LSTM_Experiment1_Padding_and_masking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version:  3.7.3 (default, Mar 27 2019, 22:11:17) \n",
      "[GCC 7.3.0]\n",
      "pandas version:  0.24.2\n",
      "numpy version:  1.16.4\n",
      "scipy version:  1.3.0\n",
      "sklearn version:  0.21.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keras version:  2.2.4\n",
      "keras backend:  tensorflow\n",
      "tensorflow version:  1.14.0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"python version: \", sys.version)\n",
    "\n",
    "import pandas as pd\n",
    "print(\"pandas version: \", pd.__version__)\n",
    "\n",
    "import numpy as np\n",
    "print('numpy version: ', np.__version__)\n",
    "\n",
    "import scipy as sp\n",
    "print('scipy version: ', sp.__version__)\n",
    "\n",
    "import sklearn as skl\n",
    "print('sklearn version: ', skl.__version__)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "\n",
    "import keras as ks\n",
    "print('keras version: ', ks.__version__)\n",
    "print('keras backend: ', ks.backend.backend())\n",
    "\n",
    "import tensorflow as tf\n",
    "print('tensorflow version: ', tf.__version__)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from keras import backend as K\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "from sklearn.metrics import roc_curve, auc, f1_score, accuracy_score, precision_recall_curve\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7584, 208)\n"
     ]
    }
   ],
   "source": [
    "excel_file = r'geriomop-data-from-egems-paper-UPDATED-COL-FIX-ATC_2-and-behavior-change-022819.tsv'\n",
    "df = pd.DataFrame(pd.read_csv(excel_file, sep='\\t'))\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop features\n",
    "f_drop = [\"Blank-1\", \"ws-sudden-stop\", \"ws-sudden-stop-drugs\", \"ws-prolonged-psychotropics\", \"mds-toilet-prgm-atmptd\"] + \\\n",
    "         ['Deleted-%d' % i for i in range(1, 13)] + ['Psychotropic-%d' % i for i in range(1, 6)] + \\\n",
    "         ['Psychotropic-%d-average-daily-dose' % i for i in range(1, 6)] + [\"Entry-discharge-type\"]\n",
    "\n",
    "# Missing value means not relevant\n",
    "f_one_hot = [\"Mds-fall-2-6-months-to-admission\", \"Mds-fall-last-month-to-admission\", \"mds-pain-last-five-days\", \\\n",
    "             \"mds-delirium-scale\", \"mds-long-term-memory-ok\", \"mds-short-term-memory-ok\", \"mds-staff-assess-pain\", \\\n",
    "             \"recent-start-other-fall-risk-rx\"] + [\"mds-pressure-ulcer-stage-%d\" % i for i in range(1, 5)]\n",
    "cate_one_hot = [['Yes', 'No', 'Unable to answer']] * 3 + [['Yes', 'No']] * 9\n",
    "\n",
    "f_period = [(\"Psychotropic-%d-Start-date\" % i, 'Psychotropic-%d-End-date' % i, 'Psychotropic-%d-Period' % i) for i in range(1, 6)]\n",
    "\n",
    "f_label = ['mds-cognitive-scale', 'mds-pain-freq-last-five-days', 'mds-pain-intensity']\n",
    "dic_label = [{'Independent': 0, 'Modified Independence': 1, 'Moderately Impaired': 2, 'Severely Impaired': 3}, \\\n",
    "             {'Continuous': 4, 'Frequent': 3, 'Occasional': 2, 'Rare': 1, 'Unable to respond': 0}, \\\n",
    "             {'None': 0, 'Mild': 1, 'Moderate': 2, 'Severe': 3, 'Very severe, horrible': 4}]\n",
    "\n",
    "f_transform = [('mds-cognitive-scale', 3, 0), ('mds-pain-freq-last-five-days', 4, 0), ('mds-pain-intensity', 4, 0)] + \\\n",
    "              [('Psychotropic-%d-Period' % i, 0, 0) for i in range(1, 6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode features (originally) with no missing values\n",
    "f_drop += [\"predictor-date\", \"projected-date\", \"PPS-assessment\", \"Federal-assessment\"]\n",
    "\n",
    "f_period += [('episode-start-date', 'episode-end-date', 'episode-period'),\\\n",
    "             ('stay-start-date', 'stay-end-date', 'stay-period')]\n",
    "\n",
    "f_label += [\"cms-long-stay\", \"mds-stay-trans-short-to-long\", \"mds-antianxiety-medication\", \\\n",
    "            \"mds-antidepressant-medication\", \"mds-antipsychotic-medication\", \"mds-antibiotic-medication\",\n",
    "            \"mds-anticoagulant-medication\", \"mds-diuretic-medication\", \\\n",
    "            \"mds-hypnotic-medication\", \"mds-behavioral-symptoms\", \"mds-behavioral-symptoms-to-others\", \\\n",
    "            \"mds-dehydrated\", \"mds-depression\", \"mds-fever\", \"mds-impaired-mobility\", \"mds-impaired-transfer\", \\\n",
    "            \"mds-internal-bleeding\", \"mds-malnutrition\", \"mds-no-problem-conditions\", \"mds-vomiting\", \\\n",
    "            \"mds-impaired-walk-in-room\", \"mds-impaired-walk-in-corridor\", \"mds-impaired-locomot-unit\", \\\n",
    "            \"mds-impaired-locomot-other\", \"ws-antibiotic-anticoag-coexposure\", \\\n",
    "            \"ws-psychotropic-with-not-ordered-weight-loss\", \\\n",
    "            \"ws-diuretic-adl\", \"ws-tramadol-antidepressant-coexposure\"] + [\"mds-pain-non-verbal\"]\n",
    "dic_label += [{'Yes': 1, 'No': 0}] * 28 + [{'None/Mild':0,'Moderate/Severe':1}]\n",
    "\n",
    "f_one_hot += [\"race\", \"facility\", \"gender\", \"mds-bims-summary-ranking\", \"ws-meclizine-psych-coexposure\"]\n",
    "cate_one_hot += [['White', 'Black', 'Other'], \\\n",
    "                 ['Sugar Creek', 'Heritage Place', 'Canterbury Place', 'Senaca Place', 'Cranberry Place'], \\\n",
    "                 ['Female', 'Male'], ['Intact or Moderately Intact', 'Moderate Impairment'], \\\n",
    "                 ['No', 'meclAndPsychStartSameR', 'meclPrecedesPsychR', 'psychPrecedesMeclR', 'meclAndPsychOlderStart']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_drop += [\"min_effective_dose_%d\" % i for i in range(1, 6)] + \\\n",
    "          [\"Psychotropic-%d-average-daily-dose\" % i for i in range(1, 6)] + \\\n",
    "          [\"psych_sdd_%d\" % i for i in range(1, 6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate target\n",
    "target = df['outcome-Mds-fall-since-prior-assessment'].map({'None': 0, 'Yes': 1})\n",
    "dff = df.drop(['outcome-mds-fall-no-injury', 'outcome-mds-fall-minor-injury', 'outcome-mds-fall-major-injury', \\\n",
    "               'outcome-Mds-fall-since-prior-assessment', 'outcome-riskmaster-fall-incident'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in sdd 0 means NA\n",
    "for i in range(1, 6):\n",
    "    df.loc[df['psych_sdd_%d' % i] == 0, 'psych_sdd_%d' % i] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import GroupShuffleSplit\n",
    "def train_test_split(df, target, groupby, radio, random_state):\n",
    "    train_idx, test_idx = next(GroupShuffleSplit(train_size=radio, test_size=1-radio, random_state=random_state).split(df, target, df[groupby]))\n",
    "    return df.iloc[train_idx], df.iloc[test_idx], target.iloc[train_idx], target.iloc[test_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of patients: 3133\n",
      "Number of patients in training set: 2193\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(dff, target, 'patient-id', 0.7, 0)\n",
    "print('Total number of patients: %d' % len(df.groupby('patient-id')))\n",
    "print('Number of patients in training set: %d' % len(X_train.groupby('patient-id')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop feature\n",
    "def drop_features(df, f):\n",
    "    df.drop(f, axis=1, inplace=True)\n",
    "    \n",
    "# one hot encoding\n",
    "def one_hot_encoding(df, features, categories):\n",
    "    for f, c in zip(features, categories):\n",
    "        df[f] = df[f].astype('category', categories=c)\n",
    "    return pd.get_dummies(df, columns=features, prefix=features)\n",
    "\n",
    "# compute date difference\n",
    "def date_diff(start, end):\n",
    "    return (pd.to_datetime(end) - pd.to_datetime(start)) / np.timedelta64(1, 'D')\n",
    "\n",
    "# generate date diff features\n",
    "def generate_period(df, f):\n",
    "    for start, end, period in f:\n",
    "        df[period] = date_diff(df[start], df[end])\n",
    "        df.drop([start, end], axis=1, inplace=True)\n",
    "        \n",
    "# label encoding\n",
    "def label_encoding(df, features, dictionaries):\n",
    "    for f, dic in zip(features, dictionaries):\n",
    "        df[f] = df[f].map(dic)\n",
    "        \n",
    "# sin&cos transform\n",
    "def col_transform(df, col, mmax, mmin, df_ref=None):\n",
    "    if(mmax <= mmin):\n",
    "        # for test set, if max&min not set, use training set value\n",
    "        if(df_ref is not None): \n",
    "            mmax = df_ref[col].max()\n",
    "            mmin = df_ref[col].max()\n",
    "        else:\n",
    "            mmax = df[col].max()\n",
    "            mmin = df[col].min()\n",
    "    angle = 0.25 * np.pi * (df[col] - mmin) / (mmax - mmin) + 0.125 * np.pi\n",
    "    return (np.cos(angle), np.sin(angle))\n",
    "\n",
    "def df_transform(df, f, df_ref=None):\n",
    "    for col, mmax, mmin in f:\n",
    "        df[col + '_x'], df[col + '_y'] = col_transform(df, col, mmax, mmin, df_ref)\n",
    "        df[col + '_x'].fillna(0, inplace=True)\n",
    "        df[col + '_y'].fillna(0, inplace=True)\n",
    "        df.drop(col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.8 s, sys: 318 ms, total: 18.1 s\n",
      "Wall time: 12.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "generate_period(X_train, f_period)\n",
    "label_encoding(X_train, f_label, dic_label)\n",
    "X_train_original = X_train.copy() # store value range\n",
    "df_transform(X_train, f_transform)\n",
    "X_train = one_hot_encoding(X_train, f_one_hot, cate_one_hot)\n",
    "drop_features(X_train, f_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_use = X_train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.6 s, sys: 51.8 ms, total: 10.7 s\n",
      "Wall time: 10.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "generate_period(X_test, f_period)\n",
