TCARER_summaryReports.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Temporal-Comorbidity Adjusted Risk of Emergency Readmission (TCARER)\n",
    "## <font style=\"font-weight:bold;color:gray\">Summary Reports</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1.  Initialise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# reload modules\n",
    "# Reload all modules (except those excluded by %aimport) every time before executing the Python code typed.\n",
    "%load_ext autoreload \n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import logging\n",
    "import os\n",
    "import sys\n",
    "import gc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import statistics\n",
    "from datetime import datetime\n",
    "from collections import OrderedDict\n",
    "from sklearn import preprocessing\n",
    "from scipy.stats import stats\n",
    "from IPython.display import display, HTML\n",
    "from pprint import pprint\n",
    "from pivottablejs import pivot_ui\n",
    "from IPython.display import clear_output\n",
    "import imblearn.over_sampling as oversampling\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# import local classes\n",
    "from Configs.CONSTANTS import CONSTANTS\n",
    "from Configs.Logger import Logger\n",
    "from Features.Variables import Variables\n",
    "from ReadersWriters.ReadersWriters import ReadersWriters\n",
    "from Stats.PreProcess import PreProcess\n",
    "from Stats.FeatureSelection import FeatureSelection\n",
    "from Stats.TrainingMethod import TrainingMethod\n",
    "from Stats.Plots import Plots\n",
    "from Stats.Stats import Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Make sure the correct Python interpreter is used!\n",
      "3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (AMD64)]\n",
      "\n",
      "Make sure sys.path of the Python interpreter is correct!\n",
      "C:\\Users\\eagle\\Documents\\GitHub\\Analytics_UoW\\TCARER\n"
     ]
    }
   ],
   "source": [
    "# Check the interpreter\n",
    "print(\"\\nMake sure the correct Python interpreter is used!\")\n",
    "print(sys.version)\n",
    "print(\"\\nMake sure sys.path of the Python interpreter is correct!\")\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.1.  Initialise General Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output path: C:\\Users\\eagle\\Documents\\GitHub\\tmp\\TCARER\\Basic_prototype\n"
     ]
    }
   ],
   "source": [
    "# init paths & directories\n",
    "config_path = os.path.abspath(\"ConfigInputs/CONFIGURATIONS.ini\")\n",
    "io_path = os.path.abspath(\"../../tmp/TCARER/Basic_prototype\")\n",
    "schema = \"parr_sample_prototype\" \n",
    "app_name = \"T-CARER\"\n",
    "\n",
    "print(\"Output path:\", io_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-10-29 13:03:26,935 - T-CARER - INFO - Creating 'C:\\Users\\eagle\\Documents\\GitHub\\tmp\\TCARER\\Basic_prototype\\T-CARER.log' File.\n"
     ]
    }
   ],
   "source": [
    "# init logs\n",
    "if not os.path.exists(io_path):\n",
    "    os.makedirs(io_path, exist_ok=True)\n",
    "\n",
    "logger = Logger(path=io_path, app_name=app_name, ext=\"log\")\n",
    "logger = logging.getLogger(app_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# init constants        \n",
    "CONSTANTS.set(io_path, app_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# initialise other classes\n",
    "readers_writers = ReadersWriters()\n",
    "plots = Plots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# other Constant variables\n",
    "submodel_name = \"hesIp\"\n",
    "submodel_input_name = \"tcarer_model_features_ip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# set print settings\n",
    "pd.set_option('display.width', 1600, 'display.max_colwidth', 800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    " Common variables:\n",
    "* Readmission\n",
    "    * 'label30', 'label365'\n",
    "* Admissions Methods:\n",
    "    * 'admimeth\\_0t30d\\_prevalence\\_1\\_cnt', ...\n",
    "* Prior Spells: \n",
    "    * 'prior\\_spells'\n",
    "* Male:\n",
    "    * 'gender\\_1'\n",
    "* LoS:\n",
    "    * 'trigger\\_los'\n",
    "* Age:\n",
    "    * 'trigger\\_age'\n",
    "* Charlson Score:\n",
    "    * 'trigger\\_charlsonFoster'\n",
    "* predictions score\n",
    "    * score\n",
    "* Most prevalent diagnoses groups (0-30-day, 0-730-day):\n",
    "    * 0-30-day: 'diagCCS\\_0t30d\\_prevalence\\_1\\_cnt', ...\n",
    "    * 0-730-day: 'diagCCS\\_0t30d\\_prevalence\\_1\\_cnt' + 'diagCCS\\_30t90d\\_prevalence\\_1\\_cnt' + \n",
    "        'diagCCS\\_90t180d\\_prevalence\\_1\\_cnt' + 'diagCCS\\_180t365d\\_prevalence\\_1\\_cnt' + \n",
    "        'diagCCS\\_365t730d\\_prevalence\\_1\\_cnt', ...\n",
    "* Comorbidity diagnoses groups (0-730-day):\n",
    "    * 'prior\\_admiOther', 'prior\\_admiAcute', 'prior\\_spells', 'prior\\_asthma', 'prior\\_copd', 'prior\\_depression', 'prior\\_diabetes', 'prior\\_hypertension', 'prior\\_cancer', 'prior\\_chd', 'prior\\_chf'\n",
    "* Charlson diagnoses groups (trigger):\n",
    "    * 'diagCci\\_01\\_myocardial\\_freq\\_\\_trigger',..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2. Load the Saved Model Outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<font style=\"font-weight:bold;color:red\">Note: Make sure the following files are located at the input path</font>\n",
    "* Step\\_05\\_Features.bz2\n",
    "* Step\\_07\\_Top\\_Features\\_...\n",
    "* Step\\_07\\_Model\\_Train\\_model\\_rank\\_summaries\\_...\n",
    "* Step\\_09\\_Model\\_..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<font style=\"font-weight:bold;color:red\">Note: Create features extra (Run only once)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# settings\n",
    "feature_table = 'tcarer_features'\n",
    "featureExtra_table = 'tcarer_featuresExtra'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result = readers_writers.load_mysql_procedure(\"tcarer_set_featuresExtra\", [feature_table, featureExtra_table], schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2.1. Initialise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# select the target variable\n",
    "target_feature = \"label365\" # \"label365\", \"label30\"  \n",
    "method_name = \"rfc\" # \"rfc\", \"gbrt\", \"randLogit\", \"wdnn\"\n",
    "rank_models = [\"rfc\"] # [\"rfc\", \"gbrt\", \"randLogit\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2.2. Load Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Load pre-processed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File size:  97692\n",
      "Number of columns:  458\n",
      "features: {train:  2500 , test:  2499 }\n"
     ]
    }
   ],
   "source": [
    "file_name = \"Step_07_Features\"\n",
    "features = readers_writers.load_serialised_compressed(path=CONSTANTS.io_path, title=file_name)\n",
    "\n",
    "# print     \n",
    "print(\"File size: \", os.stat(os.path.join(CONSTANTS.io_path, file_name + \".bz2\")).st_size)\n",
    "print(\"Number of columns: \", len(features[\"train_indep\"].columns)) \n",
    "print(\"features: {train: \", len(features[\"train_indep\"]), \", test: \", len(features[\"test_indep\"]), \"}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2.3. Load Features Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>epidur_0t30d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>epidur_365t730d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>preopdur_0t30d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gapDays_0t30d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epidur_365t730d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>preopdur_30t90d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>epidur_0t30d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>preopdur_0t30d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>epidur_30t90d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>preopdur_90t180d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>epidur_180t365d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>preopdur_30t90d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>preopdur_90t180d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gapDays_365t730d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>operOPCSL1_0t30d_prevalence_1_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>operOPCSL1_0t30d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>posopdur_30t90d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>epidur_180t365d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>posopdur_0t30d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>preopdur_180t365d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>operOPCSL1_0t30d_prevalence_3_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>operOPCSL1_0t30d_prevalence_4_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>posopdur_365t730d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>operOPCSL1_0t30d_prevalence_2_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>epidur_30t90d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>posopdur_30t90d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>operOPCSL1_0t30d_prevalence_5_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>preopdur_180t365d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>preopdur_365t730d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>preopdur_365t730d_avg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