mimic_without_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "be5e7a1a7278c138e277e90b019db4961154c731"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/home/ronaldsonbellande/Desktop/Healthcare Data Analytics/HealthCare Analysis Self/Healthcare-Data-Analytics/Disease Analysis\nModules imported \n\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from operator import itemgetter    \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "get_ipython().magic(u'matplotlib inline')\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import preprocessing\n",
    "from sklearn.impute import SimpleImputer\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler, LabelEncoder, MinMaxScaler, OneHotEncoder, LabelBinarizer\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score, mean_absolute_error\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV, KFold, cross_val_predict, StratifiedKFold, train_test_split, learning_curve, ShuffleSplit\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection, preprocessing\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV,KFold, cross_val_predict, StratifiedKFold, train_test_split, learning_curve, ShuffleSplit\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, ShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, accuracy_score, f1_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score, auc\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from mlxtend.preprocessing import shuffle_arrays_unison\n",
    "\n",
    "print(os.getcwd())\n",
    "print(\"Modules imported \\n\")\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "d624986c6d662b3dec980c593981fb913cb00c01"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "With ID (58976, 28)\nWithout ID (58976, 27)\n"
     ]
    }
   ],
   "source": [
    "mimic_data = pd.read_csv('mimic3d.csv')\n",
    "data_full = mimic_data.drop('hadm_id', 1)\n",
    "print(\"With ID\", mimic_data.shape)\n",
    "print(\"Without ID\",data_full.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "a3d700d5763270c86b368668e9ef7af00372c79c",
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 58976 entries, 0 to 58975\nData columns (total 27 columns):\n #   Column            Non-Null Count  Dtype  \n---  ------            --------------  -----  \n 0   gender            58976 non-null  object \n 1   age               58976 non-null  int64  \n 2   LOSdays           58976 non-null  float64\n 3   admit_type        58976 non-null  object \n 4   admit_location    58976 non-null  object \n 5   AdmitDiagnosis    58951 non-null  object \n 6   insurance         58976 non-null  object \n 7   religion          58518 non-null  object \n 8   marital_status    48848 non-null  object \n 9   ethnicity         58976 non-null  object \n 10  NumCallouts       58976 non-null  float64\n 11  NumDiagnosis      58976 non-null  float64\n 12  NumProcs          58976 non-null  float64\n 13  AdmitProcedure    58976 non-null  object \n 14  NumCPTevents      58976 non-null  float64\n 15  NumInput          58976 non-null  float64\n 16  NumLabs           58976 non-null  float64\n 17  NumMicroLabs      58976 non-null  float64\n 18  NumNotes          58976 non-null  float64\n 19  NumOutput         58976 non-null  float64\n 20  NumRx             58976 non-null  float64\n 21  NumProcEvents     58976 non-null  float64\n 22  NumTransfers      58976 non-null  float64\n 23  NumChartEvents    58976 non-null  float64\n 24  ExpiredHospital   58976 non-null  int64  \n 25  TotalNumInteract  58976 non-null  float64\n 26  LOSgroupNum       58976 non-null  int64  \ndtypes: float64(15), int64(3), object(9)\nmemory usage: 12.1+ MB\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                age       LOSdays   NumCallouts  NumDiagnosis      NumProcs  \\\n",
       "count  58976.000000  58976.000000  58976.000000   58976.00000  58976.000000   \n",
       "mean      53.005884     10.114955      0.099374       2.67596      0.785599   \n",
       "std       26.028120     12.456231      0.163437       8.26559      3.558090   \n",
       "min        0.000000      0.000000      0.000000       0.00000      0.000000   \n",
       "25%       43.000000      3.710000      0.000000       0.82000      0.210000   \n",
       "50%       59.000000      6.460000      0.000000       1.41000      0.420000   \n",
       "75%       73.000000     11.790000      0.160000       2.40000      0.710000   \n",
       "max       89.000000    294.630000      4.760000     450.00000    275.000000   \n",
       "\n",
       "       NumCPTevents      NumInput       NumLabs  NumMicroLabs      NumNotes  \\\n",
       "count  58976.000000  58976.000000  58976.000000  58976.000000  58976.000000   \n",
       "mean       1.074118     30.379017     46.421124      1.219913      6.464789   \n",
       "std        2.027148     62.430163     76.061561      4.236693    106.709903   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      4.850000     26.980000      0.160000      0.140000   \n",
       "50%        0.970000     13.990000     38.520000      0.510000      0.330000   \n",
       "75%        1.560000     34.360000     50.860000      1.350000      0.770000   \n",
       "max      225.000000   6825.000000   5175.000000    375.000000   7500.000000   \n",
       "\n",
       "          NumOutput         NumRx  NumProcEvents  NumTransfers  \\\n",
       "count  58976.000000  58976.000000   58976.000000  58976.000000   \n",
       "mean       7.111594      9.593702       0.691903      1.093313   \n",
       "std        7.551822     12.601338       2.117615      3.169441   \n",
       "min        0.000000      0.000000       0.000000      0.000000   \n",
       "25%        1.750000      4.330000       0.000000      0.380000   \n",
       "50%        5.310000      8.370000       0.000000      0.650000   \n",
       "75%       10.490000     12.270000       0.880000      1.060000   \n",
       "max      375.000000    750.000000     100.000000    125.000000   \n",
       "\n",
       "       NumChartEvents  ExpiredHospital  TotalNumInteract   LOSgroupNum  \n",
       "count    58976.000000     58976.000000      58976.000000  58976.000000  \n",
       "mean       528.505172         0.099261        636.115579      1.360248  \n",
       "std        640.250063         0.299014        783.938087      1.130337  \n",
       "min          0.000000         0.000000          0.000000      0.000000  \n",
       "25%        207.410000         0.000000        267.407500      0.000000  \n",
       "50%        417.875000         0.000000        497.335000      1.000000  \n",
       "75%        700.302500         0.000000        811.382500      2.000000  \n",
       "max      49325.000000         1.000000      68600.000000      3.000000  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>LOSdays</th>\n      <th>NumCallouts</th>\n      <th>NumDiagnosis</th>\n      <th>NumProcs</th>\n      <th>NumCPTevents</th>\n      <th>NumInput</th>\n      <th>NumLabs</th>\n      <th>NumMicroLabs</th>\n      <th>NumNotes</th>\n      <th>NumOutput</th>\n      <th>NumRx</th>\n      <th>NumProcEvents</th>\n      <th>NumTransfers</th>\n      <th>NumChartEvents</th>\n      <th>ExpiredHospital</th>\n      <th>TotalNumInteract</th>\n      <th>LOSgroupNum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.00000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>53.005884</td>\n      <td>10.114955</td>\n      <td>0.099374</td>\n      <td>2.67596</td>\n      <td>0.785599</td>\n      <td>1.074118</td>\n      <td>30.379017</td>\n      <td>46.421124</td>\n      <td>1.219913</td>\n      <td>6.464789</td>\n      <td>7.111594</td>\n      <td>9.593702</td>\n      <td>0.691903</td>\n      <td>1.093313</td>\n      <td>528.505172</td>\n      <td>0.099261</td>\n      <td>636.115579</td>\n      <td>1.360248</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>26.028120</td>\n      <td>12.456231</td>\n      <td>0.163437</td>\n      <td>8.26559</td>\n      <td>3.558090</td>\n      <td>2.027148</td>\n      <td>62.430163</td>\n      <td>76.061561</td>\n      <td>4.236693</td>\n      <td>106.709903</td>\n      <td>7.551822</td>\n      <td>12.601338</td>\n      <td>2.117615</td>\n      <td>3.169441</td>\n      <td>640.250063</td>\n      <td>0.299014</td>\n      <td>783.938087</td>\n      <td>1.130337</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>43.000000</td>\n      <td>3.710000</td>\n      <td>0.000000</td>\n      <td>0.82000</td>\n      <td>0.210000</td>\n      <td>0.000000</td>\n      <td>4.850000</td>\n      <td>26.980000</td>\n      <td>0.160000</td>\n      <td>0.140000</td>\n      <td>1.750000</td>\n      <td>4.330000</td>\n      <td>0.000000</td>\n      <td>0.380000</td>\n      <td>207.410000</td>\n      <td>0.000000</td>\n      <td>267.407500</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>59.000000</td>\n      <td>6.460000</td>\n      <td>0.000000</td>\n      <td>1.41000</td>\n      <td>0.420000</td>\n      <td>0.970000</td>\n      <td>13.990000</td>\n      <td>38.520000</td>\n      <td>0.510000</td>\n      <td>0.330000</td>\n      <td>5.310000</td>\n      <td>8.370000</td>\n      <td>0.000000</td>\n      <td>0.650000</td>\n      <td>417.875000</td>\n      <td>0.000000</td>\n      <td>497.335000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>73.000000</td>\n      <td>11.790000</td>\n      <td>0.160000</td>\n      <td>2.40000</td>\n      <td>0.710000</td>\n      <td>1.560000</td>\n      <td>34.360000</td>\n      <td>50.860000</td>\n      <td>1.350000</td>\n      <td>0.770000</td>\n      <td>10.490000</td>\n      <td>12.270000</td>\n      <td>0.880000</td>\n      <td>1.060000</td>\n      <td>700.302500</td>\n      <td>0.000000</td>\n      <td>811.382500</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>89.000000</td>\n      <td>294.630000</td>\n      <td>4.760000</td>\n      <td>450.00000</td>\n      <td>275.000000</td>\n      <td>225.000000</td>\n      <td>6825.000000</td>\n      <td>5175.000000</td>\n      <td>375.000000</td>\n      <td>7500.000000</td>\n      <td>375.000000</td>\n      <td>750.000000</td>\n      <td>100.000000</td>\n      <td>125.000000</td>\n      <td>49325.000000</td>\n      <td>1.000000</td>\n      <td>68600.000000</td>\n      <td>3.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "data_full.info()\n",
    "data_full.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "1fce3fd763d30cf516d00815bb386cd95cea14ec",
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  gender  age  LOSdays admit_type             admit_location  \\\n",
       "0      F   35     6.17  EMERGENCY  CLINIC REFERRAL/PREMATURE   \n",
       "1      M   59     4.04  EMERGENCY       EMERGENCY ROOM ADMIT   \n",
       "2      F   48    12.04  EMERGENCY       EMERGENCY ROOM ADMIT   \n",
       "3      F   73     7.29  EMERGENCY       EMERGENCY ROOM ADMIT   \n",
       "4      M   60     4.88  EMERGENCY  TRANSFER FROM HOSP/EXTRAM   \n",
       "5      F   54     4.38   ELECTIVE  PHYS REFERRAL/NORMAL DELI   \n",
       "6      M   21    14.38  EMERGENCY  CLINIC REFERRAL/PREMATURE   \n",
       "7      M   67    10.08  EMERGENCY  TRANSFER FROM HOSP/EXTRAM   \n",
       "8      F   49     0.63   ELECTIVE  PHYS REFERRAL/NORMAL DELI   \n",
       "9      M   55     6.17  EMERGENCY  CLINIC REFERRAL/PREMATURE   \n",
       "\n",
       "                                     AdmitDiagnosis insurance  \\\n",
       "0                             DIABETIC KETOACIDOSIS   Private   \n",
       "1                                    UPPER GI BLEED   Private   \n",
       "2                                        COPD FLARE   Private   \n",
       "3                                 BOWEL OBSTRUCTION   Private   \n",
       "4                           CORONARY ARTERY DISEASE   Private   \n",
       "5                               RENAL MASS LEFT/SDA   Private   \n",
       "6  MOTOR VEHICLE ACCIDENT WITH UNSPECIFIED INJURIES  Medicaid   \n",
       "7                           CORONARY ARTERY DISEASE  Medicare   \n",
       "8            RIGHT SHOULDER ADHESIVE CAPSULITIS/SDA  Medicaid   \n",
       "9                                         PNEUMONIA  Medicare   \n",
       "\n",
       "            religion marital_status               ethnicity  ...  \\\n",
       "0  PROTESTANT QUAKER       DIVORCED                   WHITE  ...   \n",
       "1      NOT SPECIFIED         SINGLE                   WHITE  ...   \n",
       "2      NOT SPECIFIED         SINGLE  BLACK/AFRICAN AMERICAN  ...   \n",
       "3             JEWISH        MARRIED                   WHITE  ...   \n",
       "4           CATHOLIC        MARRIED                   WHITE  ...   \n",
       "5       EPISCOPALIAN        MARRIED                   WHITE  ...   \n",
       "6      NOT SPECIFIED         SINGLE      HISPANIC OR LATINO  ...   \n",
       "7           CATHOLIC        MARRIED                   WHITE  ...   \n",
       "8           CATHOLIC         SINGLE                   WHITE  ...   \n",
       "9  PROTESTANT QUAKER         SINGLE                   WHITE  ...   \n",
       "\n",
       "   NumMicroLabs  NumNotes  NumOutput  NumRx  NumProcEvents  NumTransfers  \\\n",
       "0          0.65      0.05       5.19  14.91           1.13          0.65   \n",
       "1          1.24      1.59       5.45   7.18           0.99          1.24   \n",
       "2          0.33      0.15       4.15   6.23           0.00          0.33   \n",
       "3          0.69      0.17       9.05  11.52           0.00          0.96   \n",
       "4          0.61      0.34      16.19  25.00           2.87          2.05   \n",
       "5          0.00      0.11       7.99   9.13           1.14          0.91   \n",
       "6          1.88      0.21      18.01   9.94           4.10          0.21   \n",
       "7          0.30      0.15      15.48  14.48           2.28          0.60   \n",
       "8          0.00      0.00       0.00   0.00           0.00          4.76   \n",
       "9          0.81      0.21      16.53  10.05           1.13          0.49   \n",
       "\n",
       "   NumChartEvents  ExpiredHospital  TotalNumInteract  LOSgroupNum  \n",
       "0          398.70                0            493.89            1  \n",
       "1          373.02                0            465.71            1  \n",
       "2          286.21                0            344.00            3  \n",
       "3          526.06                0            603.05            1  \n",
       "4          554.92                0            679.84            1  \n",
       "5          448.63                0            509.00            1  \n",
       "6         1337.13                0           1482.53            3  \n",
       "7          524.11                0            618.11            2  \n",
       "8            0.00                0             25.39            0  \n",
       "9         1221.39                0           1315.92            1  \n",
       "\n",
       "[10 rows x 27 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>gender</th>\n      <th>age</th>\n      <th>LOSdays</th>\n      <th>admit_type</th>\n      <th>admit_location</th>\n      <th>AdmitDiagnosis</th>\n      <th>insurance</th>\n      <th>religion</th>\n      <th>marital_status</th>\n      <th>ethnicity</th>\n      <th>...</th>\n      <th>NumMicroLabs</th>\n      <th>NumNotes</th>\n      <th>NumOutput</th>\n      <th>NumRx</th>\n      <th>NumProcEvents</th>\n      <th>NumTransfers</th>\n      <th>NumChartEvents</th>\n      <th>ExpiredHospital</th>\n      <th>TotalNumInteract</th>\n      <th>LOSgroupNum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>F</td>\n      <td>35</td>\n      <td>6.17</td>\n      <td>EMERGENCY</td>\n      <td>CLINIC REFERRAL/PREMATURE</td>\n      <td>DIABETIC KETOACIDOSIS</td>\n      <td>Private</td>\n      <td>PROTESTANT QUAKER</td>\n      <td>DIVORCED</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.65</td>\n      <td>0.05</td>\n      <td>5.19</td>\n      <td>14.91</td>\n      <td>1.13</td>\n      <td>0.65</td>\n      <td>398.70</td>\n      <td>0</td>\n      <td>493.89</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M</td>\n      <td>59</td>\n      <td>4.04</td>\n      <td>EMERGENCY</td>\n      <td>EMERGENCY ROOM ADMIT</td>\n      <td>UPPER GI BLEED</td>\n      <td>Private</td>\n      <td>NOT SPECIFIED</td>\n      <td>SINGLE</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>1.24</td>\n      <td>1.59</td>\n      <td>5.45</td>\n      <td>7.18</td>\n      <td>0.99</td>\n      <td>1.24</td>\n      <td>373.02</td>\n      <td>0</td>\n      <td>465.71</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>F</td>\n      <td>48</td>\n      <td>12.04</td>\n      <td>EMERGENCY</td>\n      <td>EMERGENCY ROOM ADMIT</td>\n      <td>COPD FLARE</td>\n      <td>Private</td>\n      <td>NOT SPECIFIED</td>\n      <td>SINGLE</td>\n      <td>BLACK/AFRICAN AMERICAN</td>\n      <td>...