    "label_encoding(X_test, f_label, dic_label)\n",
    "df_transform(X_test, f_transform, X_train_original)\n",
    "X_test = one_hot_encoding(X_test, f_one_hot, cate_one_hot)\n",
    "drop_features(X_test, f_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_use = X_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5301, 189)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_use.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_use.to_csv (r'/tmp/export_exp1_train_dataframe.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2283, 189)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_use.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RNN - LSTM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, GRU, Dense, Dropout, Masking, TimeDistributed\n",
    "from numpy.random import seed\n",
    "from tensorflow import set_random_seed\n",
    "from copy import deepcopy\n",
    "from keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing data describe\n",
    "def missing_summary(df):\n",
    "    missing_cnt = df.isna().sum()\n",
    "    missing_data = pd.concat([missing_cnt, missing_cnt/df.shape[0]], axis=1, keys=['count', 'percentage'])\n",
    "    return missing_data[missing_data['percentage'] != 0].sort_values(by='percentage', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = missing_summary(X_train_use).index.tolist()\n",
    "X_train_use.drop(columns=idx, axis=1, inplace=True)\n",
    "X_test_use.drop(columns=idx, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scaler_transform(X_train_use, X_test_use):    \n",
    "    scaler = MinMaxScaler(feature_range=(0, 1)).fit(X_train_use.drop('patient-id', axis=1))\n",
    "    X_train_transformed = pd.DataFrame(scaler.transform(X_train_use.drop('patient-id', axis=1)), \\\n",
    "                                       columns=X_train_use.columns[1:], index=X_train_use.index)                                   \n",
    "    X_train_transformed['patient-id'] = X_train_use['patient-id']\n",
    "    X_test_transformed = pd.DataFrame(scaler.transform(X_test_use.drop('patient-id', axis=1)), \\\n",
    "                                       columns=X_test_use.columns[1:], index=X_test_use.index) \n",
    "    X_test_transformed['patient-id'] = X_test_use['patient-id']\n",
    "    return X_train_transformed, X_test_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_previous_outcome(X, Y):\n",
    "    X = X.copy()\n",
    "    x1, x2 = [], []\n",
    "    pids = X['patient-id'].unique()\n",
    "    for pid in pids:\n",
    "        df = Y[X[X['patient-id']==pid].index]\n",
    "        if(df.shape[0] == 1):\n",
    "            x1.append(pd.Series([0]))\n",
    "            x2.append(pd.Series([0]))\n",
    "        else:\n",
    "            # input 1=(1, 0), 0=(0, 1), NA=(0, 0)\n",
    "            x1.append(pd.Series([0]))\n",
    "            x1.append(df[:-1])\n",
    "            x2.append(pd.Series([0]))\n",
    "            x2.append(df[:-1].map({0: 1, 1: 0}))\n",
    "    X['input_outcome_x'] = pd.Series(pd.concat(x1, ignore_index=True).tolist(), index=X.index)\n",
    "    X['input_outcome_y'] = pd.Series(pd.concat(x2, ignore_index=True).tolist(), index=X.index)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## This step converts the outcome to a point in x,y coordinates where fall (1) = (1,0); no fall (0) = (0,1); and NA = (0,0) \n",
    "X_train_use = add_previous_outcome(X_train_use, y_train)\n",
    "X_test_use = add_previous_outcome(X_test_use, y_test)\n",
    "\n",
    "## This step transforms the data to range between 0 and 1\n",
    "X_train_transformed, X_test_transformed = scaler_transform(X_train_use, X_test_use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transformed.to_csv (r'/tmp/exp1_train_transformed_df.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5301, 102)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_use.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2283, 102)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_use.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient-id</th>\n",
       "      <th>current-stay-days</th>\n",
       "      <th>cumulative-days-in-facility</th>\n",
       "      <th>cms-long-stay</th>\n",
       "      <th>mds-stay-trans-short-to-long</th>\n",
       "      <th>age</th>\n",
       "      <th>mds-antianxiety-medication</th>\n",
       "      <th>mds-antidepressant-medication</th>\n",
       "      <th>mds-antipsychotic-medication</th>\n",
       "      <th>mds-antibiotic-medication</th>\n",
       "      <th>...</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>mds-bims-summary-ranking_Intact or Moderately Intact</th>\n",
       "      <th>mds-bims-summary-ranking_Moderate Impairment</th>\n",
       "      <th>ws-meclizine-psych-coexposure_No</th>\n",
       "      <th>ws-meclizine-psych-coexposure_meclAndPsychStartSameR</th>\n",
       "      <th>ws-meclizine-psych-coexposure_meclPrecedesPsychR</th>\n",
       "      <th>ws-meclizine-psych-coexposure_psychPrecedesMeclR</th>\n",
       "      <th>ws-meclizine-psych-coexposure_meclAndPsychOlderStart</th>\n",
       "      <th>input_outcome_x</th>\n",
       "      <th>input_outcome_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10353</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10364</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10386</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5003</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient-id  current-stay-days  cumulative-days-in-facility  cms-long-stay  \\\n",
       "0       10353                  7                            7              0   \n",
       "1       10364                  7                            7              0   \n",
       "4       10386                  7                            7              0   \n",
       "5        5000                  7                            7              0   \n",
       "6        5003                  7                            7              0   \n",
       "\n",
       "   mds-stay-trans-short-to-long  age  mds-antianxiety-medication  \\\n",
       "0                             0   67                           1   \n",
       "1                             1   84                           0   \n",
       "4                             1   77                           1   \n",
       "5                             0   90                           0   \n",
       "6                             1   90                           0   \n",
       "\n",
       "   mds-antidepressant-medication  mds-antipsychotic-medication  \\\n",
       "0                              1                             0   \n",
       "1                              0                             1   \n",
       "4                              1                             1   \n",
       "5                              1                             1   \n",
       "6                              1                             1   \n",
       "\n",
       "   mds-antibiotic-medication  ...  gender_Male  \\\n",
       "0                          0  ...            0   \n",
       "1                          1  ...            0   \n",
       "4                          0  ...            0   \n",
       "5                          0  ...            0   \n",
       "6                          1  ...            1   \n",
       "\n",
       "   mds-bims-summary-ranking_Intact or Moderately Intact  \\\n",
       "0                                                  1      \n",
       "1                                                  1      \n",
       "4                                                  1      \n",
       "5                                                  1      \n",
       "6                                                  1      \n",
       "\n",
       "   mds-bims-summary-ranking_Moderate Impairment  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "4                                             0   \n",
       "5                                             0   \n",
       "6                                             0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_No  \\\n",
       "0                                 1   \n",
       "1                                 1   \n",
       "4                                 1   \n",
       "5                                 1   \n",
       "6                                 1   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_meclAndPsychStartSameR  \\\n",
       "0                                                  0      \n",
       "1                                                  0      \n",
       "4                                                  0      \n",
       "5                                                  0      \n",
       "6                                                  0      \n",
       "\n",
       "   ws-meclizine-psych-coexposure_meclPrecedesPsychR  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_psychPrecedesMeclR  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "4                                                 0   \n",
       "5                                                 0   \n",
       "6                                                 0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_meclAndPsychOlderStart  input_outcome_x  \\\n",
       "0                                                  0                   0   \n",
       "1                                                  0                   0   \n",
       "4                                                  0                   0   \n",
       "5                                                  0                   0   \n",
       "6                                                  0                   0   \n",
       "\n",