>operOPCSL1_180t365d_prevalence_30_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>diagCCS_0t30d_prevalence_25_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>diagCCS_30t90d_prevalence_1_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>diagCCS_0t30d_prevalence_23_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>diagCCS_90t180d_prevalence_5_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>diagCCS_90t180d_prevalence_4_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>diagCCS_0t30d_prevalence_19_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>operOPCSL1_365t730d_prevalence_4_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>diagCCS_90t180d_prevalence_2_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>diagCCS_90t180d_prevalence_3_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>diagCCS_0t30d_prevalence_21_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>diagCCS_30t90d_prevalence_3_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>diagCCS_30t90d_prevalence_4_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>diagCCS_30t90d_prevalence_2_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>operOPCSL1_365t730d_prevalence_5_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>diagCCS_30t90d_prevalence_5_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>diagCCS_0t30d_prevalence_20_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>diagCCS_30t90d_prevalence_6_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>diagCCS_30t90d_prevalence_7_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>diagCCS_30t90d_prevalence_8_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>operOPCSL1_365t730d_prevalence_6_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>diagCCS_90t180d_prevalence_1_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>diagCCS_90t180d_others_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>operOPCSL1_365t730d_prevalence_7_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>diagCCS_0t30d_prevalence_17_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>diagCCS_0t30d_prevalence_18_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>diagCCS_0t30d_prevalence_16_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>operOPCSL1_365t730d_prevalence_8_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>diagCCS_30t90d_prevalence_30_cnt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>diagCCS_30t90d_prevalence_29_cnt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0\n",
       "0                         epidur_0t30d_avg\n",
       "1                      epidur_365t730d_avg\n",
       "2                       preopdur_0t30d_avg\n",
       "3                 gapDays_0t30d_others_cnt\n",
       "4               epidur_365t730d_others_cnt\n",
       "5               preopdur_30t90d_others_cnt\n",
       "6                  epidur_0t30d_others_cnt\n",
       "7                preopdur_0t30d_others_cnt\n",
       "8                 epidur_30t90d_others_cnt\n",
       "9              preopdur_90t180d_others_cnt\n",
       "10                     epidur_180t365d_avg\n",
       "11                     preopdur_30t90d_avg\n",
       "12                    preopdur_90t180d_avg\n",
       "13                    gapDays_365t730d_avg\n",
       "14       operOPCSL1_0t30d_prevalence_1_cnt\n",
       "15             operOPCSL1_0t30d_others_cnt\n",
       "16              posopdur_30t90d_others_cnt\n",
       "17              epidur_180t365d_others_cnt\n",
       "18                      posopdur_0t30d_avg\n",
       "19            preopdur_180t365d_others_cnt\n",
       "20       operOPCSL1_0t30d_prevalence_3_cnt\n",
       "21       operOPCSL1_0t30d_prevalence_4_cnt\n",
       "22                   posopdur_365t730d_avg\n",
       "23       operOPCSL1_0t30d_prevalence_2_cnt\n",
       "24                       epidur_30t90d_avg\n",
       "25                     posopdur_30t90d_avg\n",
       "26       operOPCSL1_0t30d_prevalence_5_cnt\n",
       "27                   preopdur_180t365d_avg\n",
       "28            preopdur_365t730d_others_cnt\n",
       "29                   preopdur_365t730d_avg\n",
       "..                                     ...\n",
       "370  operOPCSL1_180t365d_prevalence_30_cnt\n",
       "371        diagCCS_0t30d_prevalence_25_cnt\n",
       "372        diagCCS_30t90d_prevalence_1_cnt\n",
       "373        diagCCS_0t30d_prevalence_23_cnt\n",
       "374       diagCCS_90t180d_prevalence_5_cnt\n",
       "375       diagCCS_90t180d_prevalence_4_cnt\n",
       "376        diagCCS_0t30d_prevalence_19_cnt\n",
       "377   operOPCSL1_365t730d_prevalence_4_cnt\n",
       "378       diagCCS_90t180d_prevalence_2_cnt\n",
       "379       diagCCS_90t180d_prevalence_3_cnt\n",
       "380        diagCCS_0t30d_prevalence_21_cnt\n",
       "381        diagCCS_30t90d_prevalence_3_cnt\n",
       "382        diagCCS_30t90d_prevalence_4_cnt\n",
       "383        diagCCS_30t90d_prevalence_2_cnt\n",
       "384   operOPCSL1_365t730d_prevalence_5_cnt\n",
       "385        diagCCS_30t90d_prevalence_5_cnt\n",
       "386        diagCCS_0t30d_prevalence_20_cnt\n",
       "387        diagCCS_30t90d_prevalence_6_cnt\n",
       "388        diagCCS_30t90d_prevalence_7_cnt\n",
       "389        diagCCS_30t90d_prevalence_8_cnt\n",
       "390   operOPCSL1_365t730d_prevalence_6_cnt\n",
       "391       diagCCS_90t180d_prevalence_1_cnt\n",
       "392             diagCCS_90t180d_others_cnt\n",
       "393   operOPCSL1_365t730d_prevalence_7_cnt\n",
       "394        diagCCS_0t30d_prevalence_17_cnt\n",
       "395        diagCCS_0t30d_prevalence_18_cnt\n",
       "396        diagCCS_0t30d_prevalence_16_cnt\n",
       "397   operOPCSL1_365t730d_prevalence_8_cnt\n",
       "398       diagCCS_30t90d_prevalence_30_cnt\n",
       "399       diagCCS_30t90d_prevalence_29_cnt\n",
       "\n",
       "[400 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_name = \"Step_07_Top_Features_rfc_adhoc\" \n",
    "\n",
    "features_names_selected = readers_writers.load_csv(path=CONSTANTS.io_path, title=file_name, dataframing=False)[0]\n",
    "features_names_selected = [f.replace(\"\\n\", \"\") for f in features_names_selected]\n",
    "display(pd.DataFrame(features_names_selected))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2.4. Load the fitted model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### <font style=\"font-weight:bold;color:blue\">2.4.1. Basic Models</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Initialise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-10-29 13:04:19,778 - T-CARER - INFO - Running Random Forest Classifier\n"
     ]
    }
   ],
   "source": [
    "training_method = TrainingMethod(method_name)\n",
    "\n",
    "# file name\n",
    "file_name = \"Step_09_Model_\" + method_name + \"_\" + target_feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-10-29 13:04:19,866 - T-CARER - INFO - Running Random Forest Classifier\n"
     ]
    }
   ],
   "source": [
    "training_method.load(path=CONSTANTS.io_path, title=file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### <font style=\"font-weight:bold;color:blue\">2.4.2. TensorFlow Models</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class TrainingMethodTensorflow:    \n",
    "    def __init__(self, summaries, features_names, num_features, cut_off, train_size, test_size):\n",
    "        self.model_predict = {\"train\": {'score': [], 'model_labels': []}, \n",
    "                              \"test\": {'score': [], 'model_labels': []}}\n",
    "        self.__stats = Stats()\n",
    "        # summaries[\"fit\"][\"get_variable_names\"]\n",
    "        # summaries[\"fit\"][\"get_variable_value\"]\n",
    "        # summaries[\"fit\"][\"get_params\"]\n",
    "        # summaries[\"fit\"][\"export\"]\n",
    "        # summaries[\"fit\"][\"get_variable_names()\"]\n",
    "        # summaries[\"fit\"][\"params\"]\n",
    "        # summaries[\"fit\"][\"dnn_bias_\"]\n",
    "        # summaries[\"fit\"][\"dnn_weights_\"] \n",
    "        # summaries[\"train\"][\"results\"]\n",
    "        # summaries[\"test\"][\"results\"]\n",
    "               \n",
    "        self.model_predict[\"train\"]['pred'] = np.asarray([1 if i[1] >= 0.5 else 0 for i in summaries[\"train\"][\"predict_proba\"]][0:train_size])\n",
    "        self.model_predict[\"test\"]['pred'] = np.asarray([1 if i[1] >= 0.5 else 0 for i in summaries[\"test\"][\"predict_proba\"]][0:test_size])\n",
    "        \n",
    "        self.model_predict[\"train\"]['score'] =  np.asarray([i[1] for i in summaries[\"train\"][\"predict_proba\"]][0:train_size])\n",
    "        self.model_predict[\"test\"]['score'] =  np.asarray([i[1] for i in summaries[\"test\"][\"predict_proba\"]][0:test_size])\n",
    "        \n",
    "        self.model_predict[\"train\"]['score_0'] =  np.asarray([i[0] for i in summaries[\"train\"][\"predict_proba\"]][0:train_size])\n",
    "        self.model_predict[\"test\"]['score_0'] =  np.asarray([i[0] for i in summaries[\"test\"][\"predict_proba\"]][0:test_size])\n",
    "    \n",
    "    def train_summaries(self):\n",
    "        return {\"feature_importances_\": self.__weights}\n",
    "    \n",
    "    def predict_summaries(self, feature_target, sample_name):\n",
    "        return self.__stats.predict_summaries(self.model_predict[sample_name], feature_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-10-29 13:04:20,044 - T-CARER - ERROR - ReadersWriters._PickleSerialised - Can not open the file: \n",