</td>\n      <td>0.33</td>\n      <td>0.15</td>\n      <td>4.15</td>\n      <td>6.23</td>\n      <td>0.00</td>\n      <td>0.33</td>\n      <td>286.21</td>\n      <td>0</td>\n      <td>344.00</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>F</td>\n      <td>73</td>\n      <td>7.29</td>\n      <td>EMERGENCY</td>\n      <td>EMERGENCY ROOM ADMIT</td>\n      <td>BOWEL OBSTRUCTION</td>\n      <td>Private</td>\n      <td>JEWISH</td>\n      <td>MARRIED</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.69</td>\n      <td>0.17</td>\n      <td>9.05</td>\n      <td>11.52</td>\n      <td>0.00</td>\n      <td>0.96</td>\n      <td>526.06</td>\n      <td>0</td>\n      <td>603.05</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M</td>\n      <td>60</td>\n      <td>4.88</td>\n      <td>EMERGENCY</td>\n      <td>TRANSFER FROM HOSP/EXTRAM</td>\n      <td>CORONARY ARTERY DISEASE</td>\n      <td>Private</td>\n      <td>CATHOLIC</td>\n      <td>MARRIED</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.61</td>\n      <td>0.34</td>\n      <td>16.19</td>\n      <td>25.00</td>\n      <td>2.87</td>\n      <td>2.05</td>\n      <td>554.92</td>\n      <td>0</td>\n      <td>679.84</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>F</td>\n      <td>54</td>\n      <td>4.38</td>\n      <td>ELECTIVE</td>\n      <td>PHYS REFERRAL/NORMAL DELI</td>\n      <td>RENAL MASS LEFT/SDA</td>\n      <td>Private</td>\n      <td>EPISCOPALIAN</td>\n      <td>MARRIED</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.11</td>\n      <td>7.99</td>\n      <td>9.13</td>\n      <td>1.14</td>\n      <td>0.91</td>\n      <td>448.63</td>\n      <td>0</td>\n      <td>509.00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M</td>\n      <td>21</td>\n      <td>14.38</td>\n      <td>EMERGENCY</td>\n      <td>CLINIC REFERRAL/PREMATURE</td>\n      <td>MOTOR VEHICLE ACCIDENT WITH UNSPECIFIED INJURIES</td>\n      <td>Medicaid</td>\n      <td>NOT SPECIFIED</td>\n      <td>SINGLE</td>\n      <td>HISPANIC OR LATINO</td>\n      <td>...</td>\n      <td>1.88</td>\n      <td>0.21</td>\n      <td>18.01</td>\n      <td>9.94</td>\n      <td>4.10</td>\n      <td>0.21</td>\n      <td>1337.13</td>\n      <td>0</td>\n      <td>1482.53</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M</td>\n      <td>67</td>\n      <td>10.08</td>\n      <td>EMERGENCY</td>\n      <td>TRANSFER FROM HOSP/EXTRAM</td>\n      <td>CORONARY ARTERY DISEASE</td>\n      <td>Medicare</td>\n      <td>CATHOLIC</td>\n      <td>MARRIED</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.30</td>\n      <td>0.15</td>\n      <td>15.48</td>\n      <td>14.48</td>\n      <td>2.28</td>\n      <td>0.60</td>\n      <td>524.11</td>\n      <td>0</td>\n      <td>618.11</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>F</td>\n      <td>49</td>\n      <td>0.63</td>\n      <td>ELECTIVE</td>\n      <td>PHYS REFERRAL/NORMAL DELI</td>\n      <td>RIGHT SHOULDER ADHESIVE CAPSULITIS/SDA</td>\n      <td>Medicaid</td>\n      <td>CATHOLIC</td>\n      <td>SINGLE</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>4.76</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>25.39</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M</td>\n      <td>55</td>\n      <td>6.17</td>\n      <td>EMERGENCY</td>\n      <td>CLINIC REFERRAL/PREMATURE</td>\n      <td>PNEUMONIA</td>\n      <td>Medicare</td>\n      <td>PROTESTANT QUAKER</td>\n      <td>SINGLE</td>\n      <td>WHITE</td>\n      <td>...</td>\n      <td>0.81</td>\n      <td>0.21</td>\n      <td>16.53</td>\n      <td>10.05</td>\n      <td>1.13</td>\n      <td>0.49</td>\n      <td>1221.39</td>\n      <td>0</td>\n      <td>1315.92</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 27 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "data_full.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "798b9834d32ba534e5aca36d5763b9624fe00565"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "y - With Labels 0        1\n1        1\n2        3\n3        1\n4        1\n        ..\n58971    3\n58972    2\n58973    3\n58974    1\n58975    1\nName: LOSgroupNum, Length: 58976, dtype: int64\nX - With no Labels and no ID        gender  age admit_type             admit_location  NumCallouts  \\\n0          F   35  EMERGENCY  CLINIC REFERRAL/PREMATURE         0.16   \n1          M   59  EMERGENCY       EMERGENCY ROOM ADMIT         0.25   \n2          F   48  EMERGENCY       EMERGENCY ROOM ADMIT         0.00   \n3          F   73  EMERGENCY       EMERGENCY ROOM ADMIT         0.41   \n4          M   60  EMERGENCY  TRANSFER FROM HOSP/EXTRAM         0.00   \n...      ...  ...        ...                        ...          ...   \n58971      M   60  EMERGENCY  TRANSFER FROM HOSP/EXTRAM         0.00   \n58972      F   58  EMERGENCY       EMERGENCY ROOM ADMIT         0.00   \n58973      M   23  EMERGENCY  PHYS REFERRAL/NORMAL DELI         0.00   \n58974      M   69  EMERGENCY  TRANSFER FROM HOSP/EXTRAM         0.00   \n58975      M   88  EMERGENCY  CLINIC REFERRAL/PREMATURE         0.00   \n\n       NumDiagnosis  NumProcs  NumCPTevents  NumInput  NumLabs  NumMicroLabs  \\\n0              2.59      0.00          1.30     25.12    43.44          0.65   \n1              2.23      0.99          1.98     13.61    55.94          1.24   \n2              0.75      0.17          0.83     11.46    33.39          0.33   \n3              0.69      0.27          0.69     20.30    32.24          0.69   \n4              3.69      0.82          2.25     20.49    50.61          0.61   \n...             ...       ...           ...       ...      ...           ...   \n58971          0.37      0.53          0.94    330.92    66.56          0.20   \n58972          0.92      0.41          0.62     95.18    53.03          2.26   \n58973          0.60      0.54          0.06     12.66    27.28          0.54   \n58974          2.72      0.51          0.17     25.85    49.83          0.17   \n58975          2.36      0.18          1.27      6.73    39.82          1.64   \n\n       NumNotes  NumOutput  NumRx  NumProcEvents  NumTransfers  \\\n0          0.05       5.19  14.91           1.13          0.65   \n1          1.59       5.45   7.18           0.99          1.24   \n2          0.15       4.15   6.23           0.00          0.33   \n3          0.17       9.05  11.52           0.00          0.96   \n4          0.34      16.19  25.00           2.87          2.05   \n...         ...        ...    ...            ...           ...   \n58971      0.13      23.11   8.01           0.00          0.08   \n58972      0.30       7.90   0.00           0.00          0.31   \n58973      0.10       6.27   5.79           0.00          0.36   \n58974      0.35      12.24  17.69           0.00          1.02   \n58975      0.40       4.73   5.82           0.55          0.73   \n\n       NumChartEvents  TotalNumInteract  \n0              398.70            493.89  \n1              373.02            465.71  \n2              286.21            344.00  \n3              526.06            603.05  \n4              554.92            679.84  \n...               ...               ...  \n58971         1315.46           1746.31  \n58972          674.05            834.98  \n58973          207.10            261.30  \n58974          426.87            537.42  \n58975          550.36            614.59  \n\n[58976 rows x 18 columns]\n"
     ]
    }
   ],
   "source": [
    "y = data_full['LOSgroupNum']\n",
    "X = data_full.drop('LOSgroupNum', 1)\n",
    "X = X.drop('LOSdays', 1)\n",
    "X = X.drop('ExpiredHospital', 1)\n",
    "X = X.drop('AdmitDiagnosis', 1)\n",
    "X = X.drop('AdmitProcedure', 1)\n",
    "X = X.drop('marital_status', 1)\n",
    "X = X.drop('ethnicity', 1)\n",
    "X = X.drop('religion', 1)\n",
    "X = X.drop('insurance', 1)\n",
    "\n",
    "print(\"y - With Labels\", y)\n",
    "print(\"X - With no Labels and no ID \", X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "dfe87481713cb5fd1ca62ec4ad6178eea875fc85"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "data_full.groupby('LOSgroupNum').size().plot.bar()\n",
