diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip3 install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('dataset_diabetes/diabetic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['encounter_id', 'patient_nbr', 'race', 'gender', 'age', 'weight',\n",
       "       'admission_type_id', 'discharge_disposition_id', 'admission_source_id',\n",
       "       'time_in_hospital', 'payer_code', 'medical_specialty',\n",
       "       'num_lab_procedures', 'num_procedures', 'num_medications',\n",
       "       'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1',\n",
       "       'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult',\n",
       "       'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',\n",
       "       'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
       "       'tolazamide', 'examide', 'citoglipton', 'insulin',\n",
       "       'glyburide-metformin', 'glipizide-metformin',\n",
       "       'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
       "       'metformin-pioglitazone', 'change', 'diabetesMed', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          [0-10)\n",
       "1         [10-20)\n",
       "2         [20-30)\n",
       "3         [30-40)\n",
       "4         [40-50)\n",
       "           ...   \n",
       "101761    [70-80)\n",
       "101762    [80-90)\n",
       "101763    [70-80)\n",
       "101764    [80-90)\n",
       "101765    [70-80)\n",
       "Name: age, Length: 101766, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161\n",
      "691\n",
      "1657\n",
      "3775\n",
      "9685\n",
      "17256\n",
      "22483\n",
      "26068\n",
      "17197\n",
      "2793\n"
     ]
    }
   ],
   "source": [
    "print(len(data[data['age'] == '[0-10)']))\n",
    "print(len(data[data['age'] == '[10-20)']))\n",
    "print(len(data[data['age'] == '[20-30)']))\n",
    "print(len(data[data['age'] == '[30-40)']))\n",
    "print(len(data[data['age'] == '[40-50)']))\n",
    "print(len(data[data['age'] == '[50-60)']))\n",
    "print(len(data[data['age'] == '[60-70)']))\n",
    "print(len(data[data['age'] == '[70-80)']))\n",
    "print(len(data[data['age'] == '[80-90)']))\n",
    "print(len(data[data['age'] == '[90-100)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "age_bins = ['[0-10)', '[10-20)', '[20-30)', '[30-40)', '[40-50)', '[50-60)', '[60-70)', '[70-80)', '[80-90)', '[90-100)']\n",
    "num_patients = [161,691,1657,3775,9685,17256,22483,26068,17197,2793]\n",
    "ax.bar(age_bins,num_patients)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "encounter_id                101766\n",
       "patient_nbr                  71518\n",
       "race                             6\n",
       "gender                           3\n",
       "age                             10\n",
       "weight                          10\n",
       "admission_type_id                8\n",
       "discharge_disposition_id        26\n",
       "admission_source_id             17\n",
       "time_in_hospital                14\n",
       "payer_code                      18\n",
       "medical_specialty               73\n",
       "num_lab_procedures             118\n",
       "num_procedures                   7\n",
       "num_medications                 75\n",
       "number_outpatient               39\n",
       "number_emergency                33\n",
       "number_inpatient                21\n",
       "diag_1                         717\n",
       "diag_2                         749\n",
       "diag_3                         790\n",
       "number_diagnoses                16\n",
       "max_glu_serum                    4\n",
       "A1Cresult                        4\n",
       "metformin                        4\n",
       "repaglinide                      4\n",
       "nateglinide                      4\n",
       "chlorpropamide                   4\n",
       "glimepiride                      4\n",
       "acetohexamide                    2\n",
       "glipizide                        4\n",
       "glyburide                        4\n",
       "tolbutamide                      2\n",
       "pioglitazone                     4\n",
       "rosiglitazone                    4\n",
       "acarbose                         4\n",
       "miglitol                         4\n",
       "troglitazone                     2\n",
       "tolazamide                       3\n",
       "examide                          1\n",
       "citoglipton                      1\n",
       "insulin                          4\n",
       "glyburide-metformin              4\n",
       "glipizide-metformin              2\n",
       "glimepiride-pioglitazone         2\n",
       "metformin-rosiglitazone          2\n",
       "metformin-pioglitazone           2\n",
       "change                           2\n",
       "diabetesMed                      2\n",
       "readmitted                       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 50 columns):\n",
