/bxJX2a/09xqeMTwGgSLiebjMXj4uqfV7YtsnLcaKt9mj9nKKMO4raX5Jqn9blZNyQz3ED19MBQDSMKzbJ+hkFWdyLk4d1bv0c0+O7LNfZlXtq8tgGrcvFcT7H+QAA5AHXrW3m6pgljXtT8rTurZ+Xt3Fb3vsB9yExjqv03aa4zgsAUFSM5zZzcUyT9DVYeVjn1s/J0/gtz9ud6+sYrzFeAwCgVNjrSxiTpTs+yfp+/CK0Sevv5WlMV+R+w7Ma3Kq3eRkTpvH+EADIkTs7SQK6du0qIiIbN26s+jujR4+WpqYmmTdvnoiIvPjii7J27VrZbbfdSn5v9913l27dusncuXOtLFu3bt1ERGTDhg0dlrelpaXq340YMUL69+8vxx57rFx88cXy5ptvJvZdQe03b948ef/992X8+PElP+/cubOcfvrpgZ9b67ttaF3voO/ZbbfdpEePHvLyyy8nujymytt9v/32k+23315+85vfiKqKiMgdd9whRx55pHTu3FlERF555RVpbm6WnXfeue1zunfvLgMHDnRmvdJSnulqouYirvJ+GTVHJmzXM5OaGoeNutTqpJNOkqamJrnqqqvafnbrrbfKwQcfLL179xaR4NxEqbdhbdy4UY4++mjp37+/7L///h3+/eOPP27Lffu/6dq1a1v+44q6H0xrfxkGdb94dT/tmn/vvffKnXfeKX/5y1+kV69eEZY4HPp0fvt00P4/bo0t7zv/+c9/pLm5Wb785S9bXIuOTLaj7X2bSb6j9i/TDMZF1vObdRH7+Stv9ziZKu97prUn7HGy6e8vXbpUli9fLrfffrv8/ve/l1122UWWL19e87PjIn/5zJ9p7tIY07g8N1kNuchnLlq5nA+TfVpW24xckAsR+7modYxkuh1OPvlkmTBhgpx66qly5513ys9//vNQyxAVmchvJsKOX5Lsg2lmLsnzNGGRL/LVinyZiTMvktbYK628Uj/yWz9Ekh0vRZ0jNF0uEbNz99WYns9L4hqs+vp6ERHZaaedZOzYsdKvXz9pamqSn/zkJ9LU1CTTp08PXO4wc0JpncsJi/pCfaG+JHON5+TJk2X69Ony8MMPy9q1a+X111+XsWPHyhe/+EVZunRp4HKHqS9Bx2TUH7uoP+aoQdnVoGrLk9R1oSJkOu+ZJs/u5FnE/JrKuMh1vnMtQrbbL1/QetjMdlbnLMh0vjNNnrPJM+Ntu8i7mSxylVZfN+2TXH8ZDRkzR86i54x78syQx2DkMFoOuV/HHDmMrjw7UeY50p4biXuOxKR/RFmepMaxtb4zTBu4fg6a+lO8+iPiZw0SiXdf4RFHHCE9evSQm2++WURE3nvvPXnttdekvr6+7WdvvvmmdOvWTYYMGSIiyfYZakMyqA3xuF4bgj476raM+nem55rD1KqoY7P2fd+lc2s2r7GhbiaDuhmd6zUzbiZsjqPIbzLIbzyuZ7gaG88IS/oaWDKfDDIfXV7yXk2ce4Z5znlt5C4eX7NXie1na7KvTAaZjS5PebXxbP24yHgyyHg05NvuNo/7/Lsk18HWulPDkkENiydPtcxUpb5u8/0EZD0ZZD0esr6Jzaxz7EI9i4p6Fo2vdczFdxbZOpdJjUgGNSI+1+tF3PuNy6+pTOtdpzbvU6F+JIP6EY/rtaOa8n6YxDMNTDJho9Yk/d7MtFFLqCUi/tSSJMckjD9Kkfn88DXv1di+zjHqfRJBNSPO8YDtOkFdSgZ1KT7X65OteYpyZDIZZDI617MYR1L3oNv6fvIeDXmPx4fM23i2QpzP5hqGUtQAv23cuFGOPvpo6d+/v+y///4l/5bU8W6rsO+P5TmrZshAPKbbM+p7apKo/UHPP/3hD38oJ598smyzzTahltWU7XNeSTzToFatE7F7HWY1UZ+B5+I5Repjsepj0nUxjPK+5/L1UOXSOsdYi0mfj7veNnJPzbaLml2smt3Kte1ZzqQmlvfdJMenYT87btZtP4ck7ve41l/If3Tl2zJM364155L0mLIW+nHx+nGrOP05irDzlLbQx4vXx5PelibHWXHe05AEclC8HIikn4Uk3y0cFxkoXgZsbsu4z4UQ4TgoLPp+MsqPT1y5zpA5LOawaqEe2JV07m3cD+TCe/bSRDbzm02X3kkmEu95H+X9j3FdOOQlmI0xV7kwx2hZvEu+mqj70lptWF9fLyIiO+20k4wdO1b69esnTU1N8pOf/ESamppk+vTpgcsdZh4062vyq6GGUENaUUM6ChoHT548WaZPny4PP/ywrF27Vl5//XUZO3asfPGLX5SlS5cGLneYGuL6fT/VUGPyW2NEqDPtJVVnqi1PUu9tFCG35JbcBv1tlDn4tObYyG9+80t2N0siu1mdFySz+c2sCLltL4ncMga2i1wHs3EdRlLXE3KfRXj0efu4riA8+mE8SbVXGnNv1dCX892Xk34/hI1nX8Tp/0neJ1G+bknce2Q7+3lujyjPXHOxPeKMKYvQX5Jqn7jP7GtsbBQRkebmZuO/8aG+ZvkOkyLukwHAZ4wr3D6OFklmjqwI7cI4Y5Oi9J+k24lxB+MOAADA+FHEnXGSC9e4Fbm9ijbepL9t4kr7MT5lfAoAqI1xizvH4WncU5fn9ijauKOo/SWp9mHcwLgBABBPnscVWT+jwJTNeyvysM5hj++yXGdX7qnJYxtwnM9xPgAARcY4Lfsxi4j9e1PytO5FGbeJ5KsfcB9SR4zjAACAbYzn0h3TJH0NVh7WOY/jtzxvd66v24zxGgAAiIIxWbrjk6zvxy9Cm+RxTFfkfsOzGtyqt4wJAaCYOtn4kD//+c+y7777ylZbbSX19fVy7rnnGv1d165d23Yg77//vohs3tG216dPH/nwww9tLGpk3bt3l0ceeUS+9KUvydSpU2XEiBFy5JFHyrp162J/dtj2W7NmjYhsahff1dfXy4oVKzL57qB2r6urk1NPPVVef/11efjhh0VE5Oabb5YTTzyx7Xc++ugjERG58MILpa6uru2/xYsXhzpYzIv2mXZdkjmKW8+i1lSboi5DY2OjnHzyyfLUU0/JP/7xDxERuf7662XixIltvxOUmyTrbavTTjtNFi1aJL/+9a9l4cKFHf79a1/7mjz33HMyZ84cWbdunTz77LMye/Zs+cY3lF06bwAAIABJREFUvlHyku64ouwHXd9fBqHu50daNf+OO+6Qn/3sZ/Loo4/KsGHDEv++sOjTfgna/9uusW+99ZaIiGy11Vah/i4sk+2Y1r4t7HL5gqz7J27+gtrdZqZMa0/Y42TT3+/atatstdVW8tWvflXuuOMOefHFF2XatGmh1iFJ5M8fprlLYkxThLnJ9siFf1zOh8k+zYdtRi78k1Uuah0jhdkOU6dOlbVr18ry5ctDfX9ayIRfooxfkuqDaWbO1/Od5Msv5CuZfMWZF0lz7JVlXiuhfvgnzfFSmGyYZtDk3H1WatWwQYMGiYjIypUrS/6mW7duMnToUHnttdfafmZjTiitczlJor74h/qSnFr15Z133pHLLrtMTj75ZNlvv/2kZ8+eMnz4cJkxY4a8/fbbcvnll7d9jo36EnRMRv2Jh/oTHTUoOUHnZ124LrQWMu0f8pycqNcxu3YdPbn2E9lOju/nLMi0f8hzcmrlmfF2POQ9mixylWZfN+mTXH9phoxFR86i5Yx78qojj+GRw2g55H6d6shhcqLMcyQ9N2L7HIlJ/3CNjTbI0zlo6k9+uViDqol6X2GvXr3kkEMOkXvuuUeam5tl5syZcuKJJ8qBBx4os2bNkk8++URmzpwpxx57bNvfJNlnqA12UBuS5VptCPrsqNsyTh8wOdccplaZjs1q9f0sz60leY0NddMO6mZyXKuZLmWC/NpBfpPlWoZNmWzTtK+BJfN2kPnk+Jr3arJ4PmGSyF1+5S17cbCvtIPMJsfnvNp4tn5cZNwOMp4M8h1P3OffJbkOttadGmYHNSxZPteyOGyO/8m6HWQ9WWQ9ftY5dqGemaKeJSNPdSzp7ZvUuUxqhB3UiOS5Vi9s3G/c/prKtN51avM+FeqHHdSPZLlWO2pp3w+zeqaBjVqT9nszXUItyS9faonNbcj4IxiZzydf8u6asDUjzvGA7TpBXbKDupQ81+qTrXmKcmTSDjKZHNeyGEdS96Db+n7yboa8J8vFzNt4tkKcz+Yaho6oAf467bTTZNGiRfLrX/9aFi5cWPJvSR3vtmf6/lies1odGbDPdHtGeU9NErW/1vNPn3jiCZk/f76cdNJJoZYzjLjnvNJ4pkGtWieS3j3XUZ6B5/s5RepjPiRZF+Nw+XqorM4x1mLS523UnLi5p2Znh5qdL1luz3I2amKS49Own+171ish//nRflua9u1acy5pjCltoR/nT5T+HIfpPGVW6OP5YXNbRj3OivqehqyRg3xJMwtpvls4SWQgP2xty7jPhWjFcVB19P30tD8+yeo6Q+aw3Ec9yC9Xri+utZ1deM+eq8imX7J8J1klcZ/30b7/+bAtyYtfbI252gtzjJbFu+Rtq9WGgwYNEhGRlStXlvxNt27dZOjQofLaa6+1/czGPKhP1+RXQw3xCzUkvlpt+M4778hll10mJ598suy3337Ss2dPGT58uMyYMUPefvttufzyy9s+x0YNydN9P9VQY/xDnYkvaL4t7fc2hkVu/UNu44s6B+/aHBv59QvZjc+1OcmwyKx/yG18tdqQMXA85Dq8KMeAaVxPyH0WZujz6XBtzFMJ/TA/bLWX63Nv1dCX/ZP2+yFMxrlh+/99991Xsr3q6urk2GOPFZF075Ow0VY2sl/E9qh1/69v7RFmTFnE/pJE+9h4Zt+wYcOkoaFBXn31VeO/8aG+ZvkOkyLukwHAZ4wr3DqOribKHFmR26Wo44xqitB/kmwnxh3VMe4AAKBYGD+6M05K6xo32qujIo436W+bZd1+jE+rY3wKAGjFuMWd43Bb99QVsT2KOO4oan9Jon0YN1THuAEAYCrP44qsn1Fga/2KtM5RxgdZrrMr99TkrQ04zq+O43wAAIqBcVr6Y5ZqbN6bkpd1z/O4rZq89APuQwrGOA4AAMTFeC7dMU3S12D5vs55Hb/lebtzfV11jNcAAIAJxmTpjk+yvh8/722S1zFdkfsNz2rYxJV6y5gQAIqpU9wPWLJkiXzzm9+UgQMHyty5c+WDDz6Qyy67LPDvNmzYIKtXr5YhQ4aIyOadWqWd4vvvvy/bbrtt3EWNbaeddpI//elP8vbbb8t5550ns2bNkiuuuCLWZ0Zpv6233lpERFauXBnru7PW0tKS6rZ97LHH5MorrxQR83Y//vjjpaGhQW688UZ55ZVXpHfv3jJ06NC2f289QL3yyitFVUv+e/rpp1NZL1eUZ9p1SeYoTj2LWlNtirsMEydOlK5du8qVV14pjz32mAwePFhGjhzZ9u8muUmi3rZ3+OGHy0MPPSR9+vSRb3/727Jhw4aSf7/44otlv/32k+OPP1569+4thxxyiBx++OEyY8YMa8sQdT/ow/6yGup+fqRV86+55hq59dZb5ZFHHmmr2y6hT/snaP9vu8Y2NDSIiMgnn3wS6u/CMtmOaezboiyXD8i6n+Lkz6TdbWYqTO0Je5wc9vdHjRolnTt3lhdffDH0eiSB/PnFNHe297dFmpsUIRe+cjkfJvs017cZufBTVrkQqX6MZLodWlpa5PTTT5df/vKX8vTTT8uUKVNCL0OSyIR/wo5fkuyDaWbOx/Od5Ms/5CuZfMWZF0lr7JV2XoNQP/yU5ngpTDbCZDDo3H2WqtWwxsZG2W677WThwoUd/mbDhg3S1NQkIvbmhNI6l5MU6oufqC/JqlZfFi1aJBs3buxw7UHv3r2lX79+bedGbNWXoGMy6k841B97qEHJqlaDXLgutBYy7SfynKyw11u4dh09ufYX2U6Wr+csyLSfyHOyquWZ8XY45N2OLHKVVl837ZNcf1kZGbOHnIXPGffklSKP8ZHD+Ps77tchh2mJMs+R5NxIUudIgvqHS2y1QV7OQVN/8s21GlRN3PsKTzjhBPnwww/lD3/4g8ycOVOOPPJIOeGEE+S9996T++67T2bPni0TJkxo+/0k+wy1IRpqQ7pcqw1Bnx11W0b9O9NzzWFrVdDYzKTvZ3FuLelrbKib0VA30+NazXQpE+Q3GvKbLtcybCpom2ZxDSyZj4bMp8fXvFeTxfMJk0Lu8i1v2YuDfWU0ZDY9vufVxrP14yDj0ZDxdJDv+OI+/y6pdbC17tSwaKhh6fK9lkVlc/xP1qMh6+ki63bm+jh2oZ5VQj1LR57qWJLbN8lzmdSIaKgR6XOtXsS9/6r8mso033Vq6z4V6kc01I90uVY7qinvh1k+0yBurUn7vZmuoJbkmy+1xNY2ZPwRjMznly95d0mUmhHneMB2naAuRUNdSp+L9cnWPEV7ZDIaMpkeF7MYVVL3oNv6fvJeGXlPl6uZt/FshaifzTUMpagBfjv88MPloYcekj59+si3v/1t2bBhQ8m/J3G828r0/bE8Z7UUGUiW6faM+p6apGt/+fNPb7rpJnn44YelU6dOUldXJ3V1dW3be+rUqVJXVyfPPvtspO9qFWeOK61nGgTVujTuuY76DDyfzylSH/Mh6boYh6vXQ2V5jjFIUJ+PW3Ns5J6anQ1qdr6kvT1rsVkTkxyfhvlsn7NeCfnPj0rbMqhvB825pDGmtIF+nD9R+nPc7zOZp8wKfTw/bG7LqMdZcd/TkBVykC9pZyHNdwsnhQzkh+1tGfe5EBwHlaLvZ6P8+CSL6wyZw3If9SDfXLi+OGg7u/CePReRTT9l8U6yWqI+76O8/7m+LcmLn+KOucqFOUbL4l3ySajWho2NjbLddtvJwoULO/zNhg0bpKmpSUTszYP6ck1+NdQQP1FD4qvWhosWLZKNGzd2OMfdu3dv6devX9s11LZqSF7u+6mGGuMv6kx81dowi/c2hkFu/UVu4ws7B+/aHBv59RPZjc+1OUlTZNZf5Da+am3IGDgccm1HmGPAtK4n5D6Lyujz6XNtzFMJ/TB/4raX63Nv1dCX/ZT2+yGCvi9K///GN77RYXvdeuutIpLufRJx28pW9ovWHkH3//rWHqZjyqL2F9vtY+uZffX19TJ+/HhZuXKlPPnkk1V/b/Xq1XLSSSeJiB/1Nct3mBRxnwwAPmNc4c5xdDVR58iK2i5FHWdUU5T+k1Q7Me5g3AEAADZj/OjOOCmta9xor1JFHW/S3zbLsv0YnzI+BQCYYdziznG4rXvqitYeRR13FLW/2G4fxg2MGwAAduR9XJHlMwpsrV9R1jnO+CCrdXbpnpq8tAHH+RznAwAAxmlpj1mqSeLeFN/XPc/jtmry1A+4DykY4zgAABAX47l0xzRpXIPl6zrnefyW5+3O9XXVMV4DAAAmGJOlOz5x4X78vLZJnsd0Re43PKthE1fqLWNCACimTnE/YP78+dLS0iLf+973ZMSIEdLQ0CB1dXWBf/d///d/8umnn8quu+4qIiI777yzNDY2dngR4ty5c2X9+vXyhS98Ie6ixvL222/LwoULRWTTjuvSSy+VXXfdte1nUUVpv2HDhkm/fv3kr3/9a6zvztqjjz4qqip77bVX28+6dOkiLS0tiXzfc889Jz179hQR83bv27evHHHEETJ79my54oor5Dvf+U7Jvw8ePFgaGhrkhRdeSGSZfVKeadclmaM49SxqTbUp7jJsu+22cvjhh8vdd98tP/rRj+SMM84o+feg3CRVb9sbN26cbLnlljJ9+nR57rnnZMqUKSX//uKLL8prr70mK1askJaWFlmyZIn86le/kr59+1pbhqj7Qdf3l7VQ9/Mj6ZqvqnLeeefJ/PnzZfbs2dLY2JjI98RFn/ZP0P7fdo3deeedpVOnTvL3v/898jKbMNmOaezboiyXD8i6n+Lkz6TdbWbKtPaEPU6u9furVq2So48+usPfLFq0SDZu3CiDBw8OvR5JIH9+Mc2d7f1tUeYmW5ELP7mcD5N9muvbjFz4Katc1DpGMt0OP/jBD+Q73/mOnHnmmTJp0iS55JJLnLqogUz4J+z4Jck+mGbmfDzfSb78Q76SyVeceZEw7RAnX5XymiXqh5/SHC+FyUaYDAadu89K0FznEUccIc8//7y8/vrrbX/T3NwsixcvltGjR4uI3TmhNM7lJIX64ifqS3Jq1ZfWmzXeeeedkr/58MMPZfXq1W3nRmzVF5Nz09Qfc9Qfe6hByalVg1y4LrQWMu0n8pycKNcxu3YdPbn2F9lOjs/nLMi0n8hzcmrlmfF2OOTdjixylVZfNz3nxvWXlZExe8iZec64J68y8hgfOTTPIffrVEYO0xNlniPJuZGkzpEE9Q+X2GqDvJyDpv7km2s1qJq49xWOGzdOhg4dKlOmTJH+/fvLFltsIePHj5dBgwbJj3/8Yxk+fLj07t277feT7DPUhmioDelyrTYEfXbUbRn170zPNYetVUFjs6C+H/fcWtQMJ32NDXUzGupmelyrmS5lgvxGQ37T5VqGTQVt0yyugSXz0ZD59Pia92qyeD5hUshdvuUte3Gwr4yGzKbH97zGfbZ+XGQ8GjKeDvIdj43n3yW1DrbWnRoWDTUsXb7UMtt9zub4n6xHQ9bTRdbjZ51jF+pZNdSzdPhSx0wkuX2TPJdJjYiGGpE+1+pF3PuNy6+pTPNdp7buU6F+REP9SJdrtaOa8n6Y1TMNbNQak9rgw/MawqKW5JsvtcTWNmT8EYzM55cveQ8jyb4pEq1mxDkesF0nqEvRUJfS51p9sjlP0R6ZjIZMpse1LIpE71tJ3YNu6/vJe2XkPV0uZl4k/rMV4nw21zCUogb4bdy4cbLlllvK9OnT5bnnnpMpU6a0/VtSx7utgt4fy3NWKyMDyTJ9TmnU99TYqv2mzz/97W9/K6pa8t+KFStERGTy5MmiqrLbbruFWodKyxx1jiutZxrUqnUi6dxzHfUZeD6fU6Q+5kPSdTEOV6+HyvIcY5CgPh+35th4pzw1OxvU7HyxkUVbbNXEqONTk34c9rN9znol5D8/yrdlrb5tOueSxpjSBvpx/oTpzzYEzVNmjT6eHzaP06IeZ8V9T0NWyEG+pJ2FNN8tnBQykB82+7+N50JwHFSKvp+N8uOTLO5JYA7LfdSDfHPhXqSg7ZzFMmZx/jUssumfuO8kS0LU532U9z/XtyV58Y+NMVe5MMdoWbxL3ragY9gjjjhCnn/+eXn99dfb/qa5uVkWL14so0ePFhG786A+XJNfDTXEP9SQ+Gq14bbbbisiIu+8807J33z44YeyevXqtmuobdWQvNz3Uw01xk/UmfhqtWEW720Mg9z6idzGF2UO3rU5NvLrH7Ibn4tzkqbIrJ/IbXy12pAxcDjkOr6wx4BpXU/IfRaV0efT59qYpxL6Yf7EbS/X596qoS/7Ke33QwR9n+3+n+Z9EnHbKo3s56k9bDxzzcX2MB1TFrW/2GqfJJ7Zd/HFF0t9fb1MmjRJ1q1bV/F3FixYIF26dGlbF9fra5bvMCniPhkAfMa4wp3j6GqSmCPLY7sUfZxRTVH6j+12YtzBuAMAAHTE+NGdcZIL17gVqb2KPt6kv22SVfsxPmV8CgAIh3GLO8fhadxTl6f2KPq4o6j9xVb7MG5g3AAAsCvP44qsn1FQSdL3Vvi4znGP77JcZ1fuqclDG3Ccz3E+AADYjHFaumOWamzfm+Lzuhdh3FZNnvoB9yEFYxwHAADiYjyX7pgm6WuwfFznIozf8rzdub6uOsZrAADABGOydMcnWd+Pn8c2KcKYrsj9hmc1uFVvGRMCQDF1ivsBQ4YMERGRv/3tb/Lxxx/LokWLZO7cuR1+b/369fLBBx/Ihg0b5F//+pdMnDhRhg4dKscff7yIiDQ0NMhZZ50l9957r9x6662yZs0amT9/vnz3u9+VQYMGySmnnBJ3UWN5++235dRTT5WXX35Z1q9fL88//7wsXry45MVeUZi0X79+/eTtt9+WN998Uz788EPp1KmT/PCHP5THHntMJk6cKP/973/l008/lQ8//LDmC88reeCBB6R3794yderUWOth4tNPP5X33ntPNmzYIPPmzZMzzjhDhgwZ0tYHRERGjRolq1evltmzZ0tLS4usWLFCFi9e3OGzytuk1gvVWlpa5N1335VHH3207eVopv1WROS73/2ufPLJJ3LffffJgQceWPJvDQ0NcsIJJ8jMmTPlV7/6laxZs0Y2btwob731lrzzzjthm8grQZlOS5i+0F59fb21HJUvQ+fOnY3rWfnfDho0SETM+mZSwuSjmrPOOks2bNgg7733nuy3334l/xaUm6B6G3WbV3LQQQfJ8ccfL1OnTpXnnnuu7eennXaaDBkyRNauXRv5s8vZ2g/G3V9S96n7UaRd8xcuXCg///nPZcaMGdK1a1epq6sr+e+KK65o+136NH06jKD9v+0xyVZbbSWHHnqo3H333XLTTTfJmjVrZN68eTJ9+nSr62WyHZPYt9lYLlNknayHFSd/Ju1uM1OmtSfsvESt3+/Zs6f89a9/lUceeUTWrFkjLS0t8vzzz8txxx0nPXv2lEmTJrV9Dvkjf6ZMc2d7f5vF3CS5IBdhuZwPk32ayTYjF+QirKxyUesYyWQ7XHfddbLNNtvIIYccIiIi06ZNk89+9rNyzDHHyJo1a9q+h0yQiTDCjF9M+6CpOOc3TNTKnMl5mkp9knyRrzDIV7h8mYozLxJm326aLxGzvFI/qB9h2RovmQiTjbAZrHXuPitBc52TJk1qm7dZsmSJrFq1Ss477zxZt26dnH/++SJib07Ixrkc6gv1JSzqS3Jq1Zfhw4fLuHHjZMaMGfLYY4/JunXrZOnSpW1tcOKJJ4qIvfoSdExG/aH+ZIUalJxaNcik35JpMh0WeU6OyfUZJtdUkmtyHQXZTk7cOVUyTabDIs/JqZVnxtvkPQtZ5Cqp+xRM+mQlXH9ZiozZR87Mc8Y9eaXIoz3k0DyH3K9TihymL8q1YzafX1POxnMrqqnVP0TsPpMiDlttYPMcNPWH+pMU12pQK9vPPKurq5PjjjtOXn75ZTnuuONERKRz587yrW99S1588UX51re+VfL7SfYZagO1wQeu1QaTZ45E2Zamfxf1+T1R7rGrNTYL6vtxz62FuRcgzHLFRd2kbrrOtZqZ1nOTTJBf8usD1zJsKmibxtk/J/WcWDJfisynz9e8V2P7+YTkjtwlJW/Zi4N9JZl1XR7yGufZ+nGRcTLuMvIdT9zn3yW5DrbWnRpGDfOBL7Us6vm2amyO/8k6WfcBWY+fdY5dqGflqGfp8qWOmbC5fdN8zyM1ghrhC9fqRdhrHU2u847zrtNKavXtoPtUTFA/qB8+cK12tArqh2m977583RcvXhz7PfZpvzezPWoJtSQprtaScra2YZL3OnD8QOZd50vew7A9N1vOpE/afJeN7Xs/qEvUJV+4Vp+C5im4v4JM5pVrWRSJvq9P6h50U+SdvPvAxcy3ivNshTifzTUM1IA8Ouigg+T444+XqVOnynPPPSciZu/uMFFpO5u8P5bnrJYiA8kwfU5p1GcHlbNV+8M8/9S2OHNcaV4HVEmlWidi/55rEXvPwIt7TpH6SH0MK+26KJLcHFqcZXC1ttlY56A+H6bmRH3etwlqdrKo2fkSNYtxnuFt+re2rruIOj416cdhPzuJa77IP/mPImhb1urbYeZcTNGP6cdxxOnPYUSdpxShj9PHozHZllGPyUy3jc33NJADchBV1lmw9W5hMkAGokiy/8d9LgTHQZvR99MVdHyS5Nw/c1jMYVEP3GQz91EFbec0alPU869kk2yGEfedZEmdWzJ53ofJ8wgY15EXm2w9i6+9MMdoWbxL3ragY9hJkya17euWLFkiq1atkvPOO0/WrVsn559/vojYmwe1cS8uNYQaEgY1JL5abTh8+HAZN26czJgxQx577DFZt26dLF26tK0NTjzxRBGxV0Ns3vdTDTWGGhMWdSa+Wm1o0jfJLbkNi9zGZzJPbjLHRn7JbxhkN764c5JklsyGRW7jq9WGjIHJddrCPtvN5L5OW9cTmqLP0+ej4LqCTeiH2Yp6jj+oveJcq0dfpi+Hlfb7IYK+L8ozq2v1lzSPa+K2VZLP+G+Vp/awcf+vi+1hOqYsan+x1T5JPLNvzJgxctttt8mCBQtkn332kfvvv18++OADaWlpkTfeeENmzJghJ554onTt2jXUOptKor527tw5tXeYJPXdAIB0MK5w5zi6lc1nH1aTx3Yp+jjDtJ1sKEI7Me5g3AEAADpi/OjOOMn2e12jKFJ7FX28SX/Ltv0YnzI+BQCEw7jFnePwNJ65l6f2KPq4o6j9xVb7MG5g3AAAsCvP44qsn1FQSdL3Vvi4znHHB1musyv31OShDTjO5zgfAABsxjgt3TFLq6TvTfF53YswbjNti7jy1BaM4xjHAQCAjhjPpTumSfoaLB/XuQjjtzxvd66vq47xGgAAMMGYLN3xSdb34+exTYowpityv+FZDW7VW8aEAFBQWmbWrFla4cc1nXfeedqvXz/t06ePHnbYYXrttdeqiOjIkSN1yZIl+tvf/lbHjRun/fv31y5duugWW2yhRx11lC5evLjkcz799FO9/PLLdbvtttOuXbtq37599Zvf/Ka+8soroZZHVfWqq67SHj16qIjosGHD9PHHH9ef/exn2tTUpCKiAwYM0Ntuu03vuOMOHTBggIqI9u3bV2fOnKnXXHONDhw4UEVEe/TooQcddJC++eabOnbsWO3bt6927txZt956a508ebJu2LAh1neZtN+//vUvHTp0qHbv3l2/9KUv6bJly1RV9dprr9XRo0drQ0ODNjQ06C677KLXXXedXnbZZdq9e3cVEd1uu+30tdde01tvvVX79u2rIqLbbrutLliwQFVV77//fu3Vq5dOmTKlals+88wzutNOO2mnTp1URHTgwIE6depUve6669rWu/V7pk+frr1791YR0aFDh+qrr76qqqqnnHKKdu3aVbfZZhvt0qWL9u7dWw8++GB97bXXSr5r1apVOm7cOG1oaNDhw4frD37wAz3nnHNURHTUqFG6ZMkSVdUObXL99dfryJEjVURq/nfvvfca99v2dtllF73gggsqts8nn3yi5513ng4ZMkS7dOmiW221lR566KH64osvlmyLwYMH6y233BKmG0fKY1gTJkzQCRMmhPobk0zHzYWp8r5w0UUXGfdL1eo5irMMy5YtM65nlf62Vt8844wz2tqrZ8+eesghhxgvZ5jaUGsZTjvtNKN+PW7cOL3xxhsr/lut3NSqt9XazMQ999zTtq7Dhg3T5cuX65o1a3Tw4MEqItrY2Kg333yzqqo+8sgjusUWW5TUj65du+qOO+6o99xzj3Gbt7K9HzT5vV/84hcV+wp1v9h1P8rnJ13zKx13zZ8/v+Z2vfzyy9u+mz7tdp9WVRURnTVrVui/S/Lza+3/TWpsmL7z4Ycf6kknnaRbbLGFNjY2th2vtO57//3vfxsvd63tUWs7qprt2yrlMe4xXa3lCtOOZN3trLs6bjHJX6V+rxrc7iaZCtP3TGpPrePksPMYqqoHHXSQDh8+XBsbG7W+vl5HjhypRx55pM6fP7+kHcmf2/lzbT9rut+zOQeomv7cJLlwOxc+75dU08+H6RxI0PEmuSAXvuQ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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5tWhfp9icPa"