      "C:\\Users\\eagle\\Documents\\GitHub\\tmp\\TCARER\\Basic_prototype\\model_tensorflow_summaries_label365.bz2\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'C:\\\\Users\\\\eagle\\\\Documents\\\\GitHub\\\\tmp\\\\TCARER\\\\Basic_prototype\\\\model_tensorflow_summaries_label365.bz2'\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2889: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "file_name = \"model_tensorflow_summaries_\" + target_feature\n",
    "summaries = readers_writers.load_serialised_compressed(path=CONSTANTS.io_path, title=file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "num_features = 300\n",
    "cut_off = 0.5\n",
    "\n",
    "training_method = TrainingMethodTensorflow(summaries, features_names_selected, num_features, cut_off,\n",
    "                                           len(features[\"train_indep\"].index), len(features[\"test_indep\"].index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score 0.6592\n",
      "average_precision_score 0.47970482114\n",
      "brier_score_loss 0.218148003816\n",
      "classification_report              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.63      0.74      1885\n",
      "          1       0.40      0.74      0.52       615\n",
      "\n",
      "avg / total       0.76      0.66      0.68      2500\n",
      "\n",
      "confusion_matrix [[1194  691]\n",
      " [ 161  454]]\n",
      "f1_score 0.515909090909\n",
      "fbeta_score 0.436958614052\n",
      "hamming_loss 0.3408\n",
      "jaccard_similarity_score 0.6592\n",
      "log_loss 11.7710360041\n",
      "matthews_corrcoef 0.32124418171\n",
      "precision_recall_fscore_support (array([ 0.88118081,  0.39650655]), array([ 0.63342175,  0.73821138]), array([ 0.73703704,  0.51590909]), array([1885,  615], dtype=int64))\n",
      "precision_score 0.396506550218\n",
      "recall_score 0.738211382114\n",
      "roc_auc_score 0.742965646633\n",
      "zero_one_loss 0.3408\n",
      "\n",
      "\n",
      "accuracy_score 0.654661864746\n",
      "average_precision_score 0.423030738094\n",
      "brier_score_loss 0.22022287805\n",
      "classification_report              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.63      0.74      1959\n",
      "          1       0.36      0.75      0.48       540\n",
      "\n",
      "avg / total       0.78      0.65      0.69      2499\n",
      "\n",
      "confusion_matrix [[1230  729]\n",
      " [ 134  406]]\n",
      "f1_score 0.484776119403\n",
      "fbeta_score 0.399606299213\n",
      "hamming_loss 0.345338135254\n",
      "jaccard_similarity_score 0.654661864746\n",
      "log_loss 11.9277898901\n",
      "matthews_corrcoef 0.313889024973\n",
      "precision_recall_fscore_support (array([ 0.90175953,  0.35770925]), array([ 0.62787136,  0.75185185]), array([ 0.74029491,  0.48477612]), array([1959,  540], dtype=int64))\n",
      "precision_score 0.357709251101\n",
      "recall_score 0.751851851852\n",
      "roc_auc_score 0.73645898323\n",
      "zero_one_loss 0.345338135254\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "o_summaries = training_method.predict_summaries(features[\"train_target\"][target_feature], \"train\")\n",
    "for k in o_summaries.keys():\n",
    "    print(k,  o_summaries[k])\n",
    "    \n",
    "print(\"\\n\")\n",
    "# test\n",
    "o_summaries = training_method.predict_summaries(features[\"test_target\"][target_feature], \"test\")\n",
    "for k in o_summaries.keys():\n",
    "    print(k,  o_summaries[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2.5. Load the Extra Features for Benchmarking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Read the extra features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "table = 'tcarer_featuresExtra'\n",
    "features_extra_dtypes = {'patientID': 'U32', 'trigger_charlsonFoster': 'i4', 'trigger_los': 'i4', 'trigger_age': 'i4', 'prior_admiOther': 'i4', 'prior_admiAcute': 'i4', \n",
    "                         'prior_spells': 'i4', 'prior_asthma': 'i4', 'prior_copd': 'i4', 'prior_depression': 'i4', 'prior_diabetes': 'i4', 'prior_hypertension': 'i4', 'prior_cancer': 'i4', 'prior_chd': 'i4', 'prior_chf': 'i4', \n",
    "                         'diagCci_01_myocardial_freq': 'i4', 'diagCci_02_chf_freq': 'i4', 'diagCci_03_pvd_freq': 'i4', 'diagCci_04_cerebrovascular_freq': 'i4', 'diagCci_05_dementia_freq': 'i4', 'diagCci_06_cpd_freq': 'i4', 'diagCci_07_rheumatic_freq': 'i4', 'diagCci_08_ulcer_freq': 'i4', 'diagCci_09_liverMild_freq': 'i4', 'diagCci_10_diabetesNotChronic_freq': 'i4', 'diagCci_11_diabetesChronic_freq': 'i4', 'diagCci_12_hemiplegia_freq': 'i4', 'diagCci_13_renal_freq': 'i4', 'diagCci_14_malignancy_freq': 'i4', 'diagCci_15_liverSevere_freq': 'i4', 'diagCci_16_tumorSec_freq': 'i4', 'diagCci_17_aids_freq': 'i4', 'diagCci_18_depression_freq': 'i4', 'diagCci_19_cardiac_freq': 'i4', 'diagCci_20_valvular_freq': 'i4', 'diagCci_21_pulmonary_freq': 'i4', 'diagCci_22_vascular_freq': 'i4', 'diagCci_23_hypertensionNotComplicated_freq': 'i4', 'diagCci_24_hypertensionComplicated_freq': 'i4', 'diagCci_25_paralysis_freq': 'i4', 'diagCci_26_neuroOther_freq': 'i4', 'diagCci_27_pulmonaryChronic_freq': 'i4', 'diagCci_28_diabetesNotComplicated_freq': 'i4', 'diagCci_29_diabetesComplicated_freq': 'i4', 'diagCci_30_hypothyroidism_freq': 'i4', 'diagCci_31_renal_freq': 'i4', 'diagCci_32_liver_freq': 'i4', 'diagCci_33_ulcerNotBleeding_freq': 'i4', 'diagCci_34_psychoses_freq': 'i4', 'diagCci_35_lymphoma_freq': 'i4', 'diagCci_36_cancerSec_freq': 'i4', 'diagCci_37_tumorNotSec_freq': 'i4', 'diagCci_38_rheumatoid_freq': 'i4', 'diagCci_39_coagulopathy_freq': 'i4', 'diagCci_40_obesity_freq': 'i4', 'diagCci_41_weightLoss_freq': 'i4', 'diagCci_42_fluidDisorder_freq': 'i4', 'diagCci_43_bloodLoss_freq': 'i4', 'diagCci_44_anemia_freq': 'i4', 'diagCci_45_alcohol_freq': 'i4', 'diagCci_46_drug_freq': 'i4'}\n",
    "features_extra_name = features_extra_dtypes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "ProgrammingError",
     "evalue": "(_mysql_exceptions.ProgrammingError) (1146, \"Table 'parr_sample_prototype.tcarer_featuresextra' doesn't exist\") [SQL: 'SELECT * FROM tcarer_featuresExtra']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mProgrammingError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1181\u001b[0m                         \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1182\u001b[0;31m                         context)\n\u001b[0m\u001b[1;32m   1183\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    469\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 470\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    471\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorhandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\connections.py\u001b[0m in \u001b[0;36mdefaulterrorhandler\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merrorvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[1;32mraise\u001b[0m \u001b[0merrorvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merrorclass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36m_query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m    410\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0mrowcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_post_get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36m_do_query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_last_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m         \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    375\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\connections.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query)\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m             \u001b[0m_mysql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mProgrammingError\u001b[0m: (1146, \"Table 'parr_sample_prototype.tcarer_featuresextra' doesn't exist\")",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mProgrammingError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-086a730ff34b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# Read features from the MySQL\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfeatures_extra\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfeatures_extra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreaders_writers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_mysql_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mschema\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataframing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mfeatures_extra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeatures_extra_dtypes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfeatures_extra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeatures_extra\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\eagle\\Documents\\GitHub\\Analytics_UoW\\TCARER\\ReadersWriters\\ReadersWriters.py\u001b[0m in \u001b[0;36mload_mysql_table\u001b[0;34m(db_schema, db_table, dataframing)\u001b[0m\n\u001b[1;32m    323\u001b[0m         \"\"\"\n\u001b[1;32m    324\u001b[0m         \u001b[0mquery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"SELECT * FROM \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdb_table\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mReadersWriters\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_mysql_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdb_schema\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataframing\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\eagle\\Documents\\GitHub\\Analytics_UoW\\TCARER\\ReadersWriters\\ReadersWriters.py\u001b[0m in \u001b[0;36mload_mysql_query\u001b[0;34m(query, db_schema, dataframing, batch, float_round_vars, float_round)\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0mengine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0mdbc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMysqlCommand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdb_session_vars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdbc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataframing\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat_round_vars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat_round\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m         \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\eagle\\Documents\\GitHub\\Analytics_UoW\\TCARER\\ReadersWriters\\_MysqlCommand.