      " #   Column                    Non-Null Count   Dtype \n",
      "---  ------                    --------------   ----- \n",
      " 0   encounter_id              101766 non-null  int64 \n",
      " 1   patient_nbr               101766 non-null  int64 \n",
      " 2   race                      101766 non-null  object\n",
      " 3   gender                    101766 non-null  object\n",
      " 4   age                       101766 non-null  object\n",
      " 5   weight                    101766 non-null  object\n",
      " 6   admission_type_id         101766 non-null  int64 \n",
      " 7   discharge_disposition_id  101766 non-null  int64 \n",
      " 8   admission_source_id       101766 non-null  int64 \n",
      " 9   time_in_hospital          101766 non-null  int64 \n",
      " 10  payer_code                101766 non-null  object\n",
      " 11  medical_specialty         101766 non-null  object\n",
      " 12  num_lab_procedures        101766 non-null  int64 \n",
      " 13  num_procedures            101766 non-null  int64 \n",
      " 14  num_medications           101766 non-null  int64 \n",
      " 15  number_outpatient         101766 non-null  int64 \n",
      " 16  number_emergency          101766 non-null  int64 \n",
      " 17  number_inpatient          101766 non-null  int64 \n",
      " 18  diag_1                    101766 non-null  object\n",
      " 19  diag_2                    101766 non-null  object\n",
      " 20  diag_3                    101766 non-null  object\n",
      " 21  number_diagnoses          101766 non-null  int64 \n",
      " 22  max_glu_serum             101766 non-null  object\n",
      " 23  A1Cresult                 101766 non-null  object\n",
      " 24  metformin                 101766 non-null  object\n",
      " 25  repaglinide               101766 non-null  object\n",
      " 26  nateglinide               101766 non-null  object\n",
      " 27  chlorpropamide            101766 non-null  object\n",
      " 28  glimepiride               101766 non-null  object\n",
      " 29  acetohexamide             101766 non-null  object\n",
      " 30  glipizide                 101766 non-null  object\n",
      " 31  glyburide                 101766 non-null  object\n",
      " 32  tolbutamide               101766 non-null  object\n",
      " 33  pioglitazone              101766 non-null  object\n",
      " 34  rosiglitazone             101766 non-null  object\n",
      " 35  acarbose                  101766 non-null  object\n",
      " 36  miglitol                  101766 non-null  object\n",
      " 37  troglitazone              101766 non-null  object\n",
      " 38  tolazamide                101766 non-null  object\n",
      " 39  examide                   101766 non-null  object\n",
      " 40  citoglipton               101766 non-null  object\n",
      " 41  insulin                   101766 non-null  object\n",
      " 42  glyburide-metformin       101766 non-null  object\n",
      " 43  glipizide-metformin       101766 non-null  object\n",
      " 44  glimepiride-pioglitazone  101766 non-null  object\n",
      " 45  metformin-rosiglitazone   101766 non-null  object\n",
      " 46  metformin-pioglitazone    101766 non-null  object\n",
      " 47  change                    101766 non-null  object\n",
      " 48  diabetesMed               101766 non-null  object\n",
      " 49  readmitted                101766 non-null  object\n",
      "dtypes: int64(13), object(37)\n",
      "memory usage: 38.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries: 101766\n",
      "Number of features: 50\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of entries: \" + str(len(data)))\n",
    "print(\"Number of features: \" + str(len(data.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>796152</td>\n",
       "      <td>23043240</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[50-60)</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1070256</td>\n",
       "      <td>23043240</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[50-60)</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12923</th>\n",
       "      <td>52009764</td>\n",
       "      <td>23043240</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[50-60)</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       encounter_id  patient_nbr       race  gender      age weight  \\\n",
       "68           796152     23043240  Caucasian  Female  [50-60)      ?   \n",
       "79          1070256     23043240  Caucasian  Female  [50-60)      ?   \n",
       "12923      52009764     23043240  Caucasian  Female  [50-60)      ?   \n",
       "\n",
       "       admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "68                     2                         1                    4   \n",
       "79                     2                         1                    4   \n",