    "plt.show()\n",
    "data_full.groupby('admit_type').size().plot.bar()\n",
    "plt.show()\n",
    "data_full.groupby('admit_location').size().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "c2a17ce91d1523ebda4d7ecf75cc6a2b055f814b",
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 58976 entries, 0 to 58975\nData columns (total 18 columns):\n #   Column            Non-Null Count  Dtype  \n---  ------            --------------  -----  \n 0   gender            58976 non-null  object \n 1   age               58976 non-null  int64  \n 2   admit_type        58976 non-null  object \n 3   admit_location    58976 non-null  object \n 4   NumCallouts       58976 non-null  float64\n 5   NumDiagnosis      58976 non-null  float64\n 6   NumProcs          58976 non-null  float64\n 7   NumCPTevents      58976 non-null  float64\n 8   NumInput          58976 non-null  float64\n 9   NumLabs           58976 non-null  float64\n 10  NumMicroLabs      58976 non-null  float64\n 11  NumNotes          58976 non-null  float64\n 12  NumOutput         58976 non-null  float64\n 13  NumRx             58976 non-null  float64\n 14  NumProcEvents     58976 non-null  float64\n 15  NumTransfers      58976 non-null  float64\n 16  NumChartEvents    58976 non-null  float64\n 17  TotalNumInteract  58976 non-null  float64\ndtypes: float64(14), int64(1), object(3)\nmemory usage: 8.1+ MB\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                age   NumCallouts  NumDiagnosis      NumProcs  NumCPTevents  \\\n",
       "count  58976.000000  58976.000000   58976.00000  58976.000000  58976.000000   \n",
       "mean      53.005884      0.099374       2.67596      0.785599      1.074118   \n",
       "std       26.028120      0.163437       8.26559      3.558090      2.027148   \n",
       "min        0.000000      0.000000       0.00000      0.000000      0.000000   \n",
       "25%       43.000000      0.000000       0.82000      0.210000      0.000000   \n",
       "50%       59.000000      0.000000       1.41000      0.420000      0.970000   \n",
       "75%       73.000000      0.160000       2.40000      0.710000      1.560000   \n",
       "max       89.000000      4.760000     450.00000    275.000000    225.000000   \n",
       "\n",
       "           NumInput       NumLabs  NumMicroLabs      NumNotes     NumOutput  \\\n",
       "count  58976.000000  58976.000000  58976.000000  58976.000000  58976.000000   \n",
       "mean      30.379017     46.421124      1.219913      6.464789      7.111594   \n",
       "std       62.430163     76.061561      4.236693    106.709903      7.551822   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        4.850000     26.980000      0.160000      0.140000      1.750000   \n",
       "50%       13.990000     38.520000      0.510000      0.330000      5.310000   \n",
       "75%       34.360000     50.860000      1.350000      0.770000     10.490000   \n",
       "max     6825.000000   5175.000000    375.000000   7500.000000    375.000000   \n",
       "\n",
       "              NumRx  NumProcEvents  NumTransfers  NumChartEvents  \\\n",
       "count  58976.000000   58976.000000  58976.000000    58976.000000   \n",
       "mean       9.593702       0.691903      1.093313      528.505172   \n",
       "std       12.601338       2.117615      3.169441      640.250063   \n",
       "min        0.000000       0.000000      0.000000        0.000000   \n",
       "25%        4.330000       0.000000      0.380000      207.410000   \n",
       "50%        8.370000       0.000000      0.650000      417.875000   \n",
       "75%       12.270000       0.880000      1.060000      700.302500   \n",
       "max      750.000000     100.000000    125.000000    49325.000000   \n",
       "\n",
       "       TotalNumInteract  \n",
       "count      58976.000000  \n",
       "mean         636.115579  \n",
       "std          783.938087  \n",
       "min            0.000000  \n",
       "25%          267.407500  \n",
       "50%          497.335000  \n",
       "75%          811.382500  \n",
       "max        68600.000000  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>NumCallouts</th>\n      <th>NumDiagnosis</th>\n      <th>NumProcs</th>\n      <th>NumCPTevents</th>\n      <th>NumInput</th>\n      <th>NumLabs</th>\n      <th>NumMicroLabs</th>\n      <th>NumNotes</th>\n      <th>NumOutput</th>\n      <th>NumRx</th>\n      <th>NumProcEvents</th>\n      <th>NumTransfers</th>\n      <th>NumChartEvents</th>\n      <th>TotalNumInteract</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.00000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n      <td>58976.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>53.005884</td>\n      <td>0.099374</td>\n      <td>2.67596</td>\n      <td>0.785599</td>\n      <td>1.074118</td>\n      <td>30.379017</td>\n      <td>46.421124</td>\n      <td>1.219913</td>\n      <td>6.464789</td>\n      <td>7.111594</td>\n      <td>9.593702</td>\n      <td>0.691903</td>\n      <td>1.093313</td>\n      <td>528.505172</td>\n      <td>636.115579</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>26.028120</td>\n      <td>0.163437</td>\n      <td>8.26559</td>\n      <td>3.558090</td>\n      <td>2.027148</td>\n      <td>62.430163</td>\n      <td>76.061561</td>\n      <td>4.236693</td>\n      <td>106.709903</td>\n      <td>7.551822</td>\n      <td>12.601338</td>\n      <td>2.117615</td>\n      <td>3.169441</td>\n      <td>640.250063</td>\n      <td>783.938087</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>43.000000</td>\n      <td>0.000000</td>\n      <td>0.82000</td>\n      <td>0.210000</td>\n      <td>0.000000</td>\n      <td>4.850000</td>\n      <td>26.980000</td>\n      <td>0.160000</td>\n      <td>0.140000</td>\n      <td>1.750000</td>\n      <td>4.330000</td>\n      <td>0.000000</td>\n      <td>0.380000</td>\n      <td>207.410000</td>\n      <td>267.407500</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>59.000000</td>\n      <td>0.000000</td>\n      <td>1.41000</td>\n      <td>0.420000</td>\n      <td>0.970000</td>\n      <td>13.990000</td>\n      <td>38.520000</td>\n      <td>0.510000</td>\n      <td>0.330000</td>\n      <td>5.310000</td>\n      <td>8.370000</td>\n      <td>0.000000</td>\n      <td>0.650000</td>\n      <td>417.875000</td>\n      <td>497.335000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>73.000000</td>\n      <td>0.160000</td>\n      <td>2.40000</td>\n      <td>0.710000</td>\n      <td>1.560000</td>\n      <td>34.360000</td>\n      <td>50.860000</td>\n      <td>1.350000</td>\n      <td>0.770000</td>\n      <td>10.490000</td>\n      <td>12.270000</td>\n      <td>0.880000</td>\n      <td>1.060000</td>\n      <td>700.302500</td>\n      <td>811.382500</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>89.000000</td>\n      <td>4.760000</td>\n      <td>450.00000</td>\n      <td>275.000000</td>\n      <td>225.000000</td>\n      <td>6825.000000</td>\n      <td>5175.000000</td>\n      <td>375.000000</td>\n      <td>7500.000000</td>\n      <td>375.000000</td>\n      <td>750.000000</td>\n      <td>100.000000</td>\n      <td>125.000000</td>\n      <td>49325.000000</td>\n      <td>68600.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "X.info()\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "45c8a4a2737cf1a44f6be54e4d4424530e59b096"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(58976, 30)\n"
     ]
    }
   ],
   "source": [
    "categorical_columns = [\n",
    "                    'gender',                     \n",
    "                    'admit_type',\n",
    "                    'admit_location'\n",
    "                      ]\n",
    "\n",
    "for col in categorical_columns:\n",
    "    if col in X.columns:\n",
    "        one_hot_encoded = pd.get_dummies(X[col])\n",
    "        X = X.drop(col, axis=1)\n",
    "        X = X.join(one_hot_encoded, lsuffix='_left', rsuffix='_right')\n",
    "        \n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "60be44d230f432f3f087f15a238feb21deb68bbf"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(58976, 27)\n(58976, 30)\n"
     ]
    }
   ],
   "source": [