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, query, dataframing, batch, float_round_vars, float_round)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__logger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Reading from MySQL database.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdataframing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__read_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat_round_vars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat_round\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__read_arr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\eagle\\Documents\\GitHub\\Analytics_UoW\\TCARER\\ReadersWriters\\_MysqlCommand.py\u001b[0m in \u001b[0;36m__read_df\u001b[0;34m(self, query, batch, float_round_vars, float_round)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mbatch\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msql\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m                     \u001b[1;32mfor\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msql\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m    413\u001b[0m             \u001b[0msql\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m             \u001b[0mcoerce_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparse_dates\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             chunksize=chunksize)\n\u001b[0m\u001b[1;32m    416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1082\u001b[0m         \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msql\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1085\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1086\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    973\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[1;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    976\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m     def read_table(self, table_name, index_col=None, coerce_float=True,\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, object, *multiparams, **params)\u001b[0m\n\u001b[1;32m    937\u001b[0m         \"\"\"\n\u001b[1;32m    938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 939\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_execute_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    940\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_execute_on_connection\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_text\u001b[0;34m(self, statement, multiparams, params)\u001b[0m\n\u001b[1;32m   1095\u001b[0m             \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1096\u001b[0m             \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1097\u001b[0;31m             \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1098\u001b[0m         )\n\u001b[1;32m   1099\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m                 \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m                 context)\n\u001b[0m\u001b[1;32m   1190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1391\u001b[0m                 util.raise_from_cause(\n\u001b[1;32m   1392\u001b[0m                     \u001b[0msqlalchemy_exception\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1393\u001b[0;31m                     \u001b[0mexc_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1394\u001b[0m                 )\n\u001b[1;32m   1395\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mraise_from_cause\u001b[0;34m(exception, exc_info)\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0mexc_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0mcause\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mexception\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m     \u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexc_tb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcause\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpy3k\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb, cause)\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__cause__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcause\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1180\u001b[0m                         \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1181\u001b[0m                         \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1182\u001b[0;31m                         context)\n\u001b[0m\u001b[1;32m   1183\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m             self._handle_dbapi_exception(\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 470\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    471\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorhandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_defer_warnings\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\connections.py\u001b[0m in \u001b[0;36mdefaulterrorhandler\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[1;32mdel\u001b[0m \u001b[0mconnection\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merrorvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[1;32mraise\u001b[0m \u001b[0merrorvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merrorclass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0merrorclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merrorvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m             \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36m_query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m    409\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0mrowcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_post_get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mrowcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\cursors.py\u001b[0m in \u001b[0;36m_do_query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0mdb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_db\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_last_executed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m         \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    375\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrowcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\users\\eagle\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\MySQLdb\\connections.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query)\u001b[0m\n\u001b[1;32m    268\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_query_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m             \u001b[0m_mysql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mProgrammingError\u001b[0m: (_mysql_exceptions.ProgrammingError) (1146, \"Table 'parr_sample_prototype.tcarer_featuresextra' doesn't exist\") [SQL: 'SELECT * FROM tcarer_featuresExtra']"
     ]
    }
   ],
   "source": [
    "# Read features from the MySQL\n",
    "features_extra = dict()\n",
    "features_extra['train'] = readers_writers.load_mysql_table(schema, table, dataframing=True)\n",
    "features_extra['train'].astype(dtype=features_extra_dtypes)\n",
    "features_extra['test'] = features_extra['train']\n",
    "\n",
    "print(\"Number of columns: \", len(features_extra['train'].columns), \"; Total records: \", len(features_extra['train'].index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Replace NaN appears in the Charlson-Index feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "features_extra['train'].loc[:, \"trigger_charlsonFoster\"] = np.nan_to_num(features_extra['train'][\"trigger_charlsonFoster\"])\n",
    "features_extra['test'].loc[:, \"trigger_charlsonFoster\"] = np.nan_to_num(features_extra['test'][\"trigger_charlsonFoster\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Combine (join by PatientID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "features_extra['train'] = features_extra['train'].merge(\n",
    "    pd.concat([features['train_id'], features['train_target'], \n",
    "               pd.DataFrame({'score': training_method.model_predict[\"train\"]['score']}), features['train_indep']], axis=1), \n",
    "    how=\"inner\", on=\"patientID\")\n",
    "features_extra['test'] = features_extra['test'].merge(\n",
    "    pd.concat([features['test_id'], features['test_target'], \n",
    "               pd.DataFrame({'score': training_method.model_predict[\"test\"]['score']}), features['test_indep']], axis=1), \n",