       "12923                  2                         1                    4   \n",
       "\n",
       "       time_in_hospital  ... citoglipton insulin  glyburide-metformin  \\\n",
       "68                    2  ...          No  Steady                   No   \n",
       "79                    3  ...          No  Steady                   No   \n",
       "12923                 4  ...          No  Steady                   No   \n",
       "\n",
       "       glipizide-metformin  glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "68                      No                        No                       No   \n",
       "79                      No                        No                       No   \n",
       "12923                   No                        No                       No   \n",
       "\n",
       "       metformin-pioglitazone  change diabetesMed readmitted  \n",
       "68                         No      No         Yes        <30  \n",
       "79                         No      No         Yes        >30  \n",
       "12923                      No      No         Yes         NO  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Check to see duplicate rows for a patient number\"\"\"\n",
    "data[data['patient_nbr'] == 23043240]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Make copy of original data set\"\"\"\n",
    "orig = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Dedupe for patient encounters, selecting for encounter with maximum time_in_hospital\"\"\"\n",
    "data = data.sort_values('time_in_hospital', ascending=False).drop_duplicates(subset=['patient_nbr'], keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Remove unnecessary features\"\"\"\n",
    "data = data.drop(['encounter_id'], axis=1) # remove encounter ID, too unique and non-informative\n",
    "data = data.drop(['weight', 'payer_code', 'medical_specialty', 'race'], axis=1) # these features have over 30% missing values\n",
    "data = data.drop(['metformin-pioglitazone', 'glimepiride-pioglitazone', 'citoglipton', 'examide'], axis=1) # these all only have 1 unique value, 'No'\n",
    "# optional: remove near zero variance features?\n",
    "print(len(data.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe the distribution of missing features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip3 install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip3 install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "# https://towardsdatascience.com/visualizing-missing-values-in-python-is-shockingly-easy-56ed5bc2e7ea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>patient_nbr</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28819</th>\n",
       "      <td>5481009</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-70)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19368</th>\n",
       "      <td>72802098</td>\n",
       "      <td>Female</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74178</th>\n",
       "      <td>104625684</td>\n",
       "      <td>Male</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38988</th>\n",
       "      <td>23507469</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>43927515</td>\n",
       "      <td>Male</td>\n",
       "      <td>[80-90)</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33525</th>\n",
       "      <td>90625806</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-70)</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60165</th>\n",
       "      <td>67403385</td>\n",
       "      <td>Female</td>\n",
       "      <td>[50-60)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33520</th>\n",
       "      <td>49085757</td>\n",
       "      <td>Female</td>\n",
       "      <td>[50-60)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33510</th>\n",
       "      <td>18748791</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-70)</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8222157</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71518 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_nbr  gender      age  admission_type_id  \\\n",
       "28819      5481009    Male  [60-70)                  2   \n",
       "19368     72802098  Female  [80-90)                  1   \n",
       "74178    104625684    Male  [80-90)                  2   \n",
       "38988     23507469  Female  [20-30)                  1   \n",
       "24998     43927515    Male  [80-90)                  6   \n",
       "...            ...     ...      ...                ...   \n",
       "33525     90625806    Male  [60-70)                  3   \n",
       "60165     67403385  Female  [50-60)                  1   \n",
       "33520     49085757  Female  [50-60)                  1   \n",
       "33510     18748791    Male  [60-70)                  2   \n",
       "0          8222157  Female   [0-10)                  6   \n",
       "\n",
       "       discharge_disposition_id  admission_source_id  time_in_hospital  \\\n",