      },
      "source": [
        "# bottleneck resnet\n",
        "def resnext_bottleneck(features,stride,x_in):\n",
        "  feature1, feature2, feature3 = features\n",
        "\n",
        "  x = tf.keras.layers.Conv1D(feature1,padding='same',name='conv1')(x)\n",
        "  x = tf.keras.layers.BatchNormalization()(x)\n",
        "  x = tf.keras.layers.Activation('relu')(x)\n",
        "  x = tf.keras.layers.Conv1D(feature2,stride,padding='same',name='conv1')(x)\n",
        "  x = tf.keras.layers.BatchNormalization()(x)\n",
        "  x = tf.keras.layers.Activation('relu')(x)\n",
        "  x = tf.keras.layers.Conv1D(feature3,padding='same',name='conv1')(x)\n",
        "  x = tf.keras.layers.BatchNormalization()(x)\n",
        "\n",
        "  shortcut = tf.keras.layers.Conv1D(feature3,stride,padding='same',name='conv1')(x_in)\n",
        "  shortcut = tf.keras.layers.BatchNormalization()(shortcut)\n",
        "\n",
        "  x = tf.keras.layers.Concatenate(axis=1)([x,shortcut])\n",
        "  x = tf.keras.layers.Activation('relu')(x)\n",
        "  return x\n",
        "\n",
        "# other features\n",
        "x0 = tf.keras.layers.DenseFeatures(feature_columns,name='dense_features_feature_columns')(feature_layer_inputs) \n",
        "# x0 = tf.keras.layers.Dense(128, activation='relu')(x0)\n",
        "# x0 = tf.keras.layers.BatchNormalization()(x0)\n",
        "# x0 = tf.keras.layers.Dense(128, activation='relu')(x0)\n",
        "# x0 = tf.keras.layers.BatchNormalization()(x0)\n",
        "# x0 = tf.keras.layers.Dense(128, activation='relu')(x0)\n",
        "# x0 = tf.keras.layers.BatchNormalization()(x0)\n",
        "# x0 = tf.keras.layers.Dropout(0.4)(x0)\n",
        "\n",
        "# x1 = tf.keras.layers.DenseFeatures(embed_feature_columns,name='dense_features_embed_feature_columns')(embed_feature_layer_inputs) \n",
        "# x1 = tf.keras.layers.Dense(32, activation='relu')(x1)\n",
        "# x1 = tf.keras.layers.BatchNormalization()(x1)\n",
        "# x1 = tf.keras.layers.Dense(32, activation='relu')(x1)\n",
        "# x1 = tf.keras.layers.BatchNormalization()(x1)\n",
        "# x1 = tf.keras.layers.Dense(32, activation='relu')(x1)\n",
        "# x1 = tf.keras.layers.BatchNormalization()(x1)\n",
        "# x1 = tf.keras.layers.Dropout(0.4)(x1)\n",
        "\n",
        "# x2 = tf.keras.layers.DenseFeatures(hctci_feature_columns,name='dense_features_hctci_feature_columns')(hctci_feature_layer_inputs)\n",
        "# x2 = tf.keras.layers.Dense(128, activation='relu')(x2)\n",
        "# x2 = tf.keras.layers.BatchNormalization()(x2)\n",
        "# x2 = tf.keras.layers.Dense(128, activation='relu')(x2)\n",
        "# x2 = tf.keras.layers.BatchNormalization()(x2)\n",
        "# x2 = tf.keras.layers.Dense(128, activation='relu')(x2)\n",
        "# x2 = tf.keras.layers.BatchNormalization()(x2)\n",
        "# x2 = tf.keras.layers.Dropout(0.4)(x2)\n",
        "\n",
        "# x3 = tf.keras.layers.DenseFeatures(serogeno_feature_columns,name='dense_features_serogeno_feature_columns')(serogeno_feature_layer_inputs) \n",
        "# x3 = tf.keras.layers.Dense(64, activation='relu')(x3)\n",
        "# x3 = tf.keras.layers.BatchNormalization()(x3)\n",
        "# x3 = tf.keras.layers.Dense(64, activation='relu')(x3)\n",
        "# x3 = tf.keras.layers.BatchNormalization()(x3)\n",
        "# x3 = tf.keras.layers.Dense(64, activation='relu')(x3)\n",
        "# x3 = tf.keras.layers.BatchNormalization()(x3)\n",
        "# x3 = tf.keras.layers.Dropout(0.4)(x3)\n",
        "\n",
        "x_hla = tf.keras.layers.Concatenate(axis=1)(shared_embedding_columns)\n",
        "# x_hla = tf.keras.layers.Dense(16, activation='relu',name='dense_hla1')(x_hla)\n",
        "# x_hla = tf.keras.layers.BatchNormalization()(x_hla)\n",
        "# x_hla = tf.keras.layers.Dense(256, activation='relu',name='dense_hla2')(x_hla)\n",
        "# x_hla = tf.keras.layers.BatchNormalization()(x_hla)\n",
        "# x_hla = tf.keras.layers.Dropout(0.2)(x_hla)\n",
        "# x_hla = tf.keras.layers.Flatten(name='hla-flatten')(x_hla)\n",
        "\n",
        "x = tf.keras.layers.Concatenate(axis=1)([x0,x_hla])\n",
        "x = tf.keras.layers.Reshape((470,1))(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(2,2, activation='relu',padding='same',name='conv1')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(2,2, activation='relu',padding='same',name='conv2')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(4,2, activation='relu',padding='same',name='conv3')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(4,2, activation='relu',padding='same',name='conv4')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(8,2, activation='relu',padding='same',name='conv5')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(8,2, activation='relu',padding='same',name='conv6')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(16,2, activation='relu',padding='same',name='conv7')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(16,2, activation='relu',padding='same',name='conv8')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(32,2, activation='relu',padding='same',name='conv9')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(32,2, activation='relu',padding='same',name='conv10')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(64,2, activation='relu',padding='same',name='conv11')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(64,2, activation='relu',padding='same',name='conv12')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(128,2, activation='relu',padding='same',name='conv13')(x)\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Conv1D(128,2, activation='relu',padding='same',name='conv14')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "# x = tf.keras.layers.Conv1D(16,2, activation='relu',name='conv2')(x)\n",
        "# x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "# # x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "# x = tf.keras.layers.Conv1D(32,2, activation='relu',name='conv3')(x)\n",
        "# x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "# # x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "# x = tf.keras.layers.Conv1D(32,2, activation='relu',name='conv4')(x)\n",