    "print(data_full.shape)\n",
    "print(X.shape)\n",
    "X_copy = X.copy()\n",
    "Y_copy = y.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "63cee0b5231c742bacfb4318b16959e4f6394b1b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "            age  NumCallouts  NumDiagnosis  NumProcs  NumCPTevents  NumInput  \\\n0     -0.691792     0.370949     -0.010400 -0.220794      0.111429 -0.084239   \n1      0.230296     0.921627     -0.053954  0.057447      0.446879 -0.268607   \n2     -0.192328    -0.608032     -0.233011 -0.173015     -0.120425 -0.303045   \n3      0.768180     1.900608     -0.240270 -0.144910     -0.189488 -0.161446   \n4      0.268716    -0.608032      0.122683  0.009668      0.580072 -0.158403   \n...         ...          ...           ...       ...           ...       ...   \n58971  0.268716    -0.608032     -0.278985 -0.071837     -0.066161  4.814076   \n58972  0.191876    -0.608032     -0.212444 -0.105563     -0.224020  1.037984   \n58973 -1.152835    -0.608032     -0.251159 -0.069026     -0.500273 -0.283824   \n58974  0.614499    -0.608032      0.005328 -0.077458     -0.446009 -0.072546   \n58975  1.344485    -0.608032     -0.038226 -0.170205      0.096630 -0.378811   \n\n        NumLabs  NumMicroLabs  NumNotes  NumOutput  ...    URGENT  \\\n0     -0.039194     -0.134520 -0.060115  -0.254456  ... -0.152244   \n1      0.125148      0.004741 -0.045683  -0.220027  ... -0.152244   \n2     -0.171325     -0.210051 -0.059178  -0.392173  ... -0.152244   \n3     -0.186444     -0.125078 -0.058990   0.256683  ... -0.152244   \n4      0.055073     -0.143961 -0.057397   1.202158  ... -0.152244   \n...         ...           ...       ...        ...  ...       ...   \n58971  0.264773     -0.240735 -0.059365   2.118501  ... -0.152244   \n58972  0.086889      0.245497 -0.057772   0.104400  ... -0.152244   \n58973 -0.251655     -0.160483 -0.059646  -0.111443  ... -0.152244   \n58974  0.044818     -0.247816 -0.057303   0.679101  ... -0.152244   \n58975 -0.086787      0.099155 -0.056835  -0.315369  ... -0.152244   \n\n       ** INFO NOT AVAILABLE **  CLINIC REFERRAL/PREMATURE  \\\n0                     -0.058916                   1.975246   \n1                     -0.058916                  -0.506266   \n2                     -0.058916                  -0.506266   \n3                     -0.058916                  -0.506266   \n4                     -0.058916                  -0.506266   \n...                         ...                        ...   \n58971                 -0.058916                  -0.506266   \n58972                 -0.058916                  -0.506266   \n58973                 -0.058916                  -0.506266   \n58974                 -0.058916                  -0.506266   \n58975                 -0.058916                   1.975246   \n\n       EMERGENCY ROOM ADMIT  HMO REFERRAL/SICK  PHYS REFERRAL/NORMAL DELI  \\\n0                 -0.792579          -0.041623                  -0.586096   \n1                  1.261704          -0.041623                  -0.586096   \n2                  1.261704          -0.041623                  -0.586096   \n3                  1.261704          -0.041623                  -0.586096   \n4                 -0.792579          -0.041623                  -0.586096   \n...                     ...                ...                        ...   \n58971             -0.792579          -0.041623                  -0.586096   \n58972              1.261704          -0.041623                  -0.586096   \n58973             -0.792579          -0.041623                   1.706205   \n58974             -0.792579          -0.041623                  -0.586096   \n58975             -0.792579          -0.041623                  -0.586096   \n\n       TRANSFER FROM HOSP/EXTRAM  TRANSFER FROM OTHER HEALT  \\\n0                       -0.40912                  -0.034718   \n1                       -0.40912                  -0.034718   \n2                       -0.40912                  -0.034718   \n3                       -0.40912                  -0.034718   \n4                        2.44427                  -0.034718   \n...                          ...                        ...   \n58971                    2.44427                  -0.034718   \n58972                   -0.40912                  -0.034718   \n58973                   -0.40912                  -0.034718   \n58974                    2.44427                  -0.034718   \n58975                   -0.40912                  -0.034718   \n\n       TRANSFER FROM SKILLED NUR  TRSF WITHIN THIS FACILITY  \n0                      -0.068195                  -0.009208  \n1                      -0.068195                  -0.009208  \n2                      -0.068195                  -0.009208  \n3                      -0.068195                  -0.009208  \n4                      -0.068195                  -0.009208  \n...                          ...                        ...  \n58971                  -0.068195                  -0.009208  \n58972                  -0.068195                  -0.009208  \n58973                  -0.068195                  -0.009208  \n58974                  -0.068195                  -0.009208  \n58975                  -0.068195                  -0.009208  \n\n[58976 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "# Normalize X values \n",
    "x_value = X_copy.values\n",
    "scaler = preprocessing.StandardScaler()\n",
    "x_scaled = scaler.fit_transform(X)\n",
    "X_normalized = pd.DataFrame(x_scaled, columns=X_copy.columns)\n",
    "print(X_normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "aefe400392c73d77e55bf9f2410e7b725df2e77c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train:  (47180, 30)\n",
      "X_test:  (11796, 30)\n",
      "y_train:  (47180,)\n",
      "y_test:  (11796,)\n",
      "Y_train 20267    1\n",
      "11968    0\n",
      "13090    3\n",
      "57540    3\n",
      "28262    0\n",
      "        ..\n",
      "54343    2\n",
      "38158    3\n",
      "860      2\n",
      "15795    1\n",
      "56422    0\n",
      "Name: LOSgroupNum, Length: 47180, dtype: int64\n",
      "Y_test 40028    1\n",
      "38143    3\n",
      "14276    0\n",
      "49801    0\n",
      "21549    1\n",
      "        ..\n",
      "50194    3\n",
      "41920    3\n",
      "22400    2\n",
      "40485    1\n",
      "23098    3\n",
      "Name: LOSgroupNum, Length: 11796, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Split into Train & Test\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.2, random_state=42)\n",
    "print ('X_train: ', X_train.shape)\n",
    "print ('X_test: ', X_test.shape)\n",
    "print ('y_train: ', y_train.shape)\n",
    "print ('y_test: ', y_test.shape)\n",
    "\n",
    "print('Y_train', y_train)\n",
    "print('Y_test', y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "f03ccf0923716b07035356abcf3acb4a51d23025"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "RandomForestClassifier: 0.906189 (0.004118)\n",
      "SGDclassifier: 0.664370 (0.013841)\n",
      "DecisionTreeClassifier: 0.866405 (0.006340)\n"
     ]
    }
   ],
   "source": [
    "seed = 42\n",
    "scoring = 'accuracy' \n",
    "\n",
    "Mymodels = []\n",
    "Mymodels.append(('RandomForestClassifier', RandomForestClassifier()))\n",
    "Mymodels.append(('SGDclassifier', SGDClassifier()))\n",
    "Mymodels.append(('DecisionTreeClassifier', DecisionTreeClassifier()))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "for name, model in Mymodels:\n",
    "    kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "    cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring='accuracy')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "e0d67a66679ee59af083b102408c69db5646ba33"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "RandomForestClassifier()\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "\n",
    "param_grid = [{},]\n",
    "\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring=scoring)\n",
    "grid_search.fit(X_normalized, y)\n",
    "\n",
    "print(grid_search.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "5a406c3bcd3c161727af4b5dcab74dca35daf853"
   },