       "   input_outcome_y  \n",
       "0                0  \n",
       "1                0  \n",
       "4                0  \n",
       "5                0  \n",
       "6                0  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_use.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current-stay-days</th>\n",
       "      <th>cumulative-days-in-facility</th>\n",
       "      <th>cms-long-stay</th>\n",
       "      <th>mds-stay-trans-short-to-long</th>\n",
       "      <th>age</th>\n",
       "      <th>mds-antianxiety-medication</th>\n",
       "      <th>mds-antidepressant-medication</th>\n",
       "      <th>mds-antipsychotic-medication</th>\n",
       "      <th>mds-antibiotic-medication</th>\n",
       "      <th>mds-anticoagulant-medication</th>\n",
       "      <th>...</th>\n",
       "      <th>mds-bims-summary-ranking_Intact or Moderately Intact</th>\n",
       "      <th>mds-bims-summary-ranking_Moderate Impairment</th>\n",
       "      <th>ws-meclizine-psych-coexposure_No</th>\n",
       "      <th>ws-meclizine-psych-coexposure_meclAndPsychStartSameR</th>\n",
       "      <th>ws-meclizine-psych-coexposure_meclPrecedesPsychR</th>\n",
       "      <th>ws-meclizine-psych-coexposure_psychPrecedesMeclR</th>\n",
       "      <th>ws-meclizine-psych-coexposure_meclAndPsychOlderStart</th>\n",
       "      <th>input_outcome_x</th>\n",
       "      <th>input_outcome_y</th>\n",
       "      <th>patient-id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006369</td>\n",
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       "      <td>10353</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006369</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.825581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.825581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   current-stay-days  cumulative-days-in-facility  cms-long-stay  \\\n",
       "0           0.006369                     0.006342            0.0   \n",
       "1           0.006369                     0.006342            0.0   \n",
       "4           0.006369                     0.006342            0.0   \n",
       "5           0.006369                     0.006342            0.0   \n",
       "6           0.006369                     0.006342            0.0   \n",
       "\n",
       "   mds-stay-trans-short-to-long       age  mds-antianxiety-medication  \\\n",
       "0                           0.0  0.558140                         1.0   \n",
       "1                           1.0  0.755814                         0.0   \n",
       "4                           1.0  0.674419                         1.0   \n",
       "5                           0.0  0.825581                         0.0   \n",
       "6                           1.0  0.825581                         0.0   \n",
       "\n",
       "   mds-antidepressant-medication  mds-antipsychotic-medication  \\\n",
       "0                            1.0                           0.0   \n",
       "1                            0.0                           1.0   \n",
       "4                            1.0                           1.0   \n",
       "5                            1.0                           1.0   \n",
       "6                            1.0                           1.0   \n",
       "\n",
       "   mds-antibiotic-medication  mds-anticoagulant-medication  ...  \\\n",
       "0                        0.0                           0.0  ...   \n",
       "1                        1.0                           0.0  ...   \n",
       "4                        0.0                           0.0  ...   \n",
       "5                        0.0                           0.0  ...   \n",
       "6                        1.0                           0.0  ...   \n",
       "\n",
       "   mds-bims-summary-ranking_Intact or Moderately Intact  \\\n",
       "0                                                1.0      \n",
       "1                                                1.0      \n",
       "4                                                1.0      \n",
       "5                                                1.0      \n",
       "6                                                1.0      \n",
       "\n",
       "   mds-bims-summary-ranking_Moderate Impairment  \\\n",
       "0                                           0.0   \n",
       "1                                           0.0   \n",
       "4                                           0.0   \n",
       "5                                           0.0   \n",
       "6                                           0.0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_No  \\\n",
       "0                               1.0   \n",
       "1                               1.0   \n",
       "4                               1.0   \n",
       "5                               1.0   \n",
       "6                               1.0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_meclAndPsychStartSameR  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "4                                                0.0      \n",
       "5                                                0.0      \n",
       "6                                                0.0      \n",
       "\n",
       "   ws-meclizine-psych-coexposure_meclPrecedesPsychR  \\\n",
       "0                                               0.0   \n",
       "1                                               0.0   \n",
       "4                                               0.0   \n",
       "5                                               0.0   \n",
       "6                                               0.0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_psychPrecedesMeclR  \\\n",
       "0                                               0.0   \n",
       "1                                               0.0   \n",
       "4                                               0.0   \n",
       "5                                               0.0   \n",
       "6                                               0.0   \n",
       "\n",
       "   ws-meclizine-psych-coexposure_meclAndPsychOlderStart  input_outcome_x  \\\n",
       "0                                                0.0                 0.0   \n",
       "1                                                0.0                 0.0   \n",
       "4                                                0.0                 0.0   \n",
       "5                                                0.0                 0.0   \n",
       "6                                                0.0                 0.0   \n",
       "\n",
       "   input_outcome_y  patient-id  \n",
       "0              0.0       10353  \n",
       "1              0.0       10364  \n",
       "4              0.0       10386  \n",
       "5              0.0        5000  \n",
       "6              0.0        5003  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_nested_list(X, Y):\n",
    "    x_list, y_list = [], [] \n",
    "    pids = X['patient-id'].unique()\n",
    "    for pid in pids:\n",
    "        df = X[X['patient-id']==pid]\n",
    "        y_list.append(Y[df.index].values)\n",
    "        x_list.append([record for record in df.drop('patient-id', axis=1).values])\n",
    "    return x_list, y_list\n",
    "\n",
    "def mygenerator(x_list, y_list=None):\n",
    "    if(y_list is not None):\n",
    "        while True:\n",
    "            for x, y in zip(x_list, y_list):\n",
    "                yield np.array(x).reshape((len(x), 1, x[0].shape[0])), y\n",
    "    else:\n",
    "        while True:\n",
    "            for x in x_list:\n",
    "                yield np.array(x).reshape((len(x), 1, x[0].shape[0])) \n",
    "                \n",
    "def fit_model(model, epoch, X_train, y_train, X_valid=None, y_valid=None, verbose=0):\n",
    "    history = model.fit(X_train, y_train, epochs=epoch, verbose=verbose)\n",
    "    if(verbose != 0):\n",
    "        plot_history(history)\n",
    "    return history\n",
    "\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % auc(fpr, tpr))\n",
    "    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    \n",
    "def plot_history(history, loss=None, val_loss=None):\n",
    "    if(history is not None):\n",
    "        loss = history.history['loss']\n",
    "    plt.figure()\n",
    "    plt.plot(loss)\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    if('val_loss' in history.history):\n",
    "        val_loss = history.history['val_loss']\n",
    "        plt.plot(val_loss)\n",
    "    plt.legend(['train', 'test'], loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_data(X, maxlen):\n",
    "    return pad_sequences(X, maxlen=maxlen, dtype='float64', padding='post')\n",
    "\n",
    "def pad_all(X_train, y_train, X_test, y_test, maxlen):\n",
    "    return pad_data(X_train, maxlen), pad_data(y_train, maxlen), pad_data(X_test, maxlen), pad_data(y_test, maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def auroc(y_predict, y_true, plot=False):\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_predict, pos_label=1)    \n",
    "    if(plot):\n",
    "        plot_roc_curve(fpr, tpr)\n",
    "    return auc(fpr, tpr)\n",
    "\n",
    "\n",
    "def find_best_f1(y_predict, y_true):\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_predict, pos_label=1) \n",
    "    max_f1, th = 0, 0\n",
    "    for threshold in thresholds:\n",
    "        f1 = f1_score(y_true, y_predict > threshold)\n",
    "        if(f1 > max_f1):\n",
    "            max_f1 = f1\n",
    "            th = threshold\n",
    "    return max_f1, th\n",
    "\n",
    "def other_metrics(y_predict, y_true, threshold):\n",
    "    y_threshold = y_predict > threshold\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_threshold).ravel()\n",