        "# x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "# # x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "# x = tf.keras.layers.Conv1D(64,2, activation='relu',name='conv5')(x)\n",
        "# x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "# # x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "# x = tf.keras.layers.Conv1D(64,2, activation='relu',name='conv6')(x)\n",
        "# x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "# # x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "x = tf.keras.layers.Conv1D(256,2, activation='relu',name='conv15')(x)\n",
        "x = tf.keras.layers.MaxPooling1D(pool_size=2)(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "\n",
        "# x = tf.keras.layers.BatchNormalization()(x)\n",
        "# x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
        "x = tf.keras.layers.Flatten(name='final-flatten')(x)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.Dropout(0.8)(x)\n",
        "output_layer = tf.keras.layers.Dense(2, activation=\"softmax\")(x)\n",
        "\n",
        "model = tf.keras.models.Model(inputs=[v for v in shared_layer_inputs.values()]+\n",
        "                              [v for v in serogeno_feature_layer_inputs.values()]+\n",
        "                              [v for v in feature_layer_inputs.values()]+\n",
        "                              [v for v in hctci_feature_layer_inputs.values()], outputs=output_layer)\n",
        "\n",
        "# See model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99lVyr8Xjat_"
      },
      "source": [
        "# Utility functions for deep learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLnfFD7higNH"
      },
      "source": [
        "import functools\n",
        "# Compile\n",
        "def mean_acc_per_class1(y_true, y_pred):\n",
        "  c1_acc =tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==0,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==0,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,0]),tf.int64)\n",
        "  c2_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==1,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==1,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,1]),tf.int64)\n",
        "  return c1_acc\n",
        "def mean_acc_per_class2(y_true, y_pred):\n",
        "  c1_acc =tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==0,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==0,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,0]),tf.int64)\n",
        "  c2_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==1,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==1,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,1]),tf.int64)\n",
        "  return c2_acc\n",
        "def mean_acc_per_class3(y_true, y_pred):\n",
        "  c1_acc =tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==0,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==0,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,0]),tf.int64)\n",
        "  c2_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==1,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==1,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,1]),tf.int64)\n",
        "  c3_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==2,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==2,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,2]),tf.int64)\n",
        "  return c3_acc\n",
        "\n",
        "def mean_acc(y_true, y_pred):\n",
        "  c1_acc =tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==0,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==0,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,0]),tf.int64)\n",
        "  c2_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==1,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==1,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,1]),tf.int64)\n",
        "  c3_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==2,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==2,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,2]),tf.int64)\n",
        "  return tf.keras.backend.mean(tf.stack([c1_acc, c2_acc, c3_acc]))\n",
        "\n",
        "def mean_acc2(y_true, y_pred):\n",
        "  c1_acc =tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==0,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==0,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,0]),tf.int64)\n",
        "  c2_acc = tf.keras.backend.sum(tf.cast(tf.math.argmax(y_pred,axis=1)==1,tf.int64)*tf.cast(tf.math.argmax(y_true,axis=1)==1,tf.int64)) / tf.cast(tf.keras.backend.sum(y_true[:,1]),tf.int64)\n",
        "  return tf.keras.backend.mean(tf.stack([c1_acc, c2_acc]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCq6zii0jdgu"
      },
      "source": [
        "# Calculate Class weight"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SOCyJ7Ijb97",
        "outputId": "c9f80fb1-c96c-45e7-d744-d5abb8b15675"
      },
      "source": [
        "# class weight for three classes\n",
        "class_counts = (df_cleaned_train['MLevent_agvhd24']+df_cleaned_train['MLevent_agvhd34']).value_counts()\n",
        "total_class_counts = class_counts.sum()\n",
        "tmp = (total_class_counts/class_counts[0]+total_class_counts/class_counts[1]+total_class_counts/class_counts[2])*0.5\n",
        "class_weight = {0: total_class_counts/class_counts[0]*2, 1: total_class_counts/class_counts[1]*1.5, 2: total_class_counts/class_counts[2]*2.5}\n",
        "class_weight_array = np.array(list(class_weight.values()))\n",
        "class_weight_array, class_counts\n",
        "\n",
        "# class weight for two classes\n",
        "class_counts = df_cleaned_train['MLevent_agvhd24'].value_counts()\n",
        "total_class_counts = class_counts.sum()\n",
        "tmp = (total_class_counts/class_counts[0]+total_class_counts/class_counts[1])*0.5\n",
        "class_weight = {0: total_class_counts/class_counts[0], 1: total_class_counts/class_counts[1]}\n",
        "class_weight_array = np.array(list(class_weight.values()))\n",
        "class_weight_array, class_counts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.69004525, 2.44918033]), N    5304\n",
              " Y    3660\n",
              " Name: MLevent_agvhd24, dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eABJnM1Ojhc1"
      },
      "source": [
        "# Training model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7h7eDKfjfS1",
        "outputId": "d0dcec1e-dbd4-439a-81d9-83429b3131ab"
      },
      "source": [
        "opt = tf.keras.optimizers.Adam(lr=0.01, beta_1=0.9, beta_2=0.99, epsilon=None, decay=0.0001, amsgrad=False)\n",