    "    how=\"inner\", on=\"patientID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<font style=\"font-weight:bold;color:red\">Clean-up</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "features = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3. Charlson Index Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.1. Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<font style=\"font-weight:bold;color:brown\">Algorithm 1</font>: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "charlson_method_name = \"rfc\"\n",
    "kwargs = {\"n_estimators\": 20, \"criterion\": 'gini', \"max_depth\": None, \"min_samples_split\": 100,\n",
    "    \"min_samples_leaf\": 50, \"min_weight_fraction_leaf\": 0.0, \"max_features\": 'auto',\n",
    "    \"max_leaf_nodes\": None, \"bootstrap\": True, \"oob_score\": False, \"n_jobs\": -1, \"random_state\": None,\n",
    "    \"verbose\": 0, \"warm_start\": False, \"class_weight\": \"balanced_subsample\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<font style=\"font-weight:bold;color:brown\">Algorithm 2</font>: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "charlson_method_name = \"lr\"\n",
    "kwargs = {\"penalty\": 'l2', \"dual\": False, \"tol\": 0.0001, \"C\": 1, \"fit_intercept\": True, \"intercept_scaling\": 1,\n",
    "          \"class_weight\": None, \"random_state\": None, \"solver\": 'liblinear', \"max_iter\": 100, \"multi_class\": 'ovr',\n",
    "          \"verbose\": 0, \"warm_start\": False, \"n_jobs\": -1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.2. Initialise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# set features\n",
    "charlson_features_names = ['trigger_charlsonFoster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# select the target variable\n",
    "charlson_target_feature = \"label30\" # \"label30\",  \"label365\" \n",
    "\n",
    "# file name\n",
    "file_name = \"report_Model_Charlson_\" + charlson_method_name + \"_\" + charlson_target_feature\n",
    "\n",
    "# initialise\n",
    "charlson_training_method = TrainingMethod(charlson_method_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.3. Fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = dict()\n",
    "# Fit\n",
    "model = charlson_training_method.train(features_extra[\"train\"][charlson_features_names], features_extra[\"train\"][target_feature], **kwargs)\n",
    "charlson_training_method.save_model(path=CONSTANTS.io_path, title=file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# load model\n",
    "# charlson_training_method.load(path=CONSTANTS.io_path, title=file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# short summary\n",
    "o_summaries = charlson_training_method.train_summaries()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Fit Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = dict()\n",
    "model = charlson_training_method.predict(features_extra[\"train\"][charlson_features_names], \"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# short summary\n",
    "o_summaries = charlson_training_method.predict_summaries(pd.Series(features_extra[\"train\"][target_feature]), \"train\")\n",
    "print(\"ROC AUC:\", o_summaries['roc_auc_score_1'], \"\\n\", o_summaries['classification_report'])\n",
    "for k in o_summaries.keys():\n",
    "    print(k,  o_summaries[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.4. Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = dict()\n",
    "model = charlson_training_method.predict(features_extra[\"test\"][charlson_features_names], \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# short summary\n",
    "o_summaries = charlson_training_method.predict_summaries(pd.Series(features_extra[\"test\"][target_feature]), \"test\")\n",
    "print(\"ROC AUC:\", o_summaries['roc_auc_score_1'], \"\\n\", o_summaries['classification_report'])\n",
    "for k in o_summaries.keys():\n",
    "    print(k,  o_summaries[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.5. Cross-Validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = dict()\n",
    "score = charlson_training_method.cross_validate(features_extra[\"test\"][charlson_features_names], features_extra[\"test\"][target_feature], \n",
    "                                             scoring=\"neg_mean_squared_error\", cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# short summary\n",
    "o_summaries = charlson_training_method.cross_validate_summaries()\n",
    "print(\"Scores: \", o_summaries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.6. Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "charlson_training_method.save_model(path=CONSTANTS.io_path, title=file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4. Features Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4.1. Features Rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "<i>It is produced during modelling</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4.2. Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "<i>It is produced during modelling</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4.3. Features Weigths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def features_importance_rank(fitting_method, ranking_file_name=None, rank_models=[\"rfc\", \"gbrt\", \"randLogit\"]):\n",
    "    # Fitting weight\n",
    "    o_summaries = pd.DataFrame({\"Name\": fitting_method.model_labels,\n",
    "                                \"Fitting Weight\": fitting_method.train_summaries()[\"feature_importances_\"]},\n",
    "                              index = fitting_method.model_labels)\n",
    "    o_summaries = o_summaries.sort_values(\"Fitting Weight\", ascending=False)\n",
    "    o_summaries = o_summaries.reset_index(drop=True)\n",
    "    \n",
    "    # Ranking scores\n",
    "    if ranking_file_name is not None:\n",
    "        for rank_model in rank_models:\n",
    "            o_summaries_ranks = readers_writers.load_serialised_compressed(\n",
    "                path=CONSTANTS.io_path, title=ranking_file_name + rank_model)\n",
    "            for trial in range(len(o_summaries_ranks)):\n",
    "                o_summaries_rank = pd.DataFrame(o_summaries_ranks[trial])\n",
    "                o_summaries_rank.columns = [\"Name\", \"Importance - \" + rank_model + \" - Trial_\" + str(trial),\n",
    "                                            \"Order - \" + rank_model + \" - Trial_\" + str(trial)]\n",
    "                o_summaries = o_summaries.merge(o_summaries_rank, how=\"outer\", on=\"Name\")\n",
    "      \n",
    "    return o_summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "file_name = \"Step_07_Model_Train_model_rank_summaries_\"\n",
    "\n",
    "o_summaries = features_importance_rank(fitting_method=training_method, ranking_file_name=file_name, rank_models=rank_models)\n",
    "\n",
    "file_name = \"report_weights_ranks\"\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name, data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "display(o_summaries.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 5. Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 5.1. Performance Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "measures = [\"accuracy_score\", \"precision_score\", \"recall_score\",\n",
    "            \"roc_auc_score_1\", \"f1_score\", \"fbeta_score\", \"average_precision_score\",  \n",
    "            \"log_loss\", \"zero_one_loss\", \"hamming_loss\", \"jaccard_similarity_score\", \"matthews_corrcoef\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# train\n",
    "o_summaries = training_method.predict_summaries(features_extra[\"train\"][target_feature], \"train\")\n",
    "o_summaries = np.array([(m, o_summaries[m]) for m in measures])\n",
    "report_performance = pd.DataFrame({\"Measure\": o_summaries[:, 0], \n",
    "                                   \"Sample Train\": o_summaries[:, 1], \n",
    "                                   \"Sample Test\": [None] * len(measures)})\n",
    "\n",
    "# test\n",
    "o_summaries = training_method.predict_summaries(features_extra[\"test\"][target_feature], \"test\")\n",
    "o_summaries = np.array([(m, o_summaries[m]) for m in measures])\n",
    "report_performance[\"Sample Test\"] = o_summaries[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# print\n",
    "file_name = \"report_performance_\" + method_name + \"_\" + target_feature\n",
    "display(report_performance)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name, data=report_performance, append=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 5.2. Population Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def population_statistics(df, diagnoses, cutpoints=[0.50, 0.60, 0.70, 0.80, 0.90]):\n",
    "    o_summaries = pd.DataFrame(columns=['Name'], index=diagnoses)\n",
    "    o_summaries['Name'] = diagnoses\n",
    "    \n",
    "    for diagnose in diagnoses:\n",
    "        o_summaries.loc[diagnose, 'Total'] = len(df.index)\n",
    "        if diagnose not in df:\n",
    "            continue\n",
    "            \n",