       "28819                         1                    1                14   \n",
       "19368                         3                    7                14   \n",
       "74178                         6                    1                14   \n",
       "38988                         3                    5                14   \n",
       "24998                        22                   17                14   \n",
       "...                         ...                  ...               ...   \n",
       "33525                         1                    1                 1   \n",
       "60165                         1                    7                 1   \n",
       "33520                         1                    1                 1   \n",
       "33510                         1                    1                 1   \n",
       "0                            25                    1                 1   \n",
       "\n",
       "       num_lab_procedures  num_procedures  num_medications  ...  citoglipton  \\\n",
       "28819                  59               5               28  ...           No   \n",
       "19368                  67               0               19  ...           No   \n",
       "74178                  61               4               16  ...           No   \n",
       "38988                  45               2               37  ...           No   \n",
       "24998                  68               0               23  ...           No   \n",
       "...                   ...             ...              ...  ...          ...   \n",
       "33525                  32               1               29  ...           No   \n",
       "60165                  25               0                8  ...           No   \n",
       "33520                  48               1                8  ...           No   \n",
       "33510                  47               5                5  ...           No   \n",
       "0                      41               0                1  ...           No   \n",
       "\n",
       "       insulin  glyburide-metformin glipizide-metformin  \\\n",
       "28819   Steady                   No                  No   \n",
       "19368     Down                   No                  No   \n",
       "74178       No                   No                  No   \n",
       "38988     Down                   No                  No   \n",
       "24998     Down                   No                  No   \n",
       "...        ...                  ...                 ...   \n",
       "33525       Up                   No                  No   \n",
       "60165   Steady                   No                  No   \n",
       "33520       No                   No                  No   \n",
       "33510       No                   No                  No   \n",
       "0           No                   No                  No   \n",
       "\n",
       "      glimepiride-pioglitazone metformin-rosiglitazone  \\\n",
       "28819                       No                      No   \n",
       "19368                       No                      No   \n",
       "74178                       No                      No   \n",
       "38988                       No                      No   \n",
       "24998                       No                      No   \n",
       "...                        ...                     ...   \n",
       "33525                       No                      No   \n",
       "60165                       No                      No   \n",
       "33520                       No                      No   \n",
       "33510                       No                      No   \n",
       "0                           No                      No   \n",
       "\n",
       "       metformin-pioglitazone change diabetesMed readmitted  \n",
       "28819                      No     Ch         Yes        >30  \n",
       "19368                      No     Ch         Yes         NO  \n",
       "74178                      No     No          No         NO  \n",
       "38988                      No     Ch         Yes         NO  \n",
       "24998                      No     Ch         Yes        >30  \n",
       "...                       ...    ...         ...        ...  \n",
       "33525                      No     Ch         Yes        >30  \n",
       "60165                      No     No         Yes        >30  \n",
       "33520                      No     No         Yes         NO  \n",
       "33510                      No     No          No         NO  \n",
       "0                          No     No          No         NO  \n",
       "\n",
       "[71518 rows x 45 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace('?', pd.NA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['>30', 'NO', '<30'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.readmitted.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Define functions for feature transformations\"\"\"\n",