        "# opt = tf.keras.optimizers.SGD(learning_rate=0.0001, momentum=0.9, nesterov=True)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\"accuracy\",tf.keras.metrics.AUC(),mean_acc2])\n",
        "\n",
        "# 目標はvalidationで60-70%\n",
        "# 変数を操作したらどうアウトカムが変わるかを見れるようにしたい\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "# Learning\n",
        "def step_decay(epoch):\n",
        "    x = 0.01\n",
        "    if epoch >= 10: x = 0.005\n",
        "    if epoch >= 50: x = 0.001\n",
        "    return x\n",
        "\n",
        "base_dir = 'out'\n",
        "cb_tb = TensorBoard(log_dir=base_dir+'/hemato-tensorBoard', histogram_freq=0)\n",
        "cb_cp = tf.keras.callbacks.ModelCheckpoint(\n",
        "    'out/models/hemato-gvhd-full.h5',\n",
        "    verbose=1,\n",
        "    monitor='loss', save_best_only=True,\n",
        "    mode='min', save_weights_only=False\n",
        ")\n",
        "\n",
        "callbacks = [cb_tb,cb_cp]\n",
        "\n",
        "epochs=100\n",
        "# tf.keras.utils.Progbar(np.floor(df_cleaned_train.shape[0]/batch_size), stateful_metrics=None, interval=0.5)\n",
        "history = model.fit(train_ds, validation_data=valid_ds, \n",
        "                    epochs=epochs,initial_epoch=0,\n",
        "                    steps_per_epoch=np.floor(df_cleaned_train.shape[0]/batch_size),\n",
        "                    validation_steps=np.floor(df_cleaned_valid.shape[0]/batch_size),\n",
        "                    class_weight=class_weight,max_queue_size=2,callbacks=callbacks,verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:595: UserWarning:\n",
            "\n",
            "Input dict contained keys ['tid', 'MLevent_agvhd24', 'MLevent_agvhd34', 'bm_tnc_total_harvest_kg', 'pb_tnc_total_cryop_kg', 'pb_cd34_total_kg', 'MLevent_agvhd24_manip', 'MLevent_agvhd34_manip', 'MLevent_agvhd2434'] which did not match any model input. They will be ignored by the model.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "280/280 - 171s - loss: 1.7769 - accuracy: 0.5033 - auc_3: 0.5037 - mean_acc2: 0.5086 - val_loss: 0.7323 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00001: loss improved from inf to 1.77688, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 2/100\n",
            "280/280 - 131s - loss: 1.6405 - accuracy: 0.4927 - auc_3: 0.4930 - mean_acc2: 0.4944 - val_loss: 2.4284 - val_accuracy: 0.4339 - val_auc_3: 0.4276 - val_mean_acc2: 0.4998\n",
            "\n",
            "Epoch 00002: loss improved from 1.77688 to 1.64054, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 3/100\n",
            "280/280 - 131s - loss: 1.5931 - accuracy: 0.5057 - auc_3: 0.5069 - mean_acc2: 0.5034 - val_loss: 71.5099 - val_accuracy: 0.5661 - val_auc_3: 0.5697 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00003: loss improved from 1.64054 to 1.59313, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 4/100\n",
            "280/280 - 131s - loss: 1.5447 - accuracy: 0.5012 - auc_3: 0.5050 - mean_acc2: 0.5024 - val_loss: 0.7204 - val_accuracy: 0.5665 - val_auc_3: 0.5651 - val_mean_acc2: 0.5030\n",
            "\n",
            "Epoch 00004: loss improved from 1.59313 to 1.54473, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 5/100\n",
            "280/280 - 138s - loss: 1.4960 - accuracy: 0.5080 - auc_3: 0.5054 - mean_acc2: 0.5105 - val_loss: 124.3236 - val_accuracy: 0.4339 - val_auc_3: 0.4339 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00005: loss improved from 1.54473 to 1.49596, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 6/100\n",
            "280/280 - 131s - loss: 1.4676 - accuracy: 0.4898 - auc_3: 0.4902 - mean_acc2: 0.4996 - val_loss: 0.7177 - val_accuracy: 0.4339 - val_auc_3: 0.4315 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00006: loss improved from 1.49596 to 1.46763, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 7/100\n",
            "280/280 - 131s - loss: 1.4309 - accuracy: 0.4916 - auc_3: 0.4803 - mean_acc2: 0.5011 - val_loss: 4.6346 - val_accuracy: 0.5661 - val_auc_3: 0.5687 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00007: loss improved from 1.46763 to 1.43089, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 8/100\n",
            "280/280 - 131s - loss: 1.4191 - accuracy: 0.5016 - auc_3: 0.5042 - mean_acc2: 0.4972 - val_loss: 0.6978 - val_accuracy: 0.4955 - val_auc_3: 0.5174 - val_mean_acc2: 0.5001\n",
            "\n",
            "Epoch 00008: loss improved from 1.43089 to 1.41906, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 9/100\n",
            "280/280 - 131s - loss: 1.4022 - accuracy: 0.5106 - auc_3: 0.5092 - mean_acc2: 0.5088 - val_loss: 0.7003 - val_accuracy: 0.5647 - val_auc_3: 0.5694 - val_mean_acc2: 0.5008\n",
            "\n",
            "Epoch 00009: loss improved from 1.41906 to 1.40221, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 10/100\n",
            "280/280 - 131s - loss: 1.4074 - accuracy: 0.5031 - auc_3: 0.5065 - mean_acc2: 0.4949 - val_loss: 0.6946 - val_accuracy: 0.5509 - val_auc_3: 0.5253 - val_mean_acc2: 0.5306\n",
            "\n",
            "Epoch 00010: loss did not improve from 1.40221\n",
            "Epoch 11/100\n",
            "280/280 - 131s - loss: 1.3907 - accuracy: 0.4991 - auc_3: 0.5053 - mean_acc2: 0.5049 - val_loss: 0.6943 - val_accuracy: 0.5522 - val_auc_3: 0.5266 - val_mean_acc2: 0.5318\n",
            "\n",
            "Epoch 00011: loss improved from 1.40221 to 1.39073, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 12/100\n",
            "280/280 - 131s - loss: 1.3892 - accuracy: 0.5096 - auc_3: 0.5181 - mean_acc2: 0.5101 - val_loss: 0.6934 - val_accuracy: 0.5522 - val_auc_3: 0.5266 - val_mean_acc2: 0.5318\n",
            "\n",
            "Epoch 00012: loss improved from 1.39073 to 1.38924, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 13/100\n",
            "280/280 - 131s - loss: 1.3889 - accuracy: 0.4925 - auc_3: 0.5001 - mean_acc2: 0.5041 - val_loss: 0.6916 - val_accuracy: 0.5522 - val_auc_3: 0.5778 - val_mean_acc2: 0.5318\n",
            "\n",
            "Epoch 00013: loss improved from 1.38924 to 1.38895, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 14/100\n",
            "280/280 - 132s - loss: 1.3887 - accuracy: 0.4888 - auc_3: 0.5027 - mean_acc2: 0.5050 - val_loss: 0.6918 - val_accuracy: 0.5522 - val_auc_3: 0.5778 - val_mean_acc2: 0.5318\n",
            "\n",
            "Epoch 00014: loss improved from 1.38895 to 1.38867, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 15/100\n",