    "        o_summaries.loc[diagnose, 'Total - diagnose'] = len(df.loc[(df[diagnose] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Total - diagnose - label_1'] = len(df.loc[(df[diagnose] > 0) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Emergency Readmission Rate - cnt 1'] = len(df.loc[(df[diagnose] > 0) & (df['admimeth_0t30d_prevalence_1_cnt'] > 0) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Emergency Readmission Rate - cnt 2'] = len(df.loc[(df[diagnose] > 0) & (df['admimeth_0t30d_prevalence_2_cnt'] > 0) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Emergency Readmission Rate - cnt 3'] = len(df.loc[(df[diagnose] > 0) & (df['admimeth_0t30d_prevalence_3_cnt'] > 0) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Prior Spells'] = len(df.loc[(df[diagnose] > 0) & (df['prior_spells'] > 0) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Male - perc'] = len(df.loc[(df[diagnose] > 0) & (df['gender_1'] > 0) & (df[target_feature] > 0)].index)\n",
    "        age = df.loc[(df[diagnose] > 0) & (df[target_feature] > 0)]['trigger_age'].describe(percentiles=[.25, .5, .75])\n",
    "        o_summaries.loc[diagnose, 'Age - IQR_min'] = age['min']\n",
    "        o_summaries.loc[diagnose, 'Age - IQR_25'] = age['25%']\n",
    "        o_summaries.loc[diagnose, 'Age - IQR_50'] = age['50%']\n",
    "        o_summaries.loc[diagnose, 'Age - IQR_75'] = age['75%']\n",
    "        o_summaries.loc[diagnose, 'Age - IQR_max'] = age['max']\n",
    "        los = df.loc[(df[diagnose] > 0) & (df[target_feature] > 0)]['trigger_los'].describe(percentiles=[.25, .5, .75])\n",
    "        o_summaries.loc[diagnose, 'LoS - IQR_min'] = los['min']\n",
    "        o_summaries.loc[diagnose, 'LoS - IQR_25'] = los['25%']\n",
    "        o_summaries.loc[diagnose, 'LoS - IQR_50'] = los['50%']\n",
    "        o_summaries.loc[diagnose, 'LoS - IQR_75'] = los['75%']\n",
    "        o_summaries.loc[diagnose, 'LoS - IQR_max'] = los['max']\n",
    "        for cutpoint in cutpoints:\n",
    "            o_summaries.loc[diagnose, 'score - ' + str(cutpoint)] = len(df.loc[(df[diagnose] > 0) & (df['score'] > cutpoint)].index)\n",
    "            o_summaries.loc[diagnose, 'TP - ' + str(cutpoint)] = len(df.loc[(df[diagnose] > 0) & (df[target_feature] > 0) & (df['score'] > cutpoint)].index)\n",
    "            o_summaries.loc[diagnose, 'FP - ' + str(cutpoint)] = len(df.loc[(df[diagnose] > 0) & (df[target_feature] == 0) & (df['score'] > cutpoint)].index)\n",
    "            o_summaries.loc[diagnose, 'FN - ' + str(cutpoint)] = len(df.loc[(df[diagnose] > 0) & (df[target_feature] > 0) & (df['score'] <= cutpoint)].index)\n",
    "            o_summaries.loc[diagnose, 'TN - ' + str(cutpoint)] = len(df.loc[(df[diagnose] > 0) & (df[target_feature] == 0) & (df['score'] <= cutpoint)].index)\n",
    "            \n",
    "        \n",
    "        o_summaries.loc[diagnose, 'Charlson - 0'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 0 - label_1'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 0) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 1'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 1)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 1 - label_1'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 1) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 2'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 2)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 2 - label_1'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 2) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 3'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 3)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 3 - label_1'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 3) & (df[target_feature] > 0)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 4+'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] >= 4)].index)\n",
    "        o_summaries.loc[diagnose, 'Charlson - 4+ - label_1'] = len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] >= 4) & (df[target_feature] > 0)].index)\n",
    "        \n",
    "        for cutpoint in cutpoints:\n",
    "            o_summaries.loc[diagnose, 'Charlson - 0 - label_1 - TP - ' + str(cutpoint)] = \\\n",
    "                len(df.loc[(df[diagnose] > 0) & (df[\"trigger_charlsonFoster\"] == 0) & (df[target_feature] > 0) & (df['score'] > cutpoint)].index)\n",
    "        \n",
    "    return o_summaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.2.1. Most Prevalent Diagnoses Groups\n",
    "Most prevalent diagnoses groups (30-day, 1-year readmission): \n",
    "* Total, Admissions, Emergency Readmission Rate, Prior Spells, Male (%), Age (IQR), LoS (IQR), TP, FP, FN, TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diagnoses = ['diagCCS_0t30d_others_cnt', 'diagCCS_0t30d_prevalence_1_cnt', 'diagCCS_0t30d_prevalence_2_cnt', 'diagCCS_0t30d_prevalence_3_cnt', 'diagCCS_0t30d_prevalence_4_cnt', 'diagCCS_0t30d_prevalence_5_cnt', 'diagCCS_0t30d_prevalence_6_cnt', 'diagCCS_0t30d_prevalence_7_cnt', 'diagCCS_0t30d_prevalence_8_cnt', 'diagCCS_0t30d_prevalence_9_cnt', 'diagCCS_0t30d_prevalence_10_cnt', 'diagCCS_0t30d_prevalence_11_cnt', 'diagCCS_0t30d_prevalence_12_cnt', 'diagCCS_0t30d_prevalence_13_cnt', 'diagCCS_0t30d_prevalence_14_cnt', 'diagCCS_0t30d_prevalence_15_cnt', 'diagCCS_0t30d_prevalence_16_cnt', 'diagCCS_0t30d_prevalence_17_cnt', 'diagCCS_0t30d_prevalence_18_cnt', 'diagCCS_0t30d_prevalence_19_cnt', 'diagCCS_0t30d_prevalence_20_cnt', 'diagCCS_0t30d_prevalence_21_cnt', 'diagCCS_0t30d_prevalence_22_cnt', 'diagCCS_0t30d_prevalence_23_cnt', 'diagCCS_0t30d_prevalence_24_cnt', 'diagCCS_0t30d_prevalence_25_cnt', 'diagCCS_0t30d_prevalence_26_cnt', 'diagCCS_0t30d_prevalence_27_cnt', 'diagCCS_0t30d_prevalence_28_cnt', 'diagCCS_0t30d_prevalence_29_cnt', 'diagCCS_0t30d_prevalence_30_cnt'\n",
    "            , 'diagCCS_30t90d_others_cnt', 'diagCCS_30t90d_prevalence_1_cnt', 'diagCCS_30t90d_prevalence_2_cnt', 'diagCCS_30t90d_prevalence_3_cnt', 'diagCCS_30t90d_prevalence_4_cnt', 'diagCCS_30t90d_prevalence_5_cnt', 'diagCCS_30t90d_prevalence_6_cnt', 'diagCCS_30t90d_prevalence_7_cnt', 'diagCCS_30t90d_prevalence_8_cnt', 'diagCCS_30t90d_prevalence_9_cnt', 'diagCCS_30t90d_prevalence_10_cnt', 'diagCCS_30t90d_prevalence_11_cnt', 'diagCCS_30t90d_prevalence_12_cnt', 'diagCCS_30t90d_prevalence_13_cnt', 'diagCCS_30t90d_prevalence_14_cnt', 'diagCCS_30t90d_prevalence_15_cnt', 'diagCCS_30t90d_prevalence_16_cnt', 'diagCCS_30t90d_prevalence_17_cnt', 'diagCCS_30t90d_prevalence_18_cnt', 'diagCCS_30t90d_prevalence_19_cnt', 'diagCCS_30t90d_prevalence_20_cnt', 'diagCCS_30t90d_prevalence_21_cnt', 'diagCCS_30t90d_prevalence_22_cnt', 'diagCCS_30t90d_prevalence_23_cnt', 'diagCCS_30t90d_prevalence_24_cnt', 'diagCCS_30t90d_prevalence_25_cnt', 'diagCCS_30t90d_prevalence_26_cnt', 'diagCCS_30t90d_prevalence_27_cnt', 'diagCCS_30t90d_prevalence_28_cnt', 'diagCCS_30t90d_prevalence_29_cnt', 'diagCCS_30t90d_prevalence_30_cnt'\n",
    "            , 'diagCCS_90t180d_others_cnt', 'diagCCS_90t180d_prevalence_1_cnt', 'diagCCS_90t180d_prevalence_2_cnt', 'diagCCS_90t180d_prevalence_3_cnt', 'diagCCS_90t180d_prevalence_4_cnt', 'diagCCS_90t180d_prevalence_5_cnt', 'diagCCS_90t180d_prevalence_6_cnt', 'diagCCS_90t180d_prevalence_7_cnt', 'diagCCS_90t180d_prevalence_8_cnt', 'diagCCS_90t180d_prevalence_9_cnt', 'diagCCS_90t180d_prevalence_10_cnt', 'diagCCS_90t180d_prevalence_11_cnt', 'diagCCS_90t180d_prevalence_12_cnt', 'diagCCS_90t180d_prevalence_13_cnt', 'diagCCS_90t180d_prevalence_14_cnt', 'diagCCS_90t180d_prevalence_15_cnt', 'diagCCS_90t180d_prevalence_16_cnt', 'diagCCS_90t180d_prevalence_17_cnt', 'diagCCS_90t180d_prevalence_18_cnt', 'diagCCS_90t180d_prevalence_19_cnt', 'diagCCS_90t180d_prevalence_20_cnt', 'diagCCS_90t180d_prevalence_21_cnt', 'diagCCS_90t180d_prevalence_22_cnt', 'diagCCS_90t180d_prevalence_23_cnt', 'diagCCS_90t180d_prevalence_24_cnt', 'diagCCS_90t180d_prevalence_25_cnt', 'diagCCS_90t180d_prevalence_26_cnt', 'diagCCS_90t180d_prevalence_27_cnt', 'diagCCS_90t180d_prevalence_28_cnt', 'diagCCS_90t180d_prevalence_29_cnt', 'diagCCS_90t180d_prevalence_30_cnt'\n",
    "            , 'diagCCS_180t365d_others_cnt', 'diagCCS_180t365d_prevalence_1_cnt', 'diagCCS_180t365d_prevalence_2_cnt', 'diagCCS_180t365d_prevalence_3_cnt', 'diagCCS_180t365d_prevalence_4_cnt', 'diagCCS_180t365d_prevalence_5_cnt', 'diagCCS_180t365d_prevalence_6_cnt', 'diagCCS_180t365d_prevalence_7_cnt', 'diagCCS_180t365d_prevalence_8_cnt', 'diagCCS_180t365d_prevalence_9_cnt', 'diagCCS_180t365d_prevalence_10_cnt', 'diagCCS_180t365d_prevalence_11_cnt', 'diagCCS_180t365d_prevalence_12_cnt', 'diagCCS_180t365d_prevalence_13_cnt', 'diagCCS_180t365d_prevalence_14_cnt', 'diagCCS_180t365d_prevalence_15_cnt', 'diagCCS_180t365d_prevalence_16_cnt', 'diagCCS_180t365d_prevalence_17_cnt', 'diagCCS_180t365d_prevalence_18_cnt', 'diagCCS_180t365d_prevalence_19_cnt', 'diagCCS_180t365d_prevalence_20_cnt', 'diagCCS_180t365d_prevalence_21_cnt', 'diagCCS_180t365d_prevalence_22_cnt', 'diagCCS_180t365d_prevalence_23_cnt', 'diagCCS_180t365d_prevalence_24_cnt', 'diagCCS_180t365d_prevalence_25_cnt', 'diagCCS_180t365d_prevalence_26_cnt', 'diagCCS_180t365d_prevalence_27_cnt', 'diagCCS_180t365d_prevalence_28_cnt', 'diagCCS_180t365d_prevalence_29_cnt', 'diagCCS_180t365d_prevalence_30_cnt'\n",