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "b9097a12c41f10f3b86e0915577ce0d781e4758c"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1080x1800 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "trainFinalFI = X_normalized\n",
    "yFinalFI = y\n",
    "\n",
    "model.fit(trainFinalFI,yFinalFI)\n",
    "\n",
    "FI_model = pd.DataFrame({\"Feature Importance\":model.feature_importances_,}, index=trainFinalFI.columns)\n",
    "FI_model[FI_model[\"Feature Importance\"] > 0.005].sort_values(\"Feature Importance\").plot(kind=\"barh\",figsize=(15,25))\n",
    "plt.xticks(rotation=90)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "49522e46d392dfcf95d60a54481d1dce22cdef30"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                           Feature Importance\nNumTransfers                         0.280323\nNumNotes                             0.147531\nNumCallouts                          0.127375\nNumDiagnosis                         0.087290\nNumProcs                             0.041769\nNumMicroLabs                         0.040405\nNumChartEvents                       0.033130\nTotalNumInteract                     0.032768\nNumRx                                0.032586\nNumInput                             0.030375\nNumOutput                            0.027486\nNumCPTevents                         0.025721\nNumLabs                              0.024390\nage                                  0.021438\nNumProcEvents                        0.018829\nELECTIVE                             0.004396\nEMERGENCY                            0.003594\nM                                    0.003391\nEMERGENCY ROOM ADMIT                 0.003319\nF                                    0.002947\nCLINIC REFERRAL/PREMATURE            0.002864\nPHYS REFERRAL/NORMAL DELI            0.002647\nTRANSFER FROM HOSP/EXTRAM            0.002447\nNEWBORN                              0.001849\n"
     ]
    }
   ],
   "source": [
    "FI_model = pd.DataFrame({\"Feature Importance\":model.feature_importances_,}, index=trainFinalFI.columns)\n",
    "FI_model=FI_model.sort_values('Feature Importance', ascending = False)\n",
    "print(FI_model[FI_model[\"Feature Importance\"] > 0.001])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "9e4dc0ce72a8131e32acda585f4b76061034f7f0"
   },
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 10)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Error\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = 1-np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = 1-np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "afe92f8617e5290fc0317d2331947583282bac65"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/ronaldsonbellande/.local/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "metadata": {},
     "execution_count": 20
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "title = \"Learning Curves \"\n",
    "cv = ShuffleSplit(n_splits=7, test_size=0.2)\n",
    "plot_learning_curve(model, title, X_train, y_train, cv=cv, n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "91574fedd82e85ae7d616e23f4c5174213926f24"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train:  (47180, 30)\nX_test:  (11796, 30)\ny_train:  (47180,)\ny_test:  (11796,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.2, random_state=42)\n",
    "print ('X_train: ', X_train.shape)\n",
    "print ('X_test: ', X_test.shape)\n",
    "print ('y_train: ', y_train.shape)\n",
    "print ('y_test: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "e3c18714aca39c174cd2c2d747f3ee23c2491362"
   },
   "outputs": [],
   "source": [
    "# Model FINAL fit and evaluation on test\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "final_predictions = model.predict(X_test)\n",
    "\n",
    "confusion_mx = confusion_matrix(y_test, final_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "0f5410cd3c3f20bbee11842580a4cb0fb828b883"
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm,target_names,title='Confusion Matrix',cmap=None,\n",
    "                          normalize=False):\n",
    "    import itertools\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        \n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "9c064c815e2cb9ca1fa26681cc8d68998571911a"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x432 with 2 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_mx, \n",
    "                      normalize    = False,\n",
    "                      target_names = [0,1,2,3],\n",
    "                      title        = \"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "837aba4cbecd2bde6bdaab1cb8417348ee823dea"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Class  0\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x432 with 2 Axes>",
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "TPz  3205\nFNz  138\nFPz  212\nTNz  8241\nsum  11796\n[[8241  212]\n [ 138 3205]]\nSum of CM  11796\naccuracy  0.9703\nrecall  0.9587\nprecision  0.938\nF1Score  0.9482\n----------------------------------------\nClass  1\n"
     ]
    },
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     "output_type": "display_data",
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      "TPz  3401\nFNz  342\nFPz  552\nTNz  7501\nsum  11796\n[[7501  552]\n [ 342 3401]]\nSum of CM  11796\naccuracy  0.9242\nrecall  0.9086\nprecision  0.8604\nF1Score  0.8838\n----------------------------------------\nClass  2\n"
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "TPz  1368\nFNz  556\nFPz  325\nTNz  9547\nsum  11796\n[[9547  325]\n [ 556 1368]]\nSum of CM  11796\naccuracy  0.9253\nrecall  0.711\nprecision  0.808\nF1Score  0.7564\n----------------------------------------\nClass  3\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x432 with 2 Axes>",
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "TPz  2489\nFNz  297\nFPz  244\nTNz  8766\nsum  11796\n[[8766  244]\n [ 297 2489]]\nSum of CM  11796\naccuracy  0.9541\nrecall  0.8934\nprecision  0.9107\nF1Score  0.902\n----------------------------------------\nTN:  34055\nFP:  1333\nFN:  1333\nTP:  10463\n________________________________________\n"
     ]
    }
   ],
   "source": [
    "NumClasses = 4\n",
    "\n",
    "TP = 0\n",
    "TN = 0\n",
    "FP = 0\n",
    "FN = 0\n",
    "\n",
    "for z in range(NumClasses):\n",
    "# One class at a time - calculate confusion matrix\n",
    "    SumCM = np.sum(confusion_mx)\n",
    "    TPz = confusion_mx[z,z]\n",
    "    FNz = np.sum(confusion_mx[z,:], axis=0) -TPz\n",
    "    FPz = np.sum(confusion_mx[:,z], axis=0) -TPz\n",
    "    TNz = SumCM - (TPz+FNz+FPz)\n",
    "    print('Class ',z)\n",
    "  \n",
    "\n",
    "    # Create conf matrix for class z\n",
    "    cmZ = np.zeros([2, 2], dtype=np.int32)\n",
    "    cmZ[0,0] = TNz\n",
    "    cmZ[0,1] = FPz\n",
    "    cmZ[1,0] = FNz\n",
    "    cmZ[1,1] = TPz\n",
    "\n",
    "    plot_confusion_matrix(cmZ, \n",
    "                          normalize    = False,\n",
    "                          target_names = [0,1],\n",
    "                          title        = \"Confusion matrix for one class \")\n",
    "\n",
    "    accuracy = (TPz+TNz)/(TPz+TNz+FPz+FNz)\n",
    "    recall = TPz/(TPz+FNz)\n",
    "    precision = TPz/(TPz+FPz)\n",
    "    f1score = 2*recall*precision/(recall+precision)\n",
    "    \n",
    "    print('TPz ',TPz)\n",
    "    print('FNz ',FNz)\n",
    "    print('FPz ',FPz)\n",
    "    print('TNz ',TNz)\n",
    "    print('sum ', TPz+TNz+FPz+FNz)\n",
    "    print(cmZ)\n",
    "    print('Sum of CM ', np.sum(cmZ))\n",
    "    print ('accuracy ',round(accuracy,4))\n",
    "    print('recall ', round(recall,4))\n",
    "    print('precision ', round(precision,4))\n",
    "    print('F1Score ', round(f1score,4))\n",
    "    print('-'*40)\n",
    "    \n",
    "    TP = TP + TPz\n",
    "    TN = TN + TNz\n",
    "    FP = FP + FPz\n",
    "    FN = FN + FNz\n",
    "print ('TN: ', TN)\n",
    "print ('FP: ', FP)\n",
    "print ('FN: ', FN)\n",
    "print ('TP: ', TP)\n",