    "    return precision_score(y_true, y_threshold), recall_score(y_true, y_threshold),\\\n",
    "            accuracy_score(y_true, y_threshold), float(tn) / (tn + fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "def kfold_validation(unit, ratio, epoch, X, y, groupby, k=5, verbose=0, max_timestamps=20, n_features=101):\n",
    "    auroc_list = []\n",
    "    other_metric_list = []\n",
    "    for train_idx, valid_idx in GroupKFold(n_splits=5).split(X, y, X[groupby]):\n",
    "        X_train, X_valid, y_train, y_valid = X.iloc[train_idx], X.iloc[valid_idx], y.iloc[train_idx], y.iloc[valid_idx]\n",
    "        X_train, y_train = generate_nested_list(X_train, y_train)\n",
    "        X_valid, y_valid = generate_nested_list(X_valid, y_valid)\n",
    "        X_train_pad, y_train_pad, X_valid_pad, y_valid_pad = pad_all(X_train, y_train, X_valid, y_valid, max_timestamps)\n",
    "        y_train_pad = y_train_pad.reshape(y_train_pad.shape[0], y_train_pad.shape[1], 1)\n",
    "        seed(1)\n",
    "        set_random_seed(2)\n",
    "        model = build_model(unit, ratio, max_timestamps, n_features)\n",
    "        fit_model(model, epoch, X_train_pad, y_train_pad, verbose=verbose)\n",
    "        y_valid_len = [len(y) for y in y_valid]\n",
    "        y_predict = model.predict(X_valid_pad)\n",
    "        y_predict = truncate_prediction(y_predict, y_valid_len)\n",
    "        y_true = np.concatenate(y_valid)\n",
    "        auroc_list.append(auroc(y_predict, y_true))\n",
    "        f1, th = find_best_f1(y_predict, y_true)\n",
    "        precision, recall, accuracy, specificity = other_metrics(y_predict, y_true, th)\n",
    "        other_metric_list.append((precision, recall, accuracy, specificity,f1,th))\n",
    "    return (auroc_list, other_metric_list) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# shape: (num_of_patients, num_of_records, num_of_features)\n",
    "X_train_list, y_train_list = generate_nested_list(X_train_transformed, y_train)\n",
    "X_test_list, y_test_list = generate_nested_list(X_test_transformed, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 3,\n",
       " 1]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(x) for x in X_train_list[0:200]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.00636943, 0.00634249, 0.        , 1.        , 0.8255814 ,\n",
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       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.25      , 0.25      ,\n",
       "        1.        , 0.        , 1.        , 0.        , 0.67857143,\n",
       "        0.03676471, 0.        , 0.01563858, 0.01937984, 0.        ,\n",
       "        0.        , 0.41421356, 1.        , 0.60134489, 0.89997622,\n",
       "        0.99672198, 0.42204048, 0.9984043 , 0.41804512, 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 1.        , 0.        , 0.        , 1.        ,\n",
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       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 1.        , 1.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        ]),\n",
       " array([0.00636943, 0.0422833 , 0.        , 1.        , 0.8255814 ,\n",
       "        0.        , 1.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.375     , 0.25      ,\n",
       "        1.        , 1.        , 1.        , 1.        , 0.71428571,\n",
       "        0.06617647, 0.        , 0.04691573, 0.02131783, 0.        ,\n",
       "        0.        , 0.41421356, 1.        , 0.41421356, 1.        ,\n",
       "        0.9971726 , 0.42097469, 0.99767413, 0.41978471, 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 1.        , 0.        , 0.        , 1.        ,\n",
       "        0.        , 1.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 1.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 1.        , 1.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        1.        ])]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_list[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "max_timestamps = df.groupby(['patient-id']).size().max()\n",
    "print(max_timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n"
     ]
    }
   ],
   "source": [
    "n_features = X_train_list[0][0].shape[0]\n",
    "print(n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Use an LSTM \n",
    "from keras import regularizers\n",
    "from keras.optimizers import Adagrad, Adadelta\n",
    "def build_model(units, dropout_ratio, n_timesteps, n_dimensions):\n",
    "    model = Sequential()\n",
    "    model.add(Masking(input_shape=(n_timesteps, n_dimensions)))\n",
    "    model.add(LSTM(units, return_sequences=True, recurrent_dropout=dropout_ratio, dropout=0.5))\n",
    "    model.add(TimeDistributed(Dense(1, activation='sigmoid')))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adagrad', metrics=['binary_accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def truncate_prediction(y_, y_len):\n",
    "    y_concat = []\n",
    "    for i, y in enumerate(y_):\n",
    "        y_concat.append(np.concatenate(y[:y_len[i], :]))\n",
    "    y_concat = np.concatenate(y_concat)\n",
    "    return y_concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pad, y_train_pad, X_test_pad, y_test_pad = pad_all(X_train_list, y_train_list, X_test_list, y_test_list, max_timestamps)\n",
    "y_train_pad = y_train_pad.reshape((2193, 19, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_pad[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0424 15:33:11.598643 140690129766208 deprecation_wrapper.py:119] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0424 15:33:11.600793 140690129766208 deprecation_wrapper.py:119] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0424 15:33:11.613076 140690129766208 deprecation_wrapper.py:119] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0424 15:33:11.701265 140690129766208 deprecation_wrapper.py:119] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0424 15:33:11.716621 140690129766208 deprecation.py:506] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "W0424 15:33:12.078319 140690129766208 deprecation.py:323] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:2974: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "W0424 15:33:12.142205 140690129766208 deprecation_wrapper.py:119] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0424 15:33:12.160117 140690129766208 deprecation_wrapper.py:119] From /usr/local/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/175\n",
      "2193/2193 [==============================] - 4s 2ms/step - loss: 0.4838 - binary_accuracy: 0.8156\n",
      "Epoch 2/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4518 - binary_accuracy: 0.8214\n",
      "Epoch 3/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4390 - binary_accuracy: 0.8264\n",
      "Epoch 4/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4397 - binary_accuracy: 0.8219\n",
      "Epoch 5/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4399 - binary_accuracy: 0.8194\n",
      "Epoch 6/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4371 - binary_accuracy: 0.8214\n",
      "Epoch 7/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4348 - binary_accuracy: 0.8227\n",
      "Epoch 8/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4309 - binary_accuracy: 0.8217\n",
      "Epoch 9/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4274 - binary_accuracy: 0.8267\n",
      "Epoch 10/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4315 - binary_accuracy: 0.8213\n",
      "Epoch 11/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4275 - binary_accuracy: 0.8229\n",
      "Epoch 12/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4251 - binary_accuracy: 0.8221\n",
      "Epoch 13/175\n",
      "2193/2193 [==============================] - 3s 1ms/step - loss: 0.4295 - binary_accuracy: 0.8220\n",
      "Epoch 14/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4240 - binary_accuracy: 0.8249\n",
      "Epoch 15/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4169 - binary_accuracy: 0.8282\n",
      "Epoch 16/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4245 - binary_accuracy: 0.8245\n",
      "Epoch 17/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4216 - binary_accuracy: 0.8285\n",
      "Epoch 18/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4234 - binary_accuracy: 0.8262\n",
      "Epoch 19/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4261 - binary_accuracy: 0.8240\n",
      "Epoch 20/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4211 - binary_accuracy: 0.8264\n",
      "Epoch 21/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4202 - binary_accuracy: 0.8274\n",
      "Epoch 22/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4284 - binary_accuracy: 0.8221\n",
      "Epoch 23/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4188 - binary_accuracy: 0.8284\n",
      "Epoch 24/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4188 - binary_accuracy: 0.8256\n",
      "Epoch 25/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4155 - binary_accuracy: 0.8273\n",
      "Epoch 26/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4205 - binary_accuracy: 0.8252\n",
      "Epoch 27/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4149 - binary_accuracy: 0.8303\n",
      "Epoch 28/175\n",
      "2193/2193 [==============================] - 2s 1ms/step - loss: 0.4209 - binary_accuracy: 0.8205\n",
      "Epoch 29/175\n",
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     ]
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auroc:  0.7282291991760813\n",
      "f1:  0.44736842105263164\n",
      "threshold:  0.18032414\n",
      "precision:  0.37717908082408874\n",
      "recall:  0.5496535796766744\n",
      "accuracy:  0.7424441524310118\n",