    "\n",
    "# converts label to binary, considered readmitted if patient was readmitted within 30 days\n",
    "def label_convert_to_binary(label):\n",
    "    if label == '<30' or label == '>30':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# 700 levels, convert to 1 of 9 categories\n",
    "def diag_convert(label):\n",
    "    try:\n",
    "        label = float(label)\n",
    "    except: # to address error could not convert string to float: 'V57'\n",
    "        return \"Other\"\n",
    "    if label == 785 or (label >= 390 and label <= 459):\n",
    "        return \"Circulatory\"\n",
    "    elif label == 786 or (label >= 460 and label <= 519):\n",
    "        return \"Respiratory\"\n",
    "    elif label == 787 or (label >= 520 and label <= 579):\n",
    "        return \"Digestive\"\n",
    "    elif str(label).startswith('250.'):\n",
    "        return \"Diabetes\"\n",
    "    elif label >= 800 and label <= 999:\n",
    "        return \"Injury\"\n",
    "    elif label >= 710 and label <= 739:\n",
    "        return \"Musculoskeletal\"\n",
    "    elif label == 788 or (label >= 580 and label <= 629):\n",
    "        return \"Genitourinary\"\n",
    "    elif (label >= 1 and label <= 239) or label == 780 or label == 781 or label == 784 or (label >= 790 and label <= 799) or (label >= 240 and label <= 279 and label != 250) or (label >= 680 and label <= 709) or label == 782 or (label >= 290 and label <= 319):\n",
    "        return \"Neoplasms\"\n",
    "    else:\n",
    "        return \"Other\"\n",
    "\n",
    "# bin age, going from 10 levels to 3\n",
    "def age_convert(label):\n",
    "    young_level = {'[0-10)', '[10-20)', '[20-30)', '[30-40)', '[40-50)'}\n",
    "    mid_level = {'[50-60)', '[60-70)', '[70-80)'}\n",
    "    old_level = {'[80-90)', '[90-100)'}\n",
    "    if label in young_level:\n",
    "        return 1\n",
    "    elif label in mid_level:\n",
    "        return 1.5\n",
    "    elif label in old_level:\n",
    "        return 2\n",
    "\n",
    "# change from 8 levels to 2\n",
    "# k = known, u = unknown\n",
    "def admission_type_convert(label):\n",
    "    known_level = {1,2,3,4,7}\n",
    "    if label in known_level:\n",
    "        return 'k'\n",
    "    else:\n",
    "        return 'u'\n",
    "\n",
    "# go from 8 levels to 5\n",
    "# r = referral, t = transfer, u = unknown, b = birth, o = other\n",
    "def admission_source_convert(label):\n",
    "    referral_level = {1,2,3}\n",
    "    transfer_level = {4,5,6,10,18,22,25,26}\n",
    "    unknown_level = {20,17,9}\n",
    "    birth_level = {13,14,11}\n",
    "    other_level = {8,7}\n",
    "    if label in referral_level:\n",
    "        return 'r'\n",
    "    elif label in transfer_level:\n",
    "        return 't'\n",
    "    elif label in unknown_level:\n",
    "        return 'u'\n",
    "    elif label in birth_level:\n",
    "        return 'b'\n",
    "    elif label in other_level:\n",
    "        return 'o'\n",
    "\n",
    "# go from 25 levels to 6\n",
    "# d = discharge, h = hospice, e = expired, u = unknown, o = other\n",
    "def disposition_convert(label):\n",
    "    discharge_level = {1,2,3,4,5,6,8,10,15,16,17,22,23,24,30,27,28,29}\n",
    "    hospice_level = {13,14}\n",
    "    expired_level = {11,19,20,21}\n",
    "    unknown_level = {18,25,26}\n",
    "    other_level = {7,9,12}\n",
    "    if label in discharge_level:\n",
    "        return 'd'\n",
    "    elif label in hospice_level:\n",
    "        return 'h'\n",
    "    elif label in unknown_level:\n",
    "        return 'u'\n",
    "    elif label in expired_level:\n",
    "        return 'e'\n",
    "    elif label in other_level:\n",
    "        return 'o'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Perform transformations now\"\"\"\n",
    "data['label'] = data['readmitted'].apply(label_convert_to_binary) # convert readmitted to binary classification label\n",
    "data['age'] = data['age'].apply(age_convert) # convert age to numeric bins: 1, 1.5, 2\n",
    "data['diag_1'] = data['diag_1'].apply(diag_convert)\n",
    "data['diag_2'] = data['diag_2'].apply(diag_convert)\n",
    "data['diag_3'] = data['diag_3'].apply(diag_convert)\n",
    "data['admission_source_id'] = data['admission_source_id'].apply(admission_source_convert)\n",
    "data['admission_type_id'] = data['admission_type_id'].apply(admission_type_convert)\n",
    "data['discharge_disposition_id'] = data['discharge_disposition_id'].apply(disposition_convert)\n",
    "data = data.drop(['readmitted'], axis=1) # we have the updated binary label, no need for duplicate label column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Drop rows where discharge disposition id is expired, as that doesn't help with readmission\"\"\"\n",