            "280/280 - 132s - loss: 1.3889 - accuracy: 0.4941 - auc_3: 0.4984 - mean_acc2: 0.5100 - val_loss: 0.6910 - val_accuracy: 0.5522 - val_auc_3: 0.5778 - val_mean_acc2: 0.5318\n",
            "\n",
            "Epoch 00015: loss did not improve from 1.38867\n",
            "Epoch 16/100\n",
            "280/280 - 131s - loss: 1.3881 - accuracy: 0.4907 - auc_3: 0.4957 - mean_acc2: 0.4995 - val_loss: 0.6900 - val_accuracy: 0.5661 - val_auc_3: 0.5810 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00016: loss improved from 1.38867 to 1.38812, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 17/100\n",
            "280/280 - 131s - loss: 1.3917 - accuracy: 0.4982 - auc_3: 0.4914 - mean_acc2: 0.4967 - val_loss: 0.6928 - val_accuracy: 0.5661 - val_auc_3: 0.5580 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00017: loss did not improve from 1.38812\n",
            "Epoch 18/100\n",
            "280/280 - 131s - loss: 1.3884 - accuracy: 0.5218 - auc_3: 0.5203 - mean_acc2: 0.5129 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5741 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00018: loss did not improve from 1.38812\n",
            "Epoch 19/100\n",
            "280/280 - 132s - loss: 1.3884 - accuracy: 0.5065 - auc_3: 0.5160 - mean_acc2: 0.5005 - val_loss: 0.6902 - val_accuracy: 0.5661 - val_auc_3: 0.5741 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00019: loss did not improve from 1.38812\n",
            "Epoch 20/100\n",
            "280/280 - 131s - loss: 1.3876 - accuracy: 0.4916 - auc_3: 0.4979 - mean_acc2: 0.4986 - val_loss: 0.6899 - val_accuracy: 0.5661 - val_auc_3: 0.5741 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00020: loss improved from 1.38812 to 1.38761, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 21/100\n",
            "280/280 - 131s - loss: 1.3869 - accuracy: 0.4948 - auc_3: 0.4991 - mean_acc2: 0.5043 - val_loss: 0.6900 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00021: loss improved from 1.38761 to 1.38693, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 22/100\n",
            "280/280 - 131s - loss: 1.3877 - accuracy: 0.5022 - auc_3: 0.5052 - mean_acc2: 0.5039 - val_loss: 0.6919 - val_accuracy: 0.5661 - val_auc_3: 0.5709 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00022: loss did not improve from 1.38693\n",
            "Epoch 23/100\n",
            "280/280 - 131s - loss: 1.3876 - accuracy: 0.4854 - auc_3: 0.4942 - mean_acc2: 0.4992 - val_loss: 0.8786 - val_accuracy: 0.5661 - val_auc_3: 0.5600 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00023: loss did not improve from 1.38693\n",
            "Epoch 24/100\n",
            "280/280 - 131s - loss: 1.3878 - accuracy: 0.4993 - auc_3: 0.4998 - mean_acc2: 0.4997 - val_loss: 0.6908 - val_accuracy: 0.5661 - val_auc_3: 0.5707 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00024: loss did not improve from 1.38693\n",
            "Epoch 25/100\n",
            "280/280 - 132s - loss: 1.3866 - accuracy: 0.5106 - auc_3: 0.5210 - mean_acc2: 0.5012 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00025: loss improved from 1.38693 to 1.38665, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 26/100\n",
            "280/280 - 131s - loss: 1.3870 - accuracy: 0.4923 - auc_3: 0.4931 - mean_acc2: 0.5028 - val_loss: 0.6902 - val_accuracy: 0.5661 - val_auc_3: 0.5676 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00026: loss did not improve from 1.38665\n",
            "Epoch 27/100\n",
            "280/280 - 131s - loss: 1.3870 - accuracy: 0.4892 - auc_3: 0.4976 - mean_acc2: 0.4976 - val_loss: 0.6904 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00027: loss did not improve from 1.38665\n",
            "Epoch 28/100\n",
            "280/280 - 131s - loss: 1.3867 - accuracy: 0.5003 - auc_3: 0.5043 - mean_acc2: 0.5001 - val_loss: 0.6907 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00028: loss did not improve from 1.38665\n",
            "Epoch 29/100\n",
            "280/280 - 130s - loss: 1.3866 - accuracy: 0.4917 - auc_3: 0.5026 - mean_acc2: 0.4926 - val_loss: 0.6907 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00029: loss improved from 1.38665 to 1.38658, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 30/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4904 - auc_3: 0.5028 - mean_acc2: 0.4999 - val_loss: 0.6897 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00030: loss improved from 1.38658 to 1.38639, saving model to out/models/hemato-gvhd-full.h5\n",
            "Epoch 31/100\n",
            "280/280 - 130s - loss: 1.3867 - accuracy: 0.4865 - auc_3: 0.4870 - mean_acc2: 0.5019 - val_loss: 0.6907 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00031: loss did not improve from 1.38639\n",
            "Epoch 32/100\n",
            "280/280 - 130s - loss: 1.3865 - accuracy: 0.4876 - auc_3: 0.5013 - mean_acc2: 0.4980 - val_loss: 0.6907 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00032: loss did not improve from 1.38639\n",
            "Epoch 33/100\n",
            "280/280 - 131s - loss: 1.3865 - accuracy: 0.4895 - auc_3: 0.5016 - mean_acc2: 0.5007 - val_loss: 0.6907 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00033: loss did not improve from 1.38639\n",
            "Epoch 34/100\n",
            "280/280 - 131s - loss: 1.3865 - accuracy: 0.4968 - auc_3: 0.5087 - mean_acc2: 0.5005 - val_loss: 0.6906 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00034: loss did not improve from 1.38639\n",
            "Epoch 35/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4991 - auc_3: 0.5146 - mean_acc2: 0.4995 - val_loss: 0.6904 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00035: loss did not improve from 1.38639\n",
            "Epoch 36/100\n",
            "280/280 - 131s - loss: 1.3865 - accuracy: 0.4915 - auc_3: 0.5043 - mean_acc2: 0.4978 - val_loss: 0.6910 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00036: loss did not improve from 1.38639\n",
            "Epoch 37/100\n",
            "280/280 - 130s - loss: 1.3865 - accuracy: 0.4908 - auc_3: 0.5016 - mean_acc2: 0.5009 - val_loss: 0.6909 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00037: loss did not improve from 1.38639\n",
            "Epoch 38/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4964 - auc_3: 0.5066 - mean_acc2: 0.5023 - val_loss: 0.6908 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00038: loss did not improve from 1.38639\n",
            "Epoch 39/100\n",
            "280/280 - 130s - loss: 1.3865 - accuracy: 0.4860 - auc_3: 0.4938 - mean_acc2: 0.5010 - val_loss: 0.6909 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00039: loss did not improve from 1.38639\n",