      "specificity:  0.7875675675675675\n",
      "CPU times: user 23min 2s, sys: 5min 12s, total: 28min 14s\n",
      "Wall time: 6min 44s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "### A single run w/out cross-validation\n",
    "# with previous outcome added to the features\n",
    "# outcome is added before scaling\n",
    "seed(1)\n",
    "set_random_seed(2)\n",
    "model = build_model(32, 0.5, max_timestamps, n_features)\n",
    "fit_model(model, 175, X_train_pad, y_train_pad, verbose=1)\n",
    "y_predict = model.predict(X_test_pad)\n",
    "y_test_len = [len(y) for y in y_test_list]\n",
    "y_predict = truncate_prediction(y_predict, y_test_len)\n",
    "y_true = np.concatenate(y_test_list)\n",
    "print('auroc: ', auroc(y_predict, y_true))\n",
    "f1, th = find_best_f1(y_predict, y_true)\n",
    "print('f1: ', f1);\n",
    "print('threshold: ', th)\n",
    "precision, recall, accuracy, specificity = other_metrics(y_predict, y_true, th)\n",
    "print('precision: ', precision)\n",
    "print('recall: ', recall)\n",
    "print('accuracy: ', accuracy)\n",
    "print('specificity: ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/225\n",
      "1755/1755 [==============================] - 3s 2ms/step - loss: 0.4883 - binary_accuracy: 0.8108\n",
      "Epoch 2/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4534 - binary_accuracy: 0.8210\n",
      "Epoch 3/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4470 - binary_accuracy: 0.8200\n",
      "Epoch 4/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4368 - binary_accuracy: 0.8253\n",
      "Epoch 5/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4393 - binary_accuracy: 0.8214\n",
      "Epoch 6/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4311 - binary_accuracy: 0.8237\n",
      "Epoch 7/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4337 - binary_accuracy: 0.8246\n",
      "Epoch 8/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4273 - binary_accuracy: 0.8250\n",
      "Epoch 9/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4305 - binary_accuracy: 0.8215\n",
      "Epoch 10/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4273 - binary_accuracy: 0.8230\n",
      "Epoch 11/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4260 - binary_accuracy: 0.8269\n",
      "Epoch 12/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4301 - binary_accuracy: 0.8204\n",
      "Epoch 13/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4286 - binary_accuracy: 0.8212\n",
      "Epoch 14/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4335 - binary_accuracy: 0.8175\n",
      "Epoch 15/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4292 - binary_accuracy: 0.8255\n",
      "Epoch 16/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4250 - binary_accuracy: 0.8259\n",
      "Epoch 17/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4256 - binary_accuracy: 0.8248\n",
      "Epoch 18/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4248 - binary_accuracy: 0.8238\n",
      "Epoch 19/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4263 - binary_accuracy: 0.8204\n",
      "Epoch 20/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4207 - binary_accuracy: 0.8264\n",
      "Epoch 21/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4234 - binary_accuracy: 0.8234\n",
      "Epoch 22/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4223 - binary_accuracy: 0.8231\n",
      "Epoch 23/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4231 - binary_accuracy: 0.8196\n",
      "Epoch 24/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4226 - binary_accuracy: 0.8274\n",
      "Epoch 25/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4265 - binary_accuracy: 0.8243\n",
      "Epoch 26/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4131 - binary_accuracy: 0.8324\n",
      "Epoch 27/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4202 - binary_accuracy: 0.8280\n",
      "Epoch 28/225\n",
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      "Epoch 185/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3994 - binary_accuracy: 0.8355\n",
      "Epoch 186/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4013 - binary_accuracy: 0.8320\n",
      "Epoch 187/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4009 - binary_accuracy: 0.8285\n",
      "Epoch 188/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4024 - binary_accuracy: 0.8339\n",
      "Epoch 189/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4011 - binary_accuracy: 0.8300\n",
      "Epoch 190/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4007 - binary_accuracy: 0.8329\n",
      "Epoch 191/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4037 - binary_accuracy: 0.8343\n",
      "Epoch 192/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4042 - binary_accuracy: 0.8296\n",
      "Epoch 193/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4014 - binary_accuracy: 0.8329\n",
      "Epoch 194/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4014 - binary_accuracy: 0.8314\n",
      "Epoch 195/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4017 - binary_accuracy: 0.8354\n",
      "Epoch 196/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4057 - binary_accuracy: 0.8296\n",
      "Epoch 197/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4043 - binary_accuracy: 0.8314\n",
      "Epoch 198/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4036 - binary_accuracy: 0.8268\n",
      "Epoch 199/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4012 - binary_accuracy: 0.8353\n",
      "Epoch 200/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3971 - binary_accuracy: 0.8303\n",
      "Epoch 201/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4075 - binary_accuracy: 0.8263\n",
      "Epoch 202/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3964 - binary_accuracy: 0.8353\n",
      "Epoch 203/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4025 - binary_accuracy: 0.8275\n",
      "Epoch 204/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4012 - binary_accuracy: 0.8289\n",
      "Epoch 205/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4058 - binary_accuracy: 0.8285\n",
      "Epoch 206/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3987 - binary_accuracy: 0.8329\n",
      "Epoch 207/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3974 - binary_accuracy: 0.8349\n",
      "Epoch 208/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3967 - binary_accuracy: 0.8312\n",
      "Epoch 209/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3946 - binary_accuracy: 0.8343\n",
      "Epoch 210/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4025 - binary_accuracy: 0.8333\n",
      "Epoch 211/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3971 - binary_accuracy: 0.8301\n",
      "Epoch 212/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3946 - binary_accuracy: 0.8376\n",
      "Epoch 213/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4037 - binary_accuracy: 0.8333\n",
      "Epoch 214/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4076 - binary_accuracy: 0.8281\n",
      "Epoch 215/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3978 - binary_accuracy: 0.8336\n",
      "Epoch 216/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4003 - binary_accuracy: 0.8335\n",
      "Epoch 217/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3931 - binary_accuracy: 0.8365\n",
      "Epoch 218/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3989 - binary_accuracy: 0.8357\n",
      "Epoch 219/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4055 - binary_accuracy: 0.8339\n",
      "Epoch 220/225\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3980 - binary_accuracy: 0.8353\n",
      "Epoch 221/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3978 - binary_accuracy: 0.8324A: 0s - loss: 0.3890 - binary_accuracy: \n",
      "Epoch 222/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3976 - binary_accuracy: 0.8348\n",
      "Epoch 223/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3955 - binary_accuracy: 0.8305\n",
      "Epoch 224/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4072 - binary_accuracy: 0.8308\n",
      "Epoch 225/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4013 - binary_accuracy: 0.8312\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/225\n",
      "1755/1755 [==============================] - 3s 2ms/step - loss: 0.4854 - binary_accuracy: 0.8157\n",
      "Epoch 2/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4519 - binary_accuracy: 0.8244\n",
      "Epoch 3/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4416 - binary_accuracy: 0.8245\n",
      "Epoch 4/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4407 - binary_accuracy: 0.8229\n",
      "Epoch 5/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4387 - binary_accuracy: 0.8228\n",
      "Epoch 6/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4317 - binary_accuracy: 0.8259\n",
      "Epoch 7/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4347 - binary_accuracy: 0.8225\n",
      "Epoch 8/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4286 - binary_accuracy: 0.8281\n",
      "Epoch 9/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4334 - binary_accuracy: 0.8224\n",
      "Epoch 10/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4259 - binary_accuracy: 0.8271\n",
      "Epoch 11/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4294 - binary_accuracy: 0.8248\n",
      "Epoch 12/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4239 - binary_accuracy: 0.8255\n",
      "Epoch 13/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4293 - binary_accuracy: 0.8275\n",