    "            , 'diagCCS_365t730d_others_cnt', 'diagCCS_365t730d_prevalence_1_cnt', 'diagCCS_365t730d_prevalence_2_cnt', 'diagCCS_365t730d_prevalence_3_cnt', 'diagCCS_365t730d_prevalence_4_cnt', 'diagCCS_365t730d_prevalence_5_cnt', 'diagCCS_365t730d_prevalence_6_cnt', 'diagCCS_365t730d_prevalence_7_cnt', 'diagCCS_365t730d_prevalence_8_cnt', 'diagCCS_365t730d_prevalence_9_cnt', 'diagCCS_365t730d_prevalence_10_cnt', 'diagCCS_365t730d_prevalence_11_cnt', 'diagCCS_365t730d_prevalence_12_cnt', 'diagCCS_365t730d_prevalence_13_cnt', 'diagCCS_365t730d_prevalence_14_cnt', 'diagCCS_365t730d_prevalence_15_cnt', 'diagCCS_365t730d_prevalence_16_cnt', 'diagCCS_365t730d_prevalence_17_cnt', 'diagCCS_365t730d_prevalence_18_cnt', 'diagCCS_365t730d_prevalence_19_cnt', 'diagCCS_365t730d_prevalence_20_cnt', 'diagCCS_365t730d_prevalence_21_cnt', 'diagCCS_365t730d_prevalence_22_cnt', 'diagCCS_365t730d_prevalence_23_cnt', 'diagCCS_365t730d_prevalence_24_cnt', 'diagCCS_365t730d_prevalence_25_cnt', 'diagCCS_365t730d_prevalence_26_cnt', 'diagCCS_365t730d_prevalence_27_cnt', 'diagCCS_365t730d_prevalence_28_cnt', 'diagCCS_365t730d_prevalence_29_cnt', 'diagCCS_365t730d_prevalence_30_cnt']\n",
    "file_name = \"report_population_prevalent_diagnoses_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = population_statistics(features_extra['train'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"train\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "o_summaries = population_statistics(features_extra['test'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"test\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.2.2. Major Comorbidity Groups\n",
    "Comorbidity diagnoses groups (30-day, 1-year readmission): \n",
    "* Total, Admissions, Emergency Readmission Rate, Prior Spells, Male (%), Age (IQR), LoS (IQR), TP, FP, FN, TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diagnoses = ['prior_admiOther', 'prior_admiAcute', 'prior_spells', 'prior_asthma', 'prior_copd', 'prior_depression', 'prior_diabetes', 'prior_hypertension', 'prior_cancer', 'prior_chd', 'prior_chf']\n",
    "file_name = \"report_population_comorbidity_diagnoses_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = population_statistics(features_extra['train'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"train\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "o_summaries = population_statistics(features_extra['test'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"test\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.2.3. Charlson Comorbidity Groups\n",
    "Charlson diagnoses groups (30-day, 1-year readmission): \n",
    "* Total, Admissions, Emergency Readmission Rate, Prior Spells, Male (%), Age (IQR), LoS (IQR), TP, FP, FN, TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diagnoses = ['diagCci_01_myocardial_freq', 'diagCci_02_chf_freq', 'diagCci_03_pvd_freq', 'diagCci_04_cerebrovascular_freq', 'diagCci_05_dementia_freq', 'diagCci_06_cpd_freq', 'diagCci_07_rheumatic_freq', 'diagCci_08_ulcer_freq', 'diagCci_09_liverMild_freq', 'diagCci_10_diabetesNotChronic_freq', 'diagCci_11_diabetesChronic_freq', 'diagCci_12_hemiplegia_freq', 'diagCci_13_renal_freq', 'diagCci_14_malignancy_freq', 'diagCci_15_liverSevere_freq', 'diagCci_16_tumorSec_freq', 'diagCci_17_aids_freq', 'diagCci_18_depression_freq', 'diagCci_19_cardiac_freq', 'diagCci_20_valvular_freq', 'diagCci_21_pulmonary_freq', 'diagCci_22_vascular_freq', 'diagCci_23_hypertensionNotComplicated_freq', 'diagCci_24_hypertensionComplicated_freq', 'diagCci_25_paralysis_freq', 'diagCci_26_neuroOther_freq', 'diagCci_27_pulmonaryChronic_freq', 'diagCci_28_diabetesNotComplicated_freq', 'diagCci_29_diabetesComplicated_freq', 'diagCci_30_hypothyroidism_freq', 'diagCci_31_renal_freq', 'diagCci_32_liver_freq', 'diagCci_33_ulcerNotBleeding_freq', 'diagCci_34_psychoses_freq', 'diagCci_35_lymphoma_freq', 'diagCci_36_cancerSec_freq', 'diagCci_37_tumorNotSec_freq', 'diagCci_38_rheumatoid_freq', 'diagCci_39_coagulopathy_freq', 'diagCci_40_obesity_freq', 'diagCci_41_weightLoss_freq', 'diagCci_42_fluidDisorder_freq', 'diagCci_43_bloodLoss_freq', 'diagCci_44_anemia_freq', 'diagCci_45_alcohol_freq', 'diagCci_46_drug_freq']\n",
    "file_name = \"report_population_charlson_diagnoses_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = population_statistics(features_extra['train'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"train\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "o_summaries = population_statistics(features_extra['test'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"test\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.2.4. Most Prevalent Operatons\n",
    "Most prevalent operations variables (30-day, 1-year readmission): \n",
    "* Total, Admissions, Emergency Readmission Rate, Prior Spells, Male (%), Age (IQR), LoS (IQR), TP, FP, FN, TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diagnoses = ['operOPCSL1_0t30d_others_cnt', 'operOPCSL1_0t30d_prevalence_1_cnt', 'operOPCSL1_0t30d_prevalence_2_cnt', 'operOPCSL1_0t30d_prevalence_3_cnt', 'operOPCSL1_0t30d_prevalence_4_cnt', 'operOPCSL1_0t30d_prevalence_5_cnt', 'operOPCSL1_0t30d_prevalence_6_cnt', 'operOPCSL1_0t30d_prevalence_7_cnt', 'operOPCSL1_0t30d_prevalence_8_cnt', 'operOPCSL1_0t30d_prevalence_9_cnt', 'operOPCSL1_0t30d_prevalence_10_cnt', 'operOPCSL1_0t30d_prevalence_11_cnt', 'operOPCSL1_0t30d_prevalence_12_cnt', 'operOPCSL1_0t30d_prevalence_13_cnt', 'operOPCSL1_0t30d_prevalence_14_cnt', 'operOPCSL1_0t30d_prevalence_15_cnt', 'operOPCSL1_0t30d_prevalence_16_cnt', 'operOPCSL1_0t30d_prevalence_17_cnt', 'operOPCSL1_0t30d_prevalence_18_cnt', 'operOPCSL1_0t30d_prevalence_19_cnt', 'operOPCSL1_0t30d_prevalence_20_cnt', 'operOPCSL1_0t30d_prevalence_21_cnt', 'operOPCSL1_0t30d_prevalence_22_cnt', 'operOPCSL1_0t30d_prevalence_23_cnt', 'operOPCSL1_0t30d_prevalence_24_cnt', 'operOPCSL1_0t30d_prevalence_25_cnt', 'operOPCSL1_0t30d_prevalence_26_cnt', 'operOPCSL1_0t30d_prevalence_27_cnt', 'operOPCSL1_0t30d_prevalence_28_cnt', 'operOPCSL1_0t30d_prevalence_29_cnt', 'operOPCSL1_0t30d_prevalence_30_cnt'\n",
    "            , 'operOPCSL1_30t90d_others_cnt', 'operOPCSL1_30t90d_prevalence_1_cnt', 'operOPCSL1_30t90d_prevalence_2_cnt', 'operOPCSL1_30t90d_prevalence_3_cnt', 'operOPCSL1_30t90d_prevalence_4_cnt', 'operOPCSL1_30t90d_prevalence_5_cnt', 'operOPCSL1_30t90d_prevalence_6_cnt', 'operOPCSL1_30t90d_prevalence_7_cnt', 'operOPCSL1_30t90d_prevalence_8_cnt', 'operOPCSL1_30t90d_prevalence_9_cnt', 'operOPCSL1_30t90d_prevalence_10_cnt', 'operOPCSL1_30t90d_prevalence_11_cnt', 'operOPCSL1_30t90d_prevalence_12_cnt', 'operOPCSL1_30t90d_prevalence_13_cnt', 'operOPCSL1_30t90d_prevalence_14_cnt', 'operOPCSL1_30t90d_prevalence_15_cnt', 'operOPCSL1_30t90d_prevalence_16_cnt', 'operOPCSL1_30t90d_prevalence_17_cnt', 'operOPCSL1_30t90d_prevalence_18_cnt', 'operOPCSL1_30t90d_prevalence_19_cnt', 'operOPCSL1_30t90d_prevalence_20_cnt', 'operOPCSL1_30t90d_prevalence_21_cnt', 'operOPCSL1_30t90d_prevalence_22_cnt', 'operOPCSL1_30t90d_prevalence_23_cnt', 'operOPCSL1_30t90d_prevalence_24_cnt', 'operOPCSL1_30t90d_prevalence_25_cnt', 'operOPCSL1_30t90d_prevalence_26_cnt', 'operOPCSL1_30t90d_prevalence_27_cnt', 'operOPCSL1_30t90d_prevalence_28_cnt', 'operOPCSL1_30t90d_prevalence_29_cnt', 'operOPCSL1_30t90d_prevalence_30_cnt'\n",
    "            , 'operOPCSL1_90t180d_others_cnt', 'operOPCSL1_90t180d_prevalence_1_cnt', 'operOPCSL1_90t180d_prevalence_2_cnt', 'operOPCSL1_90t180d_prevalence_3_cnt', 'operOPCSL1_90t180d_prevalence_4_cnt', 'operOPCSL1_90t180d_prevalence_5_cnt', 'operOPCSL1_90t180d_prevalence_6_cnt', 'operOPCSL1_90t180d_prevalence_7_cnt', 'operOPCSL1_90t180d_prevalence_8_cnt', 'operOPCSL1_90t180d_prevalence_9_cnt', 'operOPCSL1_90t180d_prevalence_10_cnt', 'operOPCSL1_90t180d_prevalence_11_cnt', 'operOPCSL1_90t180d_prevalence_12_cnt', 'operOPCSL1_90t180d_prevalence_13_cnt', 'operOPCSL1_90t180d_prevalence_14_cnt', 'operOPCSL1_90t180d_prevalence_15_cnt', 'operOPCSL1_90t180d_prevalence_16_cnt', 'operOPCSL1_90t180d_prevalence_17_cnt', 'operOPCSL1_90t180d_prevalence_18_cnt', 'operOPCSL1_90t180d_prevalence_19_cnt', 'operOPCSL1_90t180d_prevalence_20_cnt', 'operOPCSL1_90t180d_prevalence_21_cnt', 'operOPCSL1_90t180d_prevalence_22_cnt', 'operOPCSL1_90t180d_prevalence_23_cnt', 'operOPCSL1_90t180d_prevalence_24_cnt', 'operOPCSL1_90t180d_prevalence_25_cnt', 'operOPCSL1_90t180d_prevalence_26_cnt', 'operOPCSL1_90t180d_prevalence_27_cnt', 'operOPCSL1_90t180d_prevalence_28_cnt', 'operOPCSL1_90t180d_prevalence_29_cnt', 'operOPCSL1_90t180d_prevalence_30_cnt'\n",
    "            , 'operOPCSL1_180t365d_others_cnt', 'operOPCSL1_180t365d_prevalence_1_cnt', 'operOPCSL1_180t365d_prevalence_2_cnt', 'operOPCSL1_180t365d_prevalence_3_cnt', 'operOPCSL1_180t365d_prevalence_4_cnt', 'operOPCSL1_180t365d_prevalence_5_cnt', 'operOPCSL1_180t365d_prevalence_6_cnt', 'operOPCSL1_180t365d_prevalence_7_cnt', 'operOPCSL1_180t365d_prevalence_8_cnt', 'operOPCSL1_180t365d_prevalence_9_cnt', 'operOPCSL1_180t365d_prevalence_10_cnt', 'operOPCSL1_180t365d_prevalence_11_cnt', 'operOPCSL1_180t365d_prevalence_12_cnt', 'operOPCSL1_180t365d_prevalence_13_cnt', 'operOPCSL1_180t365d_prevalence_14_cnt', 'operOPCSL1_180t365d_prevalence_15_cnt', 'operOPCSL1_180t365d_prevalence_16_cnt', 'operOPCSL1_180t365d_prevalence_17_cnt', 'operOPCSL1_180t365d_prevalence_18_cnt', 'operOPCSL1_180t365d_prevalence_19_cnt', 'operOPCSL1_180t365d_prevalence_20_cnt', 'operOPCSL1_180t365d_prevalence_21_cnt', 'operOPCSL1_180t365d_prevalence_22_cnt', 'operOPCSL1_180t365d_prevalence_23_cnt', 'operOPCSL1_180t365d_prevalence_24_cnt', 'operOPCSL1_180t365d_prevalence_25_cnt', 'operOPCSL1_180t365d_prevalence_26_cnt', 'operOPCSL1_180t365d_prevalence_27_cnt', 'operOPCSL1_180t365d_prevalence_28_cnt', 'operOPCSL1_180t365d_prevalence_29_cnt', 'operOPCSL1_180t365d_prevalence_30_cnt'\n",