    "print('_'*40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "1d04b2db06faa9b5a97616249890ac562ec4ed4e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Confusion Matix for ALL Classes\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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XMm3aNIqLi1m5ciVJSUkAJCYmEhMTg91uP+/caq2LiIi40Y4dO2jZsiXNmjUjIyOD2NhYAGJjY8nIyABg69atDBw4EIvFQqdOnTh58iQFBQVkZWURFRWF3W7HbrcTFRVFVlZWjfOpIhcREdNyZ0WemJjoeh4fH098fPw5x23atIlrr70WgMLCQsLDwwEICwujsLAQAKfTSdOmTV3viYiIwOl04nQ6iYiIcG13OBw4nc4a41IiFxER03JnIv+x3V2TiooKvv76a+68885zxlIXrX611kVERNwkMzOTdu3aERYWBkCTJk0oKCgAoKCggNDQUOBMpZ2Xl+d6X35+Pg6HA4fDQX5+vmu70+nE4XDUOKcSuYiImJfFTY9a+mlbHSAmJob169cDsH79evr06ePavmHDBgzDYM+ePYSEhBAeHk50dDTZ2dkUFxdTXFxMdnY20dHRNc6p1rqIiJiWJ28IU1payvbt25kwYYJr2/Dhw0lOTiYtLc319TOAXr16sW3bNiZPnkxQUBAJCQkA2O12brnlFp566ikAbr311hpXrANYDMMw6uicLlnDXg95OwS/snHZEwy46zVvh+F3CjJSvB2CXwqyQVmlt6PwL8FeKB1b3LvCLcf5z8Lb3HKcuqCKXERETMsfbtGqRC4iIqalRC4iIuLD/CGRa9W6iIiID1NFLiIi5mX+glyJXEREzEutdREREanXVJGLiIhp+UNFrkQuIiKm5Q+JXK11ERERH6aKXEREzMv8BbkSuZlNuut6xt7cH8Mw2JVzhAnPLeV0WYVr/zP338C4Ef05XlAMQEjDIAAcTRrx3vTx9O7WhqUfbeGRV///XsUfpiTQslkoATYbmzL3MeWV96mqqre36xc/VVpaSvz1Ayk7fZqKygpuHnErU597gQfuG8+2r7diGAadruzEmwuXYLfbeevNBbw5fy42m41Gdjtz5/+RLl27evs0xA3UWhefdVmzJiTcEcu1d71GzG3TsFmt3Dak91nj5iz9O31HJdF3VBI/FJcCUHq6nD/M+4SnklefNX70k29zze1J9L71ZZqF27nll1fV+bmI/FwNGjTg87+l8dW2bL7cmsUXf/2cL7ds4bUZyXy1LZuMzO1c0foK5s8784M1t99xJ1uzdvDl11k8+rsnePLxR718BiK1p0RuYgE2Gw0bBGKzWWkYHETu8cJave9UaRmbs/ZTerr8rH1FJ88k+4AAK4EBNurxj+eJH7NYLK6ffiwvL6eivByLxUJoaCgAhmFQUlLiqtZ+3A5w8uRJv6ji/IXFYnHLoz5Ta92kjhwvZNaf1rHnsxcpOV3Gun9+y7ot35417oFRA7lz2NVs2/0vbNba/WP9aO5EYrq34YtNu1m1NtPdoYu4RWVlJf2v7s2+fTnc/+BErr7mGgAmjL+Hv36+hq5duzLttRmu8QvmzWX2GzMpKyvj8y/SvBW2uFl9T8Lu4LGKPCsri4cffphJkyaRmprqqWn9VljjhgyL60GXYc8R+atnaNQwiFE39Kk25q0V/6Drjc9zzagkjub9QKsW4bU69k0T59Lul0/TICiAuD5X1kX4IpfMZrPx5ddZ5Hx/mK0ZX7Fr504A/rhoMfv/dYTOnbuw8oP3XeMfSJjI7u/28dK0V0ma9pK3whb52TySyKuqqli0aBFPP/00ycnJbNq0icOHD3tiar816JrOfH8kn7yCYioqqkhNy6Zvz3bVxhxzFlFVZWAYBm+v2kSj/1vsVhunyyr4OH07N8b1cHfoIm4VFhZGbNz1fPHF565tNpuN20eNInX1X84aP/L2UXz8UaoHI5S65A+tdY8k8pycHFq2bEmLFi0ICAigf//+ZGRkeGJqv3XoqJOre7SjYXAgANdffSXfHfgPf5h0EzddHwVAy6b/f13wN4N6UnKOa+I/1ahhkOs9NpuVXw/oxnff/6eOzkDk4h0/fpwTJ04AUFJSwrq1f6NTpyvZl5MDnLlG/vFHH9Hpys4A5Ozd63rvZ2s+pUOHjh6PWeqIxU2Peswj18idTicRERGu1xEREez9yX84P1q7di1r164FICkpiY3LnvBEeKYVFhrCkfTXMAyDU6XldO3wCyJbNePG66N44t7/oe1lEYQEB2IAZeUVBAXYXJ959w6XYbOd+Ut03C3XsvfgMSoqq+hwRbMzf6ECRadO0651M0bf1Ner5+nrgmzejsB88o/lcs/YMVRWVlJVVcVtt43kNzcOJXbgdfzwww8YhkHPnj2ZO28+QTb44/wU1q1bS2BgIOHh4Sx55x39/0V8Rr1a7BYfH098fLzr9YC7XvNiNOb00dyJ3DRx7jn3bVz2hD5zLyjISPF2CKbTuVsU/8yovhCzwoB16ze5XgfZoKzyzOO1mW+cdYyyyjoP0+8EeyHj1Pe2uDt45GN1OBzk5+e7Xufn5+NwODwxtfyX8yVxEREz8odE7pFr5O3btyc3N5djx45RUVHB5s2biYmJ8cTUIiIipuaRitxmszFu3DhefvllqqqquP7662ndurUnphYRET/mBwW5566RX3XVVVx1lW7nKSIinuMPrfV6tdhNRETEnfwgj+te6yIiIr5MFbmIiJiWWusiIiI+zA/yuFrrIiIivkwVuYiImJa1lj/P7MuUyEVExLTUWhcREZF6TRW5iIiYllati4iI+DA/yONqrYuIiPgyVeQiImJaaq2LiIj4ME8m8pMnT7JgwQIOHTqExWLhwQcf5LLLLiM5OZnjx4/TrFkzHnnkEex2O4ZhsHjxYjIzM2nQoAEJCQlERkYCkJ6ezqpVqwAYMWIEcXFxNc6rRC4iIuIGixcvJjo6mscee4yKigpOnz7N6tWr6dGjB8OHDyc1NZXU1FRGjx5NZmYmR48eZfbs2ezdu5eFCxcybdo0iouLWblyJUlJSQAkJiYSExOD3W4/77y6Ri4iIqZlsbjncSGnTp3im2++YdCgQQAEBATQqFEjMjIyiI2NBSA2NpaMjAwAtm7dysCBA7FYLHTq1ImTJ09SUFBAVlYWUVFR2O127HY7UVFRZGVl1Ti3KnIRETEtT7XWjx07RmhoKPPmzePgwYNERkYyduxYCgsLCQ8PByAsLIzCwkIAnE4nTZs2db0/IiICp9OJ0+kkIiLCtd3hcOB0OmucW4lcRESkFhITE13P4+PjiY+Pd72urKzkwIEDjBs3jo4dO7J48WJSU1Orvd9isdTJHxZK5CIiYlruzJs/Xrc+l4iICCIiIujYsSMAffv2JTU1lSZNmlBQUEB4eDgFBQWEhoYCZyrtvLw81/vz8/NxOBw4HA52797t2u50OunatWuNcekauYiImNaPVfClPi4kLCyMiIgIjhw5AsCOHTto1aoVMTExrF+/HoD169fTp08fAGJiYtiwYQOGYbBnzx5CQkIIDw8nOjqa7OxsiouLKS4uJjs7m+jo6BrnVkUuIiKm5cmvkY8bN47Zs2dTUVFB8+bNSUhIwDAMkpOTSUtLc339DKBXr15s27aNyZMnExQUREJCAgB2u51bbrmFp556CoBbb721xhXrABbDMIy6PbWL17DXQ94Owa9sXPYEA+56zdth+J2CjBRvh+CXgmxQVuntKPxLsBdKx5iX/u6W42z9/fVuOU5dUEUuIiKmpTu7iYiI+DA/yONa7CYiIuLLVJGLiIhpqbUuIiLiw/wgj6u1LiIi4stUkYuIiGmptS4iIuLD/CCPq7UuIiLiy1SRi4iIaam1LiIi4sP8II+rtS4iIuLLVJGLiIhpqbUuIiLiw/whkau1LiIi4sNUkYuIiGn5QUGuRC4iIual1rqIiIjUa6rIRUTEtPygIFciFxER8/KH1roSuYiImJYf5HFdIxcREfFlqshFRMS0rH5QkiuRi4iIaflBHldrXURExJepIhcREdPSqnUREREfZjV/HldrXURExJepIhcREdNSa11ERMSH+UEeV2tdRETEl6kiFxER07Jg/pJciVxERExLq9ZFRESkXlNFLiIipqVV6yIiIj7MD/K4WusiIiK+TBW5iIiYlid/xnTixIkEBwdjtVqx2WwkJSVRXFxMcnIyx48fp1mzZjzyyCPY7XYMw2Dx4sVkZmbSoEEDEhISiIyMBCA9PZ1Vq1YBMGLECOLi4mqcV4lcRERMy9Ot9eeee47Q0FDX69TUVHr06MHw4cNJTU0lNTWV0aNHk5mZydGjR5k9ezZ79+5l4cKFTJs2jeLiYlauXElSUhIAiYmJxMTEYLfbzzunWusiImJaFovFLY+LlZGRQWxsLACxsbFkZGQAsHXrVgYOHIjFYqFTp06cPHmSgoICsrKyiIqKwm63Y7fbiYqKIisrq8Y5zluRz5kzp1bBP/TQQz/jlERERHxTYmKi63l8fDzx8fFnjXn55ZcB+OUvf0l8fDyFhYWEh4cDEBYWRmFhIQBOp5OmTZu63hcREYHT6cTpdBIREeHa7nA4cDqdNcZ13kTesmXL2pyXiIhIveXO1vqP7e7zefHFF3E4HBQWFvLSSy9x2WWX/Vcsl1bdn895E/ltt93m9slEREQ8yZOL3RwOBwBNmjShT58+5OTk0KRJEwoKCggPD6egoMB1/dzhcJCXl+d6b35+Pg6HA4fDwe7du13bnU4nXbt2rXHeWl8j3759O/Pnz3f9RbJv3z527txZ+zMUERExqdLSUkpKSlzPt2/fzhVXXEFMTAzr168HYP369fTp0weAmJgYNmzYgGEY7Nmzh5CQEMLDw4mOjiY7O5vi4mKKi4vJzs4mOjq6xrlrtWr9s88+Y82aNQwePJgtW7YAEBQUxOLFi3nppZcu9rxFRETqlKfq8cLCQl5//XUAKisrGTBgANHR0bRv357k5GTS0tJcXz8D6NWrF9u2bWPy5MkEBQWRkJAAgN1u55ZbbuGpp54C4NZbb61xxTrUMpGvWbOGqVOn0rx5cz788EMALr/8co4cOXJxZywiIuIBnrpFa4sWLZg+ffpZ2xs3bsyzzz571naLxcK99957zmMNGjSIQYMG1XruWrXWS0pKqq2uA6ioqCAgQF9DFxER8aZaJfIuXbqQmppabdtnn31Gt27d6iImERERt7Ba3POoz2pVUo8bN45XX32VdevWUVpaysMPP0zDhg2rfadORESkvtGvn/2f8PBwXnnlFfbt28fx48eJiIigQ4cOWK26MZyIiIg31foit2EYVFRUAFBVVVVnAYmIiLiLHxTktUvkBw8eZPr06ZSXl7tuFxcYGMjvfvc72rZtW8chioiIXBy11v/P/PnzGTJkCMOGDcNisWAYBp9++inz58/n1VdfresYRURE5DxqdZE7NzeXoUOHuv6ysVgs3HDDDRw9erROgxMREbkU/rBqvVaJvFevXmzdurXatq1bt9KrV686CUpERMQdvP0zpp5Qq58xraqqYtasWURGRhIREUF+fj779+8nJibGY4GKiIjI2Wr9M6atW7d2PW/VqhU9e/asu6hERETcoH7X0u6hnzEVERHT8uTPmHpLrb9HXlFRwZEjR/jhhx+qbe/evbvbgxIREZHaqVUi//bbb5k5cybl5eWUlJTQsGFDSktLiYiIICUlpa5jFBERuSh+UJDXLpG/88473HTTTQwbNox77rmHxYsXs3LlSoKCguo6PhERkYtW31ecu0Otvn525MgRbrjhhmrbhg8fzqefflonQYmIiLiDxeKeR31Wq0QeEhJCSUkJAGFhYRw+fJji4mJKS0vrNDgRERGpWa1a69dccw2ZmZkMGDCA66+/nhdeeAGbzUbfvn3rOj4REZGLplXr/2fs2LGu5zfddBMdO3aktLRU3yUXEZF6zQ/yeO2/fvZTXbp0cXccIiIichHOm8ifffbZWq32e+GFF9wa0E8VZOirbZ4UZNNn7g2bcvK8HYJf6tOmCRkHC70dhl8Z3Lmpx+f0h1Xr503kgwYN8mQcIiIiblerFd0+7ryJPC4uzoNhiIiIyMW4qGvkIiIivsCvW+siIiK+zmr+PO4Xlw9ERERMSxW5iIiYlj9U5LVK5OXl5axcuZJNmzZRVFTEO++8Q3Z2Nrm5ufzP//xPXccoIiJyUfzhGnmtWuvvvPMOhw4dYvLkya4PpXXr1nzxxRd1GpyIiIjUrFYV+VdffcXs2bMJDg52JXKHw4HT6azT4ERERC6FWus/DgoIoKqqqtq2H374gcaNG9dJUCIiIu7gB5312rXW+/btS0pKCseOHQOgoKCARYsW0b9//zoNTkRERGpWq0R+55130rx5cx577DFOnTrF5MmTCQ8P57bbbqvr+ERERC6a1WJxy6M+q3VrfezYsYwdO9bVUveHlYAiIuLb/OFmKbVK5P/5z3+qvS4pKXE9b9GihXsjEhERcRN/qDlrlcgnT5583n3vv/++24IRERGRn6dWify/k/WJEydYsWIFXbp0qZOgRERE3KG+X992h4u6RWtYWBhjx47l4YcfZsCAAe6OSURExC08mcerqqpITEzE4XCQmJjIsWPHmDVrFkVFRURGRjJp0iQCAgIoLy8nJSWF/fv307hxY6ZMmULz5s0BWL16NWlpaVitVu655x6io6MvOO9FrwM4cuQIp0+fvti3i4iImMqaNWu4/PLLXa+XLl3K0KFDmTNnDo0aNSItLQ2AtLQ0GjVqxJw5cxg6dCjLli0D4PDhw2zevJmZM2fyzDPPsGjRorPu4XIutarIn3322Wqr1E+fPs2hQ4e49dZbf9ZJioiIeJKn7uyWn5/Ptm3bGDFiBJ988gmGYbBr1y4efvhhAOLi4lixYgW/+tWv2Lp1q+vr23379uXtt9/GMAwyMjLo378/gYGBNG/enJYtW5KTk0OnTp1qnLtWiXzQoEHVXgcHB9OmTRt+8YtfXMz5ioiIeIQ7r5EnJia6nsfHxxMfH+96vWTJEkaPHu36VldRUREhISHYbDag+m3NnU4nERERANhsNkJCQigqKsLpdNKxY0fXMWt7K/QLJvKqqip27tzJ/fffT2BgYG3OVURExHSSkpLOuf3rr7+mSZMmREZGsmvXLg9HVYtEbrVa2b59u24AIyIiPscTqeu7775j69atZGZmUlZWRklJCUuWLOHUqVNUVlZis9lwOp04HA7gTKWdn59PREQElZWVnDp1isaNG7u2/+in76lJrRa7DR06lA8++ICKioqLPE0RERHPs1rc86jJnXfeyYIFC5g7dy5Tpkyhe/fuTJ48mW7durFlyxYA0tPTiYmJAaB3796kp6cDsGXLFrp164bFYiEmJobNmzdTXl7OsWPHyM3NpUOHDhc8xxor8o0bNzJgwAA+//xzTpw4waeffkpoaGi1MfPnz7/gJCIiIv7mrrvuYtasWSxfvpx27dq51psNGjSIlJQUJk2ahN1uZ8qUKQC0bt2afv368eijj2K1Whk/fjxW64XrbYthGMb5do4ZM4Z33nmH3bt3n/cAXbt2/ZmnVnulagB4VJANyiq9HYX/2ZST5+0Q/FKfNk3IOFjo7TD8yuDOTT0+57R1+9xynKcHt3fLcepCjRX5jzm+LpO1iIhIXfHU18+8qcZE/uOK9Zp0797drQGJiIhI7dWYyMvLy1mwYAHn675bLBZSUlLqJDAREZFL5fcVeXBwsBK1iIj4LH/46rQ//Oa6iIiIadVqsZuIiIgv8vvW+p/+9CdPxSEiIuJ2ftBZV2tdRETEl9Xq189ERER8kTt//ay+UiIXERHT8vtr5CIiIr7MDwpyXSMXERHxZarIRUTEtKyYvyRXIhcREdNSa11ERETqNVXkIiJiWlq1LiIi4sP84Xvkaq2LiIj4MFXkIiJiWn5QkCuRi4iIeam1LiIiIvWaKnIRETEtPyjIlchFRMS8/KHt7A/nKCIiYlqqyEVExLQsftBbVyIXERHTMn8aV2tdRETEp6kiFxER0/KH75ErkYuIiGmZP40rkYuIiIn5QUGua+QiIiK+TBW5iIiYlr5+JiIi4sP8oe3sD+coIiJiWqrIRUTEtNRaFxER8WHmT+NqrYuIiPg0VeQiImJanmqtl5WV8dxzz1FRUUFlZSV9+/Zl5MiRHDt2jFmzZlFUVERkZCSTJk0iICCA8vJyUlJS2L9/P40bN2bKlCk0b94cgNWrV5OWlobVauWee+4hOjq6xrlVkYuIiGlZ3fS4kMDAQJ577jmmT5/Oa6+9RlZWFnv27GHp0qUMHTqUOXPm0KhRI9LS0gBIS0ujUaNGzJkzh6FDh7Js2TIADh8+zObNm5k5cybPPPMMixYtoqqq6oLnKCIiIpfAYrEQHBwMQGVlJZWVlVgsFnbt2kXfvn0BiIuLIyMjA4CtW7cSFxcHQN++fdm5cyeGYZCRkUH//v0JDAykefPmtGzZkpycnBrnVmtdRERMy52t9cTERNfz+Ph44uPjq+2vqqriySef5OjRowwZMoQWLVoQEhKCzWYDwOFw4HQ6AXA6nURERABgs9kICQmhqKgIp9NJx44dXcf86XvOR4lcRERMy51XyJOSkmrcb7VamT59OidPnuT111/nyJEjbpy9hnk9MouIiIifaNSoEd26dWPPnj2cOnWKyspK4EwV7nA4gDOVdn5+PnCmFX/q1CkaN25cbft/v+d8lMhFRMS0LBb3PC7khx9+4OTJk8CZFezbt2/n8ssvp1u3bmzZsgWA9PR0YmJiAOjduzfp6ekAbNmyhW7dumGxWIiJiWHz5s2Ul5dz7NgxcnNz6dChQ41zq7UuIiKmZfXQLWEKCgqYO3cuVVVVGIZBv3796N27N61atWLWrFksX76cdu3aMWjQIAAGDRpESkoKkyZNwm63M2XKFABat25Nv379ePTRR7FarYwfPx6rteaa22IYhlHXJ3ixSiu8HYF/CbJBWaW3o/A/m3LyvB2CX+rTpgkZBwu9HYZfGdy5qcfn/HjHf9xynBt7tHDLceqCKnIRETEtP7jVuhK5iIiYl8UP7rauxW4iIiI+TBW5iIiYllrrIiIiPsxTq9a9SYlcRERMyx8qcl0jFxER8WGqyEVExLT8oSJXIhcREdPS189ERESkXlNFLiIipmU1f0GuRC4iIual1rqIiIjUa6rIRUTEtLRqXURExIf5Q2tdidyPlJaWEn/9QMpOn6aisoKbR9zK1Ode4IH7xrPt662AQfuOnXhr0RLsdjtvvbmAN+fPxWaz0chuZ+78P9Kla1dvn4ZINcdy/82riRMpyD+OBQtDR97NiN/ef9a49M9S+dPc6ViwENm5G6krlgOQeN9Ivsn+mu5XXcPLC95zjX/tqYfYnvFPGjVuDMDj0+bQoUsPz5yUyM+gRO5HGjRowOd/S8Nut1NeXs6g2AH8asiveW1GMqGhoQTZYMojjzJ/XgqPP5HI7XfcyX33PwDAJx9/xJOPP8pHn37u5bMQqc5ms/HAEy/QsVtPTp0s5sFbBtO7fxxtOlzpGnP4+338+a03eGPZpzRuEkZB/nHXvpHjHuJ0aQmfvP/OWcee8PhzDBxyk0fOQ+qGP6xa12I3P2KxWLDb7QCUl5dTUV6OxWIhNDQUAMMwKC0pwfJ/F5V+3A5w8uRJ13aR+iSieUs6dusJQEgjO1e070Tef3KrjVmzYim/uWMcjZuEARAe0cy176p+A2nYyO6xeMWzLG76v/pMidzPVFZWck3vaK64rDmD4n/J1ddcA8CE8fdw2S9a8t1335IwcZJr/IJ5c+l6ZXueeeoJZiTP9lbYIrVy9N//IuebHXTu2bva9sMH93H4+/08fOcNPHT7//DVP9bV6nhvz5rGfb+JZd4rv6es7HRdhCxyyTySyOfNm8e9997LY4895onppAY2m40vv84i5/vDbM34il07dwLwx0WLOfzvI3Tu3IWVH7zvGv9AwkR2f7ePl6a9StK0l7wVtsgFlZws5oXJ95CQ+BKN7I2r7ausqODfB/cz450PeWbGmyQ/+ygnTpyo8XjjH/k9i9f8k7krvqCosID335pTh9FLXbFY3POozzySyOPi4nj66ac9MZXUUlhYGLFx1/PFF/9/zdtms3Hb7aNIXf2Xs8aPvH0UH3+U6sEIRWqvoryc5x++h8E33sp1vxp21v5mLS+j36AhBAQG8otWbWjVtj37cnJqPGZE85ZYLBaCghowZMSdfLtjW12FL3XI4qZHfeaRRN61a1fXtVnxnuPHj7uqkJKSEtat/RudOl3p+h80wzD45OOP6HRlZwBy9u51vfezNZ/SoUNHj8csciGGYfD676fQJrITt4590LU9ddlCUpctBKD/4F+T/dUmAAoL8jn8/T7atWtX43Hzjx11HX/z2jW07di5js5A5NJo1bofOZqby33jxlBZWUmVUcUtt47k1zcMZXDcdRT98ANg0L1HT2bPnQ/A/Hkp/D1tLYEBgYSFh/PW22ev6hXxtp3bvmTtRx/QrlNX7r85DoBxU57h0P69dLvqzBqQPgMG8fWmdMYNuxar1caE3z2PIyKCfcWFTBk9jEP7cyg5dZJRcVE89tIs+gwYxCtPPMgJZz4YBu27dGfKc9O9d5Jy0az1vS/uBhbDMAxPTHTs2DFeffVVZsyYcd4xa9euZe3atQAkJSVR5ZHI5EcWQB+55xWXlns7BFMaOWI4S5d/QFBQ0Dn3N2pg4+TpSg9H5d9CGwZ6fM4tOSfccpy+HcLccpy6UK8q8vj4eOLj412vy/TfmEcF2fSZe0PGwUJvh2BKjye/Q3ZuCVByzv192jTRZ+9hgzs39fyk5i/I9fUzERERX+aRinzWrFns3r2boqIiHnjgAUaOHMmgQYM8MbWIiPix+n4zF3fwSCKfMmWKJ6YRERGpxg/Wuqm1LiIi4svq1WI3ERERd/KDglyJXERETMwPMrla6yIiIj5MFbmIiJiWVq2LiIj4MK1aFxERkXpNFbmIiJiWHxTkSuQiImJifpDJlchFREQuUV5eHnPnzuXEiRNYLBbi4+O54YYbKC4uJjk5mePHj9OsWTMeeeQR7HY7hmGwePFiMjMzadCgAQkJCURGRgKQnp7OqlWrABgxYgRxcXE1zq1ELiIipuWpVes2m427776byMhISkpKSExMJCoqivT0dHr06MHw4cNJTU0lNTWV0aNHk5mZydGjR5k9ezZ79+5l4cKFTJs2jeLiYlauXElSUhIAiYmJxMTEYLfbzzu3FruJiIhpWSzueVxIeHi4q6Ju2LAhl19+OU6nk4yMDGJjYwGIjY0lIyMDgK1btzJw4EAsFgudOnXi5MmTFBQUkJWVRVRUFHa7HbvdTlRUFFlZWTXOrYpcRESkFhITE13P4+PjiY+PP+e4Y8eOceDAATp06EBhYSHh4eEAhIWFUVhYCIDT6aRp0///ffaIiAicTidOp5OIiAjXdofDgdPprDEuJXIRETEtdzbWf2x316S0tJQZM2YwduxYQkJCqsdisWCpgy+2q7UuIiLmZXHToxYqKiqYMWMG1113Hddccw0ATZo0oaCgAICCggJCQ0OBM5V2Xl6e6735+fk4HA4cDgf5+fmu7U6nE4fDUeO8SuQiIiKXyDAMFixYwOWXX86wYcNc22NiYli/fj0A69evp0+fPq7tGzZswDAM9uzZQ0hICOHh4URHR5OdnU1xcTHFxcVkZ2cTHR1d49xqrYuIiGl5atX6d999x4YNG7jiiit4/PHHAbjjjjsYPnw4ycnJpKWlub5+BtCrVy+2bdvG5MmTCQoKIiEhAQC73c4tt9zCU089BcCtt95a44p1AIthGEYdntslKa3wdgT+JcgGZZXejsL/bMrJu/Agcbs+bZqQcbDQ22H4lcGdm154kJvtOFzsluP0aFVzMvUmVeQiImJafnBjN10jFxER8WWqyEVExLz8oCRXIhcREdPy1GI3b1JrXURExIepIhcREdOqgxup1TtK5CIiYlp+kMfVWhcREfFlqshFRMS8/KAkVyIXERHT0qp1ERERqddUkYuIiGlp1bqIiIgP84M8rta6iIiIL1NFLiIi5uUHJbkSuYiImJZWrYuIiEi9popcRERMS6vWRUREfJgf5HElchERMTE/yOS6Ri4iIuLDVJGLiIhp+cOqdSVyERExLX9Y7KbWuoiIiA9TRS4iIqblBwW5ErmIiJiYH2RytdZFRER8mCpyERExLa1aFxER8WFatS4iIiL1mipyERExLT8oyJXIRUTEvNRaFxERkXpNFbmIiJiY+UtyJXIRETEttdZFRESkXlNFLiIipuWpgnzevHls27aNJk2aMGPGDACKi4tJTk7m+PHjNGvWjEceeQS73Y5hGCxevJjMzEwaNGhAQkICkZGRAKSnp7Nq1SoARowYQVxc3AXnVkUuIiKmZbG453EhcXFxPP3009W2paam0qNHD2bPnk2PHj1ITU0FIDMzk6NHjzJ79mwmTJjAwoULgTOJf+XKlUybNo1p06axcuVKiouLLzi3ErmIiMgl6tq1K3a7vdq2jIwMYmNjAYiNjSUjIwOArVu3MnDgQCwWC506deLkyZMUFBSQlZVFVFQUdrsdu91OVFQUWVlZF5xbrXURETEtb95rvbCwkPDwcADCwsIoLCwEwOl00rRpU9e4iIgInE4nTqeTiIgI13aHw4HT6bzgPErkIiJiXm7M44mJia7n8fHxxMfH1z4MiwVLHS2hVyIXERHTcmfqTEpK+lnjmzRpQkFBAeHh4RQUFBAaGgqcqbTz8vJc4/Lz83E4HDgcDnbv3u3a7nQ66dq16wXn0TVyERGROhATE8P69esBWL9+PX369HFt37BhA4ZhsGfPHkJCQggPDyc6Oprs7GyKi4spLi4mOzub6OjoC85jMQzDqMsTuRSlFd6OwL8E2aCs0ttR+J9NOXkXHiRu16dNEzIOFno7DL8yuHPTCw9ys2NF5W45TvPGgTXunzVrFrt376aoqIgmTZowcuRI+vTpQ3JyMnl5eWd9/WzRokVkZ2cTFBREQkIC7du3ByAtLY3Vq1cDZ75+dv31118wNiVycVEi9w4lcu9QIvc8byTy40XuSSTNGtffK9FqrYuIiPiw+vsnhoiIyKXyg3utK5GLiIhp+UEeV2tdRETEl6kiFxER0/KHnzFVIhcREdPy5i1aPUWtdRERER+milxEREzLH1rrqshFRER8mBK5iIiID1NrXURETMsfWutK5CIiYlpatS4iIiL1mipyERExLbXWRUREfJgf5HElchERMTE/yOS6Ri4iIuLDVJGLiIhp+cOqdSVyERExLX9Y7KbWuoiIiA9TRS4iIqblBwW5ErmIiJiYH2RytdZFRER8mCpyERExLa1aFxER8WH+sGrdYhiG4e0gRERE5OLoGrm4JCYmejsEEY/Rv3cxCyVyERERH6ZELiIi4sOUyMUlPj7e2yGIeIz+vYtZaLGbiIiID1NFLiIi4sOUyEVERHyYErmIiIgPUyL3c0eOHGHPnj1UVFRQVVXl7XBE6pz+nYvZaLGbH/vyyy/585//jMPhwOFwEBkZSVxcHCEhId4OTcTtjhw5wmWXXQacSeZWq+oYMQf9S/ZTFRUVbN68mQceeIBnn32WmJgY8vPz+fDDDzl16pS3wxNxq6+//ponnniCN954AwCr1arKXExDidyPlZSUcPToUQCuvvpqevfuTWVlJRs3bkSNGjGL0tJSPv/8c8aOHUtAQACzZ88GlMzFPJTI/VRAQADDhg3jyy+/5JtvvsFqtdK5c2fatm3Lt99+6+3wRNwmODiYBx98kAEDBnD33XdTXl5eLZmL+Dr9K/ZjnTt3pmfPnmzYsIHdu3djtVoZMGAABQUFHDx40NvhibiNw+EgODiY0NBQJkyYQFlZmSuZ79+/n3//+99ejlDk4un3yP1YUFAQAwYMACA1NZUjR44QEBBAYWEhYWFh3g1OpI40btyYCRMmsHTpUqZMmUJVVRXPPfect8MSuWhatS5UVFTw7bffsnbtWgIDA7nhhhto166dt8MSqVOffPIJH374IVOnTuWKK67wdjgiF02JXFx+XPij64ZidsXFxSQnJ/Pb3/6WNm3aeDsckUuiRC4ifqmsrIygoCBvhyFyyZTIRUREfJh6qCIiIj5MiVxERMSHKZGLiIj4MCVyERERH6ZELvIzzJ07l+XLlwPwzTff8PDDD3tk3pEjR7rui//fnn/+edatW1er40ycOJHt27dfVAyX8l4RqTu6s5uYzsSJEzlx4gRWq5Xg4GCio6MZP348wcHBbp2nS5curl/Tqkl6ejrr1q3jxRdfdOv8IiKgilxM6sknn+Tdd9/l1VdfZf/+/fzlL385a0xlZaUXIhMRcS9V5GJqDoeD6OhoDh06BJxpUY8bN441a9ZQWVnJ3Llz+frrr1m+fDnHjx+nVatW3Hfffa67fR04cIAFCxaQm5tLr169sFgsrmPv2rWLOXPmsGDBAgDy8vJYsmQJ33zzDYZhcO211zJkyBDeeustKioquPvuu7HZbCxZsoTy8nL+/Oc/889//pOKigr69OnD2LFjXTco+eijj/jkk0+wWCzcfvvttT7fo0eP8uabb3Lw4EEsFgs9e/Zk/PjxNGrUyDVm3759LF68mBMnTtCnTx/uvfde17w1fRYiUj+pIhdTy8vLIzMzk7Zt27q2ZWRkMG3aNJKTkzlw4ADz589nwoQJvP3228THx/Paa69RXl5ORUUF06dP57rrruPtt9+mX79+fPnll+ecp6qqildffZWmTZsyd+5cFixYwLXXXutKhp06deLdd99lyZIlACxbtozc3FymT5/O7NmzcTqdrFy5EoCsrCw+/vhjfv/73/PGG2+wY8eOn3XON998M2+++SbJycnk5+ezYsWKavs3btzIM888w5w5c8jNzWXVqlUANX4WIlJ/KZGLKU2fPp2xY8fy7LPP0rVrV0aMGOHad/PNN2O32wkKCmLt2rXEx8fTsWNHrFYrcXFxBAQEsHfvXvbs2UNlZSVDhw4lICCAvn370r59+3POl5OTg9Pp5O677yY4OJigoCA6d+58zrGGYbBu3TrGjBmD3W6nYcOGjBgxgk2bNgGwefNm4uLiuOKKKwgODua2226r9Xm3bNmSqKgoAgMDCQ0NZejQoezevbvamCFDhtC0aVPsdjs333yza96aPgsRqb/UWhdTevzxx4mKijrnvoiICNfzvLw81q9fz+eff+7aVlFRgdPpxGKx4HA4qrXTmzZtes5j5uXl0axZM2w22wVj++GHHzh9+jSJiYmubYZhuH60pqCggMjISNe+Zs2aXfCYPzpx4oSrvV9aWkpVVRV2u73amJ+eQ7NmzXA6na5zON9nISL1lxK5+J2fJuaIiAhGjBhRrWL/0e7du3E6nRiG4XpPfn4+LVu2PGts06ZNycvLo7Ky8oLJvHHjxgQFBTFz5kwcDsdZ+8PDw8nPz3e9zsvLq/W5/fnPfwZgxowZ2O12vvrqK95+++1qY356vLy8PFcMNX0WIlJ/qbUufm3w4MH87W9/Y+/evRiGQWlpKdu2baOkpIROnTphtVr57LPPqKio4MsvvyQnJ+ecx+nQoQPh4eEsW7aM0tJSysrK+PbbbwEICwvD6XRSUVEBnPmZ2MGDB7NkyRIKCwsBcDqdZGVlAdCvXz/S09M5fPgwp0+fPusad01KSkoIDg4mJCQEp9PJxx9/fNaYv/71r+Tn51NcXMyqVavo16/fBT8LEam/VJGLX2vfvj33338/b7/9Nrm5ua5r2126dCEgIIDf/e53vPnmmyxfvpxevXpx9dVXn/M4VquVJ598krfffpuEhAQsFgvXXnstnTt3pnv37q5Fb1arlUWLFnHXXXexcuVKnnnmGYqKinA4HPzyl78kOjqaXr16MXToUF544QWsViu33347GzdurNX53HbbbaSkpDBmzBhatmzJwIED+fTTT6uNGTBgAC+99BIFBQXExMRwyy23XPCzEJH6Sz9jKiIi4sPUWhcREfFhSuQiIiI+TIlcRETEhymRi4iI+DAlchERER+mRC4iIuLDlMhFRER8mBK5iIiID/tf5RTJTUX9XgwAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "print('Confusion Matix for ALL Classes')\n",
    "\n",
    "TP = TP / NumClasses\n",
    "TN = TN / NumClasses\n",
    "FP = FP / NumClasses\n",
    "FN = FN / NumClasses\n",
    "\n",
    "\n",
    "cm = np.zeros([2, 2], dtype=np.int32)\n",
    "cm[0,0] = TN\n",
    "cm[0,1] = FP\n",
    "cm[1,0] = FN\n",
    "cm[1,1] = TP\n",
    "\n",
    "plot_confusion_matrix(cm, \n",
    "                      normalize    = False,\n",
    "                      target_names = [0,1],\n",
    "                      title        = \"Confusion Matrix\")\n"
   ]
  }
 ],
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