      "Epoch 14/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4272 - binary_accuracy: 0.8230\n",
      "Epoch 15/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4219 - binary_accuracy: 0.8276\n",
      "Epoch 16/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4284 - binary_accuracy: 0.8305\n",
      "Epoch 17/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4259 - binary_accuracy: 0.8283\n",
      "Epoch 18/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4238 - binary_accuracy: 0.8268\n",
      "Epoch 19/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4211 - binary_accuracy: 0.8307\n",
      "Epoch 20/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4245 - binary_accuracy: 0.8263\n",
      "Epoch 21/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4179 - binary_accuracy: 0.8261\n",
      "Epoch 22/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4195 - binary_accuracy: 0.8282\n",
      "Epoch 23/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4194 - binary_accuracy: 0.8317\n",
      "Epoch 24/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4196 - binary_accuracy: 0.8307\n",
      "Epoch 25/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4166 - binary_accuracy: 0.8257\n",
      "Epoch 26/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4158 - binary_accuracy: 0.8285\n",
      "Epoch 27/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4268 - binary_accuracy: 0.8246\n",
      "Epoch 28/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4149 - binary_accuracy: 0.8291\n",
      "Epoch 29/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4217 - binary_accuracy: 0.8239\n",
      "Epoch 30/225\n",
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      "Epoch 187/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3923 - binary_accuracy: 0.8382\n",
      "Epoch 188/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4002 - binary_accuracy: 0.8366\n",
      "Epoch 189/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4011 - binary_accuracy: 0.8353\n",
      "Epoch 190/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4003 - binary_accuracy: 0.8386\n",
      "Epoch 191/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3967 - binary_accuracy: 0.8369\n",
      "Epoch 192/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3994 - binary_accuracy: 0.8344\n",
      "Epoch 193/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4080 - binary_accuracy: 0.8307\n",
      "Epoch 194/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3985 - binary_accuracy: 0.8365\n",
      "Epoch 195/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4054 - binary_accuracy: 0.8298\n",
      "Epoch 196/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4007 - binary_accuracy: 0.8336\n",
      "Epoch 197/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4013 - binary_accuracy: 0.8374\n",
      "Epoch 198/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3944 - binary_accuracy: 0.8379\n",
      "Epoch 199/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3966 - binary_accuracy: 0.8381\n",
      "Epoch 200/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3991 - binary_accuracy: 0.8342\n",
      "Epoch 201/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4021 - binary_accuracy: 0.8348\n",
      "Epoch 202/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3969 - binary_accuracy: 0.8354\n",
      "Epoch 203/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3990 - binary_accuracy: 0.8371\n",
      "Epoch 204/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4017 - binary_accuracy: 0.8311\n",
      "Epoch 205/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4038 - binary_accuracy: 0.8314\n",
      "Epoch 206/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4061 - binary_accuracy: 0.8316\n",
      "Epoch 207/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4080 - binary_accuracy: 0.8346\n",
      "Epoch 208/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3938 - binary_accuracy: 0.8383\n",
      "Epoch 209/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4038 - binary_accuracy: 0.8356\n",
      "Epoch 210/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4021 - binary_accuracy: 0.8309\n",
      "Epoch 211/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3978 - binary_accuracy: 0.8396\n",
      "Epoch 212/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3930 - binary_accuracy: 0.8396\n",
      "Epoch 213/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3970 - binary_accuracy: 0.8361\n",
      "Epoch 214/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4049 - binary_accuracy: 0.8329\n",
      "Epoch 215/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3976 - binary_accuracy: 0.8339\n",
      "Epoch 216/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4018 - binary_accuracy: 0.8297\n",
      "Epoch 217/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3972 - binary_accuracy: 0.8367\n",
      "Epoch 218/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4047 - binary_accuracy: 0.8331\n",
      "Epoch 219/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4022 - binary_accuracy: 0.8309\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 220/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4026 - binary_accuracy: 0.8339\n",
      "Epoch 221/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4043 - binary_accuracy: 0.8348\n",
      "Epoch 222/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.3953 - binary_accuracy: 0.8380\n",
      "Epoch 223/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4055 - binary_accuracy: 0.8316\n",
      "Epoch 224/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4005 - binary_accuracy: 0.8337\n",
      "Epoch 225/225\n",
      "1755/1755 [==============================] - 2s 1ms/step - loss: 0.4017 - binary_accuracy: 0.8373\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/225\n",
      "1754/1754 [==============================] - 4s 2ms/step - loss: 0.4867 - binary_accuracy: 0.8130\n",
      "Epoch 2/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4508 - binary_accuracy: 0.8218\n",
      "Epoch 3/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4406 - binary_accuracy: 0.8227\n",
      "Epoch 4/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4399 - binary_accuracy: 0.8257\n",
      "Epoch 5/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4342 - binary_accuracy: 0.8235\n",
      "Epoch 6/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4284 - binary_accuracy: 0.8242\n",
      "Epoch 7/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4297 - binary_accuracy: 0.8228\n",
      "Epoch 8/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4251 - binary_accuracy: 0.8252\n",
      "Epoch 9/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4298 - binary_accuracy: 0.8228\n",
      "Epoch 10/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4181 - binary_accuracy: 0.8307\n",
      "Epoch 11/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4207 - binary_accuracy: 0.8250\n",
      "Epoch 12/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4224 - binary_accuracy: 0.8284\n",
      "Epoch 13/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4248 - binary_accuracy: 0.8285\n",
      "Epoch 14/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4218 - binary_accuracy: 0.8277\n",
      "Epoch 15/225\n",
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      "Epoch 172/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4010 - binary_accuracy: 0.8330\n",
      "Epoch 173/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4032 - binary_accuracy: 0.8316\n",
      "Epoch 174/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4003 - binary_accuracy: 0.8347\n",
      "Epoch 175/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4048 - binary_accuracy: 0.8294\n",
      "Epoch 176/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4040 - binary_accuracy: 0.8291\n",
      "Epoch 177/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3951 - binary_accuracy: 0.8338\n",
      "Epoch 178/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4008 - binary_accuracy: 0.8349\n",
      "Epoch 179/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3961 - binary_accuracy: 0.8353\n",
      "Epoch 180/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3921 - binary_accuracy: 0.8392\n",
      "Epoch 181/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3984 - binary_accuracy: 0.8343\n",
      "Epoch 182/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3983 - binary_accuracy: 0.8324\n",
      "Epoch 183/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3990 - binary_accuracy: 0.8330\n",
      "Epoch 184/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3982 - binary_accuracy: 0.8317\n",
      "Epoch 185/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3973 - binary_accuracy: 0.8367\n",
      "Epoch 186/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4031 - binary_accuracy: 0.8333\n",
      "Epoch 187/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4030 - binary_accuracy: 0.8298\n",
      "Epoch 188/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4008 - binary_accuracy: 0.8365\n",
      "Epoch 189/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3967 - binary_accuracy: 0.8337\n",
      "Epoch 190/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3991 - binary_accuracy: 0.8386\n",
      "Epoch 191/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3989 - binary_accuracy: 0.8355\n",
      "Epoch 192/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4008 - binary_accuracy: 0.8344\n",
      "Epoch 193/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3990 - binary_accuracy: 0.8339\n",
      "Epoch 194/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3943 - binary_accuracy: 0.8389\n",