            "Epoch 40/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4888 - auc_3: 0.5018 - mean_acc2: 0.5000 - val_loss: 0.6909 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00040: loss did not improve from 1.38639\n",
            "Epoch 41/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4888 - auc_3: 0.5041 - mean_acc2: 0.5000 - val_loss: 0.6910 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00041: loss did not improve from 1.38639\n",
            "Epoch 42/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5026 - mean_acc2: 0.5000 - val_loss: 0.6910 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00042: loss did not improve from 1.38639\n",
            "Epoch 43/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5021 - mean_acc2: 0.5000 - val_loss: 0.6910 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00043: loss did not improve from 1.38639\n",
            "Epoch 44/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5019 - mean_acc2: 0.5000 - val_loss: 0.6910 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00044: loss did not improve from 1.38639\n",
            "Epoch 45/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5018 - mean_acc2: 0.5000 - val_loss: 0.6911 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00045: loss did not improve from 1.38639\n",
            "Epoch 46/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5018 - mean_acc2: 0.5000 - val_loss: 0.6911 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00046: loss did not improve from 1.38639\n",
            "Epoch 47/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5018 - mean_acc2: 0.5000 - val_loss: 0.6911 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00047: loss did not improve from 1.38639\n",
            "Epoch 48/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5029 - mean_acc2: 0.5000 - val_loss: 0.6911 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00048: loss did not improve from 1.38639\n",
            "Epoch 49/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5024 - mean_acc2: 0.5000 - val_loss: 0.6911 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00049: loss did not improve from 1.38639\n",
            "Epoch 50/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5022 - mean_acc2: 0.5000 - val_loss: 0.6912 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00050: loss did not improve from 1.38639\n",
            "Epoch 51/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4902 - auc_3: 0.5015 - mean_acc2: 0.5000 - val_loss: 0.6912 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00051: loss did not improve from 1.38639\n",
            "Epoch 52/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4902 - auc_3: 0.5021 - mean_acc2: 0.5000 - val_loss: 0.6912 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00052: loss did not improve from 1.38639\n",
            "Epoch 53/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5020 - mean_acc2: 0.5000 - val_loss: 0.6912 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00053: loss did not improve from 1.38639\n",
            "Epoch 54/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5000 - mean_acc2: 0.5000 - val_loss: 0.6912 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00054: loss did not improve from 1.38639\n",
            "Epoch 55/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.5001 - mean_acc2: 0.5000 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00055: loss did not improve from 1.38639\n",
            "Epoch 56/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4897 - auc_3: 0.4999 - mean_acc2: 0.5000 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00056: loss did not improve from 1.38639\n",
            "Epoch 57/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4908 - auc_3: 0.4994 - mean_acc2: 0.5000 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00057: loss did not improve from 1.38639\n",
            "Epoch 58/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4908 - auc_3: 0.4989 - mean_acc2: 0.5000 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00058: loss did not improve from 1.38639\n",
            "Epoch 59/100\n",
            "280/280 - 134s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.4994 - mean_acc2: 0.5000 - val_loss: 0.6913 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00059: loss did not improve from 1.38639\n",
            "Epoch 60/100\n",
            "280/280 - 143s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5002 - mean_acc2: 0.5000 - val_loss: 0.6914 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00060: loss did not improve from 1.38639\n",
            "Epoch 61/100\n",
            "280/280 - 141s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5009 - mean_acc2: 0.5000 - val_loss: 0.6914 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00061: loss did not improve from 1.38639\n",
            "Epoch 62/100\n",
            "280/280 - 141s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5014 - mean_acc2: 0.5000 - val_loss: 0.6914 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00062: loss did not improve from 1.38639\n",
            "Epoch 63/100\n",
            "280/280 - 141s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5009 - mean_acc2: 0.5000 - val_loss: 0.6914 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00063: loss did not improve from 1.38639\n",
            "Epoch 64/100\n",
            "280/280 - 139s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5010 - mean_acc2: 0.5000 - val_loss: 0.6914 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00064: loss did not improve from 1.38639\n",
            "Epoch 65/100\n",
            "280/280 - 130s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5000 - mean_acc2: 0.5000 - val_loss: 0.6914 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00065: loss did not improve from 1.38639\n",
            "Epoch 66/100\n",
            "280/280 - 131s - loss: 1.3864 - accuracy: 0.4900 - auc_3: 0.5012 - mean_acc2: 0.5000 - val_loss: 0.6915 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00066: loss did not improve from 1.38639\n",
            "Epoch 67/100\n",
            "280/280 - 133s - loss: 1.3864 - accuracy: 0.4895 - auc_3: 0.5021 - mean_acc2: 0.5000 - val_loss: 0.6915 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00067: loss did not improve from 1.38639\n",
            "Epoch 68/100\n",
            "280/280 - 133s - loss: 1.3864 - accuracy: 0.4906 - auc_3: 0.5019 - mean_acc2: 0.5000 - val_loss: 0.6915 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00068: loss did not improve from 1.38639\n",
            "Epoch 69/100\n",