    "indexNames = data[data['discharge_disposition_id'] == 'e'].index\n",
    "data.drop(indexNames, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>patient_nbr</th>\n",
       "      <th>age</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28448</th>\n",
       "      <td>5481009</td>\n",
       "      <td>[60-70)</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>250.6</td>\n",
       "      <td>682</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28819</th>\n",
       "      <td>5481009</td>\n",
       "      <td>[60-70)</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250.8</td>\n",
       "      <td>707</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33795</th>\n",
       "      <td>5481009</td>\n",
       "      <td>[60-70)</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>724</td>\n",
       "      <td>250</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_nbr      age  time_in_hospital  admission_type_id  \\\n",
       "28448      5481009  [60-70)                 7                  1   \n",
       "28819      5481009  [60-70)                14                  2   \n",
       "33795      5481009  [60-70)                 4                  2   \n",
       "\n",
       "       discharge_disposition_id  admission_source_id diag_1 diag_2 diag_3  \n",
       "28448                         1                    6  250.6    682    682  \n",
       "28819                         1                    1  250.8    707    682  \n",
       "33795                         3                    1    724    250    401  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient_data_orig = orig[orig['patient_nbr'] == 5481009]\n",
    "patient_data_orig[['patient_nbr', 'age', 'time_in_hospital', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', 'diag_1', 'diag_2', 'diag_3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>patient_nbr</th>\n",
       "      <th>age</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28819</th>\n",
       "      <td>5481009</td>\n",
       "      <td>1.5</td>\n",
       "      <td>14</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_nbr  age  time_in_hospital admission_type_id  \\\n",
       "28819      5481009  1.5                14                 k   \n",
       "\n",
       "      discharge_disposition_id admission_source_id    diag_1     diag_2  \\\n",
       "28819                        d                   r  Diabetes  Neoplasms   \n",
       "\n",
       "          diag_3  label  \n",
       "28819  Neoplasms      1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to validate transformations made\n",
    "patient_data = data[data['patient_nbr'] == 5481009]\n",
    "patient_data[['patient_nbr', 'age', 'time_in_hospital', 'admission_type_id', 'discharge_disposition_id', 'admission_source_id', 'diag_1', 'diag_2', 'diag_3', 'label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Remove patient nbr, was used for debugging purposes but now unnecessary\"\"\"\n",
    "data = data.drop(['patient_nbr'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Drop gender == unknown, as that seems to show a massive error bar\"\"\"\n",
    "indexNames = data[data['gender'] == 'Unknown/Invalid'].index\n",
    "data.drop(indexNames, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 70213 entries, 28819 to 0\n",
      "Data columns (total 44 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   gender                    70213 non-null  object \n",
      " 1   age                       70213 non-null  float64\n",
      " 2   admission_type_id         70213 non-null  object \n",
      " 3   discharge_disposition_id  70213 non-null  object \n",
      " 4   admission_source_id       70213 non-null  object \n",
      " 5   time_in_hospital          70213 non-null  int64  \n",
      " 6   num_lab_procedures        70213 non-null  int64  \n",
      " 7   num_procedures            70213 non-null  int64  \n",
      " 8   num_medications           70213 non-null  int64  \n",
      " 9   number_outpatient         70213 non-null  int64  \n",
      " 10  number_emergency          70213 non-null  int64  \n",
      " 11  number_inpatient          70213 non-null  int64  \n",
      " 12  diag_1                    70213 non-null  object \n",
      " 13  diag_2                    70213 non-null  object \n",
      " 14  diag_3                    70213 non-null  object \n",
      " 15  number_diagnoses          70213 non-null  int64  \n",
      " 16  max_glu_serum             70213 non-null  object \n",
      " 17  A1Cresult                 70213 non-null  object \n",
      " 18  metformin                 70213 non-null  object \n",
      " 19  repaglinide               70213 non-null  object \n",
      " 20  nateglinide               70213 non-null  object \n",
      " 21  chlorpropamide            70213 non-null  object \n",
      " 22  glimepiride               70213 non-null  object \n",
      " 23  acetohexamide             70213 non-null  object \n",