      "Epoch 195/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4009 - binary_accuracy: 0.8306\n",
      "Epoch 196/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4024 - binary_accuracy: 0.8291\n",
      "Epoch 197/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4035 - binary_accuracy: 0.8336\n",
      "Epoch 198/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4007 - binary_accuracy: 0.8361\n",
      "Epoch 199/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3986 - binary_accuracy: 0.8308\n",
      "Epoch 200/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4030 - binary_accuracy: 0.8346\n",
      "Epoch 201/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4055 - binary_accuracy: 0.8322\n",
      "Epoch 202/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4027 - binary_accuracy: 0.8308\n",
      "Epoch 203/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3993 - binary_accuracy: 0.8349\n",
      "Epoch 204/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3948 - binary_accuracy: 0.8360\n",
      "Epoch 205/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3934 - binary_accuracy: 0.8347\n",
      "Epoch 206/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3942 - binary_accuracy: 0.8404\n",
      "Epoch 207/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3989 - binary_accuracy: 0.8362\n",
      "Epoch 208/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3964 - binary_accuracy: 0.8356\n",
      "Epoch 209/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3985 - binary_accuracy: 0.8311\n",
      "Epoch 210/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4037 - binary_accuracy: 0.8339\n",
      "Epoch 211/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3926 - binary_accuracy: 0.8340\n",
      "Epoch 212/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4029 - binary_accuracy: 0.8297\n",
      "Epoch 213/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3995 - binary_accuracy: 0.8334\n",
      "Epoch 214/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3968 - binary_accuracy: 0.8354\n",
      "Epoch 215/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3929 - binary_accuracy: 0.8381\n",
      "Epoch 216/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4007 - binary_accuracy: 0.8336\n",
      "Epoch 217/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3982 - binary_accuracy: 0.8375\n",
      "Epoch 218/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3987 - binary_accuracy: 0.8358\n",
      "Epoch 219/225\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4014 - binary_accuracy: 0.8354\n",
      "Epoch 220/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3976 - binary_accuracy: 0.8328\n",
      "Epoch 221/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4025 - binary_accuracy: 0.8330\n",
      "Epoch 222/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3947 - binary_accuracy: 0.8359\n",
      "Epoch 223/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4041 - binary_accuracy: 0.8332\n",
      "Epoch 224/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4008 - binary_accuracy: 0.8325\n",
      "Epoch 225/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3973 - binary_accuracy: 0.8354\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/225\n",
      "1754/1754 [==============================] - 4s 2ms/step - loss: 0.4859 - binary_accuracy: 0.8103\n",
      "Epoch 2/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4597 - binary_accuracy: 0.8185\n",
      "Epoch 3/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4495 - binary_accuracy: 0.8190\n",
      "Epoch 4/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4450 - binary_accuracy: 0.8169\n",
      "Epoch 5/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4350 - binary_accuracy: 0.8234\n",
      "Epoch 6/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4387 - binary_accuracy: 0.8214\n",
      "Epoch 7/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4369 - binary_accuracy: 0.8182\n",
      "Epoch 8/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4355 - binary_accuracy: 0.8210\n",
      "Epoch 9/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4338 - binary_accuracy: 0.8223\n",
      "Epoch 10/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4245 - binary_accuracy: 0.8256\n",
      "Epoch 11/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4336 - binary_accuracy: 0.8214\n",
      "Epoch 12/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4313 - binary_accuracy: 0.8232\n",
      "Epoch 13/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4319 - binary_accuracy: 0.8196\n",
      "Epoch 14/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4270 - binary_accuracy: 0.8269\n",
      "Epoch 15/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4276 - binary_accuracy: 0.8221\n",
      "Epoch 16/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4259 - binary_accuracy: 0.8256\n",
      "Epoch 17/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4263 - binary_accuracy: 0.8246\n",
      "Epoch 18/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4218 - binary_accuracy: 0.8257\n",
      "Epoch 19/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4222 - binary_accuracy: 0.8235\n",
      "Epoch 20/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4207 - binary_accuracy: 0.8249\n",
      "Epoch 21/225\n",
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      "Epoch 179/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4086 - binary_accuracy: 0.8289\n",
      "Epoch 180/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4056 - binary_accuracy: 0.8283\n",
      "Epoch 181/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4100 - binary_accuracy: 0.8276\n",
      "Epoch 182/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4075 - binary_accuracy: 0.8283\n",
      "Epoch 183/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4011 - binary_accuracy: 0.8302\n",
      "Epoch 184/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4103 - binary_accuracy: 0.8250\n",
      "Epoch 185/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4123 - binary_accuracy: 0.8234\n",
      "Epoch 186/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4060 - binary_accuracy: 0.8314\n",
      "Epoch 187/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3981 - binary_accuracy: 0.8325\n",
      "Epoch 188/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3958 - binary_accuracy: 0.8363\n",
      "Epoch 189/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4038 - binary_accuracy: 0.8290\n",
      "Epoch 190/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4051 - binary_accuracy: 0.8294\n",
      "Epoch 191/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4130 - binary_accuracy: 0.8257\n",
      "Epoch 192/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4035 - binary_accuracy: 0.8269\n",
      "Epoch 193/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4049 - binary_accuracy: 0.8258\n",
      "Epoch 194/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4024 - binary_accuracy: 0.8334\n",
      "Epoch 195/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4096 - binary_accuracy: 0.8223\n",
      "Epoch 196/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4106 - binary_accuracy: 0.8252\n",
      "Epoch 197/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4055 - binary_accuracy: 0.8239\n",
      "Epoch 198/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4065 - binary_accuracy: 0.8263\n",
      "Epoch 199/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4079 - binary_accuracy: 0.8301\n",
      "Epoch 200/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4078 - binary_accuracy: 0.8278\n",
      "Epoch 201/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4055 - binary_accuracy: 0.8302\n",
      "Epoch 202/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3986 - binary_accuracy: 0.8364\n",
      "Epoch 203/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4059 - binary_accuracy: 0.8264\n",
      "Epoch 204/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4041 - binary_accuracy: 0.8309\n",
      "Epoch 205/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4066 - binary_accuracy: 0.8266\n",
      "Epoch 206/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4045 - binary_accuracy: 0.8301\n",
      "Epoch 207/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4043 - binary_accuracy: 0.8301\n",
      "Epoch 208/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4017 - binary_accuracy: 0.8351\n",
      "Epoch 209/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4065 - binary_accuracy: 0.8289\n",
      "Epoch 210/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4039 - binary_accuracy: 0.8293\n",
      "Epoch 211/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4061 - binary_accuracy: 0.8285\n",
      "Epoch 212/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4044 - binary_accuracy: 0.8270\n",
      "Epoch 213/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4036 - binary_accuracy: 0.8337\n",
      "Epoch 214/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4036 - binary_accuracy: 0.8290\n",
      "Epoch 215/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4047 - binary_accuracy: 0.8307\n",
      "Epoch 216/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4047 - binary_accuracy: 0.8304\n",
      "Epoch 217/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4009 - binary_accuracy: 0.8357\n",
      "Epoch 218/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4019 - binary_accuracy: 0.8309\n",
      "Epoch 219/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4003 - binary_accuracy: 0.8349\n",
      "Epoch 220/225\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4041 - binary_accuracy: 0.8311\n",