            "280/280 - 132s - loss: 1.3864 - accuracy: 0.4906 - auc_3: 0.5028 - mean_acc2: 0.5000 - val_loss: 0.6915 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00069: loss did not improve from 1.38639\n",
            "Epoch 70/100\n",
            "280/280 - 133s - loss: 1.3864 - accuracy: 0.4906 - auc_3: 0.5027 - mean_acc2: 0.5000 - val_loss: 0.6915 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00070: loss did not improve from 1.38639\n",
            "Epoch 71/100\n",
            "280/280 - 133s - loss: 1.3864 - accuracy: 0.4906 - auc_3: 0.5026 - mean_acc2: 0.5000 - val_loss: 0.6915 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00071: loss did not improve from 1.38639\n",
            "Epoch 72/100\n",
            "280/280 - 134s - loss: 1.3864 - accuracy: 0.4906 - auc_3: 0.5018 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00072: loss did not improve from 1.38639\n",
            "Epoch 73/100\n",
            "280/280 - 133s - loss: 1.3864 - accuracy: 0.4915 - auc_3: 0.5015 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00073: loss did not improve from 1.38639\n",
            "Epoch 74/100\n",
            "280/280 - 134s - loss: 1.3864 - accuracy: 0.4917 - auc_3: 0.5010 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00074: loss did not improve from 1.38639\n",
            "Epoch 75/100\n",
            "280/280 - 133s - loss: 1.3864 - accuracy: 0.4917 - auc_3: 0.4995 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00075: loss did not improve from 1.38639\n",
            "Epoch 76/100\n",
            "280/280 - 135s - loss: 1.3864 - accuracy: 0.4917 - auc_3: 0.4985 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00076: loss did not improve from 1.38639\n",
            "Epoch 77/100\n",
            "280/280 - 134s - loss: 1.3864 - accuracy: 0.4917 - auc_3: 0.4978 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00077: loss did not improve from 1.38639\n",
            "Epoch 78/100\n",
            "280/280 - 135s - loss: 1.3864 - accuracy: 0.4931 - auc_3: 0.4980 - mean_acc2: 0.5000 - val_loss: 0.6916 - val_accuracy: 0.5661 - val_auc_3: 0.5661 - val_mean_acc2: 0.5000\n",
            "\n",
            "Epoch 00078: loss did not improve from 1.38639\n",
            "Epoch 79/100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "op9HF8Us2pSd"
      },
      "source": [
        "# Train data Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "ChfK5NKxji-L",
        "outputId": "783c4a2b-0e97-4f49-b972-1d379ccd030f"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, accuracy_score\n",
        "\n",
        "df = df_cleaned_train\n",
        "ds = train_ds # NO SHUFFLE!!\n",
        "\n",
        "res = model.predict(ds,steps=np.ceil(df.shape[0]/batch_size))\n",
        "y_true = (df[label_name]=='Y').values.astype(int)\n",
        "y_pred = np.argmax(res[:df.shape[0],:],axis=1)\n",
        "\n",
        "print(confusion_matrix(y_true, y_pred))\n",
        "print(classification_report(y_true, y_pred,target_names=['N','Y']))\n",
        "print(accuracy_score(y_true, y_pred))\n",
        "\n",
        "\n",
        "y_pred = res[:df.shape[0],1]\n",
        "\n",
        "# ROC\n",
        "fpr, tpr, thres = roc_curve(y_true, y_pred)\n",
        "auc = auc(fpr, tpr)\n",
        "print('auc:', auc)\n",
        "\n",
        "fig = plt.figure(figsize = (5, 5),dpi=100) #単一グラフの場合のサイズ比の与え方\n",
        "plt.plot(fpr, tpr, marker='o')\n",
        "plt.xlabel('FPR: False Positive Rete', fontsize = 13)\n",
        "plt.ylabel('TPR: True Positive Rete', fontsize = 13)\n",
        "plt.grid()\n",
        "plt.show()\n",
        "fig.savefig(\"roc.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3827 1477]\n",
            " [ 679 2981]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.85      0.72      0.78      5304\n",
            "           Y       0.67      0.81      0.73      3660\n",
            "\n",
            "    accuracy                           0.76      8964\n",
            "   macro avg       0.76      0.77      0.76      8964\n",
            "weighted avg       0.78      0.76      0.76      8964\n",
            "\n",
            "0.7594823739402052\n",
            "auc: 0.8480692991782672\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jrhUacS4bmu"
      },
      "source": [
        "# Test data Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "jvmV4WYC3gMM",
        "outputId": "cd78c82a-7fa1-4f56-b8f3-cc0efb5afb7c"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, accuracy_score\n",
        "\n",
        "df = df_cleaned_test\n",
        "ds = test_ds\n",
        "\n",
        "res = model.predict(ds,steps=np.ceil(df.shape[0]/batch_size))\n",
        "y_true = (df[label_name]=='Y').values.astype(int)\n",
        "y_pred = np.argmax(res[:df.shape[0],:],axis=1)\n",
        "\n",
        "print(confusion_matrix(y_true, y_pred))\n",
        "print(classification_report(y_true, y_pred,target_names=['N','Y']))\n",
        "print(accuracy_score(y_true, y_pred))\n",
        "\n",
        "\n",
        "y_pred = res[:df.shape[0],1]\n",
        "\n",
        "# ROC\n",
        "fpr, tpr, thres = roc_curve(y_true, y_pred)\n",
        "auc = auc(fpr, tpr)\n",
        "print('auc:', auc)\n",
        "\n",
        "fig = plt.figure(figsize = (5, 5),dpi=100) #単一グラフの場合のサイズ比の与え方\n",
        "plt.plot(fpr, tpr, marker='o')\n",
        "plt.xlabel('FPR: False Positive Rete', fontsize = 13)\n",
        "plt.ylabel('TPR: True Positive Rete', fontsize = 13)\n",
        "plt.grid()\n",
        "plt.show()\n",
        "fig.savefig(\"roc.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[910 781]\n",
            " [500 611]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.65      0.54      0.59      1691\n",
            "           Y       0.44      0.55      0.49      1111\n",
            "\n",
            "    accuracy                           0.54      2802\n",
            "   macro avg       0.54      0.54      0.54      2802\n",
            "weighted avg       0.56      0.54      0.55      2802\n",
            "\n",
            "0.5428265524625268\n",
            "auc: 0.5552272554280857\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xmlvd553Xf6",
        "outputId": "ecfa45f3-6bee-45d7-cd3b-20c0970b773a"
      },
      "source": [
        "y_true,y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 1, ..., 1, 0, 1]),\n",
              " array([0.9494676 , 0.7952808 , 0.7902556 , ..., 0.17315933, 0.39526767,\n",
              "        0.82992756], dtype=float32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGG6Vl6h3nbz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}