    "            , 'operOPCSL1_365t730d_others_cnt', 'operOPCSL1_365t730d_prevalence_1_cnt', 'operOPCSL1_365t730d_prevalence_2_cnt', 'operOPCSL1_365t730d_prevalence_3_cnt', 'operOPCSL1_365t730d_prevalence_4_cnt', 'operOPCSL1_365t730d_prevalence_5_cnt', 'operOPCSL1_365t730d_prevalence_6_cnt', 'operOPCSL1_365t730d_prevalence_7_cnt', 'operOPCSL1_365t730d_prevalence_8_cnt', 'operOPCSL1_365t730d_prevalence_9_cnt', 'operOPCSL1_365t730d_prevalence_10_cnt', 'operOPCSL1_365t730d_prevalence_11_cnt', 'operOPCSL1_365t730d_prevalence_12_cnt', 'operOPCSL1_365t730d_prevalence_13_cnt', 'operOPCSL1_365t730d_prevalence_14_cnt', 'operOPCSL1_365t730d_prevalence_15_cnt', 'operOPCSL1_365t730d_prevalence_16_cnt', 'operOPCSL1_365t730d_prevalence_17_cnt', 'operOPCSL1_365t730d_prevalence_18_cnt', 'operOPCSL1_365t730d_prevalence_19_cnt', 'operOPCSL1_365t730d_prevalence_20_cnt', 'operOPCSL1_365t730d_prevalence_21_cnt', 'operOPCSL1_365t730d_prevalence_22_cnt', 'operOPCSL1_365t730d_prevalence_23_cnt', 'operOPCSL1_365t730d_prevalence_24_cnt', 'operOPCSL1_365t730d_prevalence_25_cnt', 'operOPCSL1_365t730d_prevalence_26_cnt', 'operOPCSL1_365t730d_prevalence_27_cnt', 'operOPCSL1_365t730d_prevalence_28_cnt', 'operOPCSL1_365t730d_prevalence_29_cnt', 'operOPCSL1_365t730d_prevalence_30_cnt']\n",
    "file_name = \"report_population_operations_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = population_statistics(features_extra['train'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"train\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "o_summaries = population_statistics(features_extra['test'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"test\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.2.4. Most Prevalent Main Speciality\n",
    "Most prevalent operations variables (30-day, 1-year readmission): \n",
    "* Total, Admissions, Emergency Readmission Rate, Prior Spells, Male (%), Age (IQR), LoS (IQR), TP, FP, FN, TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diagnoses = ['mainspef_0t30d_others_cnt', 'mainspef_0t30d_prevalence_1_cnt', 'mainspef_0t30d_prevalence_2_cnt', 'mainspef_0t30d_prevalence_3_cnt', 'mainspef_0t30d_prevalence_4_cnt', 'mainspef_0t30d_prevalence_5_cnt', 'mainspef_0t30d_prevalence_6_cnt', 'mainspef_0t30d_prevalence_7_cnt', 'mainspef_0t30d_prevalence_8_cnt', 'mainspef_0t30d_prevalence_9_cnt', 'mainspef_0t30d_prevalence_10_cnt'\n",
    "            , 'mainspef_30t90d_others_cnt', 'mainspef_30t90d_prevalence_1_cnt', 'mainspef_30t90d_prevalence_2_cnt', 'mainspef_30t90d_prevalence_3_cnt', 'mainspef_30t90d_prevalence_4_cnt', 'mainspef_30t90d_prevalence_5_cnt', 'mainspef_30t90d_prevalence_6_cnt', 'mainspef_30t90d_prevalence_7_cnt', 'mainspef_30t90d_prevalence_8_cnt', 'mainspef_30t90d_prevalence_9_cnt', 'mainspef_30t90d_prevalence_10_cnt'\n",
    "            , 'mainspef_90t180d_others_cnt', 'mainspef_90t180d_prevalence_1_cnt', 'mainspef_90t180d_prevalence_2_cnt', 'mainspef_90t180d_prevalence_3_cnt', 'mainspef_90t180d_prevalence_4_cnt', 'mainspef_90t180d_prevalence_5_cnt', 'mainspef_90t180d_prevalence_6_cnt', 'mainspef_90t180d_prevalence_7_cnt', 'mainspef_90t180d_prevalence_8_cnt', 'mainspef_90t180d_prevalence_9_cnt', 'mainspef_90t180d_prevalence_10_cnt'\n",
    "            , 'mainspef_180t365d_others_cnt', 'mainspef_180t365d_prevalence_1_cnt', 'mainspef_180t365d_prevalence_2_cnt', 'mainspef_180t365d_prevalence_3_cnt', 'mainspef_180t365d_prevalence_4_cnt', 'mainspef_180t365d_prevalence_5_cnt', 'mainspef_180t365d_prevalence_6_cnt', 'mainspef_180t365d_prevalence_7_cnt', 'mainspef_180t365d_prevalence_8_cnt', 'mainspef_180t365d_prevalence_9_cnt', 'mainspef_180t365d_prevalence_10_cnt'\n",
    "            , 'mainspef_365t730d_others_cnt', 'mainspef_365t730d_prevalence_1_cnt', 'mainspef_365t730d_prevalence_2_cnt', 'mainspef_365t730d_prevalence_3_cnt', 'mainspef_365t730d_prevalence_4_cnt', 'mainspef_365t730d_prevalence_5_cnt', 'mainspef_365t730d_prevalence_6_cnt', 'mainspef_365t730d_prevalence_7_cnt', 'mainspef_365t730d_prevalence_8_cnt', 'mainspef_365t730d_prevalence_9_cnt', 'mainspef_365t730d_prevalence_10_cnt']\n",
    "file_name = \"report_population_operations_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = population_statistics(features_extra['train'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"train\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "o_summaries = population_statistics(features_extra['test'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"test\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.2.5. Other Variables\n",
    "Other variables (30-day, 1-year readmission): \n",
    "* Total, Admissions, Emergency Readmission Rate, Prior Spells, Male (%), Age (IQR), LoS (IQR), TP, FP, FN, TN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diagnoses = ['gapDays_0t30d_avg', 'gapDays_30t90d_avg', 'gapDays_90t180d_avg', 'gapDays_180t365d_avg', 'gapDays_365t730d_avg', \n",
    "             'epidur_0t30d_avg', 'epidur_30t90d_avg', 'epidur_90t180d_avg', 'epidur_180t365d_avg', 'epidur_365t730d_avg', \n",
    "             'preopdur_0t30d_avg', 'preopdur_30t90d_avg', 'preopdur_90t180d_avg', 'preopdur_180t365d_avg', 'preopdur_365t730d_avg', \n",
    "             'posopdur_0t30d_avg', 'posopdur_30t90d_avg', 'posopdur_90t180d_avg', 'posopdur_180t365d_avg', 'posopdur_365t730d_avg']\n",
    "file_name = \"report_population_other_variables_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "o_summaries = population_statistics(features_extra['train'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"train\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)\n",
    "\n",
    "o_summaries = population_statistics(features_extra['test'], diagnoses)\n",
    "readers_writers.save_csv(path=CONSTANTS.io_path, title=file_name + \"test\", data=o_summaries, append=False, extension=\"csv\", header=o_summaries.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 5.3. Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "file_name = \"report_population_\" + method_name + \"_\" + target_feature + \"_\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.3.1. ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig, summaries = plots.roc(training_method.model_predict[\"test\"], features_extra[\"test\"][target_feature], \n",
    "                           title=\"ROC Curve\", lw=2)\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# save\n",
    "plt.savefig(os.path.join(CONSTANTS.io_path, file_name + \"_roc\" + \".pdf\"), \n",
    "            dpi=300, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=\"pdf\",\n",
    "            transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.3.2. Precision Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fig, summaries = plots.precision_recall(training_method.model_predict[\"test\"], \n",
    "                                        features_extra[\"test\"][target_feature], \n",
    "                                        title=\"Precision-Recall Curve\", lw=2)\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# save\n",
    "plt.savefig(os.path.join(CONSTANTS.io_path, file_name + \"_precision_recall\" + \".pdf\"), \n",
    "            dpi=300, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=\"pdf\",\n",
    "            transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.3.3. Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig, summaries = plots.learning_curve(training_method.model_train, \n",
    "                                      features_extra[\"test\"][features_names_selected], \n",
    "                                      features_extra[\"test\"][target_feature],\n",
    "                                      title=\"Learning Curve\", ylim=None, cv=None, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5))\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# save\n",
    "plt.savefig(os.path.join(CONSTANTS.io_path, file_name + \"_learning_curve\" + \".pdf\"), \n",
    "            dpi=300, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=\"pdf\",\n",
    "            transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### 5.3.4. Validation Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Set the model's metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# method metadata\n",
    "if method_name == \"lr\":\n",
    "    param_name = \"clf__C\"\n",
    "    param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]\n",
    "elif method_name == \"rfc\":\n",
    "    param_name = \"max_features\"\n",
    "    param_range = range(1, 4, 1) # range(1, 20, 1)\n",
    "elif method_name == \"nn\":\n",
    "    param_name = \"alpha\"\n",
    "    param_range = range(1e4, 1e6, 9e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig, summaries = plots.validation_curve(training_method.model_train, \n",
    "                                        features_extra[\"test\"][features_names_selected], \n",
    "                                        features_extra[\"test\"][target_feature],\n",
    "                                        param_name, param_range, \n",
    "                                        title=\"Learning Curve\", ylim=None, cv=None, lw=2, n_jobs=-1)\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# save\n",
    "plt.savefig(os.path.join(CONSTANTS.io_path, file_name + \"_validation_curve\" + \".pdf\"), \n",
    "            dpi=300, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=\"pdf\",\n",
    "            transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
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