      "Epoch 221/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4053 - binary_accuracy: 0.8271\n",
      "Epoch 222/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4065 - binary_accuracy: 0.8259\n",
      "Epoch 223/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4011 - binary_accuracy: 0.8283\n",
      "Epoch 224/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4013 - binary_accuracy: 0.8308\n",
      "Epoch 225/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4079 - binary_accuracy: 0.8243\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/225\n",
      "1754/1754 [==============================] - 4s 2ms/step - loss: 0.4858 - binary_accuracy: 0.8095\n",
      "Epoch 2/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4570 - binary_accuracy: 0.8177\n",
      "Epoch 3/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4501 - binary_accuracy: 0.8181\n",
      "Epoch 4/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4438 - binary_accuracy: 0.8220\n",
      "Epoch 5/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4416 - binary_accuracy: 0.8202\n",
      "Epoch 6/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4345 - binary_accuracy: 0.8198\n",
      "Epoch 7/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4352 - binary_accuracy: 0.8226\n",
      "Epoch 8/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4340 - binary_accuracy: 0.8213\n",
      "Epoch 9/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4271 - binary_accuracy: 0.8239\n",
      "Epoch 10/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4325 - binary_accuracy: 0.8206\n",
      "Epoch 11/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4282 - binary_accuracy: 0.8224\n",
      "Epoch 12/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4288 - binary_accuracy: 0.8263\n",
      "Epoch 13/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4257 - binary_accuracy: 0.8235\n",
      "Epoch 14/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4270 - binary_accuracy: 0.8211\n",
      "Epoch 15/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4288 - binary_accuracy: 0.8204\n",
      "Epoch 16/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4283 - binary_accuracy: 0.8236\n",
      "Epoch 17/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4248 - binary_accuracy: 0.8247\n",
      "Epoch 18/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4196 - binary_accuracy: 0.8236\n",
      "Epoch 19/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4293 - binary_accuracy: 0.8222\n",
      "Epoch 20/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4274 - binary_accuracy: 0.8198\n",
      "Epoch 21/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4248 - binary_accuracy: 0.8247\n",
      "Epoch 22/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4239 - binary_accuracy: 0.8230\n",
      "Epoch 23/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4213 - binary_accuracy: 0.8263\n",
      "Epoch 24/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4260 - binary_accuracy: 0.8210\n",
      "Epoch 25/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4182 - binary_accuracy: 0.8292\n",
      "Epoch 26/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4233 - binary_accuracy: 0.8237\n",
      "Epoch 27/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4246 - binary_accuracy: 0.8225\n",
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      "Epoch 185/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4059 - binary_accuracy: 0.8299\n",
      "Epoch 186/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4044 - binary_accuracy: 0.8277\n",
      "Epoch 187/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4118 - binary_accuracy: 0.8258\n",
      "Epoch 188/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4036 - binary_accuracy: 0.8305\n",
      "Epoch 189/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4104 - binary_accuracy: 0.8276\n",
      "Epoch 190/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4022 - binary_accuracy: 0.8275\n",
      "Epoch 191/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3979 - binary_accuracy: 0.8382\n",
      "Epoch 192/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4030 - binary_accuracy: 0.8303\n",
      "Epoch 193/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4072 - binary_accuracy: 0.8290\n",
      "Epoch 194/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4035 - binary_accuracy: 0.8271\n",
      "Epoch 195/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4038 - binary_accuracy: 0.8316\n",
      "Epoch 196/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4071 - binary_accuracy: 0.8292\n",
      "Epoch 197/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4029 - binary_accuracy: 0.8329\n",
      "Epoch 198/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4109 - binary_accuracy: 0.8293\n",
      "Epoch 199/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4042 - binary_accuracy: 0.8303\n",
      "Epoch 200/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4023 - binary_accuracy: 0.8325\n",
      "Epoch 201/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4007 - binary_accuracy: 0.8326\n",
      "Epoch 202/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4043 - binary_accuracy: 0.8268\n",
      "Epoch 203/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4110 - binary_accuracy: 0.8270\n",
      "Epoch 204/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4032 - binary_accuracy: 0.8308\n",
      "Epoch 205/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3992 - binary_accuracy: 0.8292\n",
      "Epoch 206/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3990 - binary_accuracy: 0.8299\n",
      "Epoch 207/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3983 - binary_accuracy: 0.8305\n",
      "Epoch 208/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4034 - binary_accuracy: 0.8244\n",
      "Epoch 209/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4062 - binary_accuracy: 0.8263\n",
      "Epoch 210/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4027 - binary_accuracy: 0.8329\n",
      "Epoch 211/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4059 - binary_accuracy: 0.8292\n",
      "Epoch 212/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4067 - binary_accuracy: 0.8267\n",
      "Epoch 213/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4035 - binary_accuracy: 0.8301\n",
      "Epoch 214/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4018 - binary_accuracy: 0.8307\n",
      "Epoch 215/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4039 - binary_accuracy: 0.8275\n",
      "Epoch 216/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4003 - binary_accuracy: 0.8325\n",
      "Epoch 217/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4081 - binary_accuracy: 0.8237\n",
      "Epoch 218/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.3987 - binary_accuracy: 0.8305\n",
      "Epoch 219/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4047 - binary_accuracy: 0.8280\n",
      "Epoch 220/225\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4043 - binary_accuracy: 0.8310\n",
      "Epoch 221/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4066 - binary_accuracy: 0.8293\n",
      "Epoch 222/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4059 - binary_accuracy: 0.8317\n",
      "Epoch 223/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4019 - binary_accuracy: 0.8319\n",
      "Epoch 224/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4092 - binary_accuracy: 0.8276\n",
      "Epoch 225/225\n",
      "1754/1754 [==============================] - 2s 1ms/step - loss: 0.4038 - binary_accuracy: 0.8307\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Run 5-fold cross-validation to get average performance characteristics\n",
    "unit = 32\n",
    "ratio = 0.5\n",
    "epoch = 225\n",
    "(auroc_list, other_metric_list) = kfold_validation(unit, ratio, epoch, X_train_transformed, y_train, 'patient-id', verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average precision:0.367420345444041\n",
      "average recall:0.6113446962990996\n",
      "average specificity:0.7640764206647053\n",
      "average F1:0.45583504193998525\n"
     ]
    }
   ],
   "source": [
    "## other_metric_list has precision, recall, accuracy, specificity, f1, threshold for metrics\n",
    "ave_prec = np.mean([x[0] for x in other_metric_list])\n",
    "print(\"average precision:\" + str(ave_prec))\n",
    "\n",
    "ave_recall = np.mean([x[1] for x in other_metric_list])\n",
    "print(\"average recall:\" + str(ave_recall))\n",
    "\n",
    "ave_specificity = np.mean([x[3] for x in other_metric_list])\n",
    "print(\"average specificity:\" + str(ave_specificity))\n",
    "\n",
    "ave_f1 = np.mean([x[4] for x in other_metric_list])\n",
    "print(\"average F1:\" + str(ave_f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max F1: 0.49248120300751874\n"
     ]
    }
   ],
   "source": [
    "max_f1 = np.max([x[4] for x in other_metric_list])\n",
    "print(\"max F1: \" + str(max_f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit: 32\tepoch: 225\tauroc_mean: 0.728644\tauroc_std: 0.009813\n"
     ]
    }
   ],
   "source": [
    "## Average metrics performance\n",
    "print(\"unit: %d\\tepoch: %d\\tauroc_mean: %f\\tauroc_std: %f\"  % (unit, epoch, np.mean(auroc_list), np.std(auroc_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7153920206673003,\n",
       " 0.7355418206012642,\n",
       " 0.7267156862745099,\n",
       " 0.7432787548008895,\n",
       " 0.7222924091289071]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auroc_list"
   ]
  }
 ],
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