      " 24  glipizide                 70213 non-null  object \n",
      " 25  glyburide                 70213 non-null  object \n",
      " 26  tolbutamide               70213 non-null  object \n",
      " 27  pioglitazone              70213 non-null  object \n",
      " 28  rosiglitazone             70213 non-null  object \n",
      " 29  acarbose                  70213 non-null  object \n",
      " 30  miglitol                  70213 non-null  object \n",
      " 31  troglitazone              70213 non-null  object \n",
      " 32  tolazamide                70213 non-null  object \n",
      " 33  examide                   70213 non-null  object \n",
      " 34  citoglipton               70213 non-null  object \n",
      " 35  insulin                   70213 non-null  object \n",
      " 36  glyburide-metformin       70213 non-null  object \n",
      " 37  glipizide-metformin       70213 non-null  object \n",
      " 38  glimepiride-pioglitazone  70213 non-null  object \n",
      " 39  metformin-rosiglitazone   70213 non-null  object \n",
      " 40  metformin-pioglitazone    70213 non-null  object \n",
      " 41  change                    70213 non-null  object \n",
      " 42  diabetesMed               70213 non-null  object \n",
      " 43  label                     70213 non-null  int64  \n",
      "dtypes: float64(1), int64(9), object(34)\n",
      "memory usage: 24.1+ MB\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Handle missing values...again\"\"\"\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create cohorts based on age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.5, 2. , 2. , ..., 1.5, 1.5, 1. ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "young_cohort = data[data['age'] == 1]\n",
    "mid_cohort = data[data['age'] == 1.5]\n",
    "old_cohort = data[data['age'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11227\n",
      "45783\n",
      "13203\n"
     ]
    }
   ],
   "source": [
    "print(len(young_cohort))\n",
    "print(len(mid_cohort))\n",
    "print(len(old_cohort))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_nbr', 'gender', 'age', 'admission_type_id',\n",
       "       'discharge_disposition_id', 'admission_source_id', 'time_in_hospital',\n",
       "       'num_lab_procedures', 'num_procedures', 'num_medications',\n",
       "       'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1',\n",
       "       'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult',\n",
       "       'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',\n",
       "       'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',\n",
       "       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',\n",
       "       'tolazamide', 'insulin', 'glyburide-metformin', 'glipizide-metformin',\n",
       "       'metformin-rosiglitazone', 'change', 'diabetesMed', 'readmitted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metformin-repaglinide': 972,\n",
       " 'metformin-nateglinide': 503,\n",
       " 'metformin-chlorpropamide': 66,\n",
       " 'metformin-glimepiride': 3768,\n",
       " 'metformin-acetohexamide': 1,\n",
       " 'metformin-glipizide': 9085,\n",
       " 'metformin-glyburide': 7761,\n",
       " 'metformin-tolbutamide': 20,\n",
       " 'metformin-pioglitazone': 5315,\n",
       " 'metformin-rosiglitazone': 4581,\n",
       " 'metformin-acarbose': 191,\n",
       " 'metformin-miglitol': 18,\n",
       " 'metformin-troglitazone': 3,\n",
       " 'metformin-tolazamide': 30,\n",
       " 'metformin-insulin': 37644,\n",
       " 'repaglinide-metformin': 14907,\n",
       " 'repaglinide-nateglinide': 503,\n",
       " 'repaglinide-chlorpropamide': 66,\n",
       " 'repaglinide-glimepiride': 3768,\n",
       " 'repaglinide-acetohexamide': 1,\n",
       " 'repaglinide-glipizide': 9085,\n",
       " 'repaglinide-glyburide': 7761,\n",
       " 'repaglinide-tolbutamide': 20,\n",
       " 'repaglinide-pioglitazone': 5315,\n",
       " 'repaglinide-rosiglitazone': 4581,\n",
       " 'repaglinide-acarbose': 191,\n",
       " 'repaglinide-miglitol': 18,\n",
       " 'repaglinide-troglitazone': 3,\n",
       " 'repaglinide-tolazamide': 30,\n",
       " 'repaglinide-insulin': 37644,\n",
       " 'nateglinide-metformin': 14907,\n",
       " 'nateglinide-repaglinide': 972,\n",
       " 'nateglinide-chlorpropamide': 66,\n",
       " 'nateglinide-glimepiride': 3768,\n",
       " 'nateglinide-acetohexamide': 1,\n",
       " 'nateglinide-glipizide': 9085,\n",
       " 'nateglinide-glyburide': 7761,\n",
       " 'nateglinide-tolbutamide': 20,\n",
       " 'nateglinide-pioglitazone': 5315,\n",
       " 'nateglinide-rosiglitazone': 4581,\n",
       " 'nateglinide-acarbose': 191,\n",
       " 'nateglinide-miglitol': 18,\n",
       " 'nateglinide-troglitazone': 3,\n",
       " 'nateglinide-tolazamide': 30,\n",
       " 'nateglinide-insulin': 37644,\n",
       " 'chlorpropamide-metformin': 14907,\n",
       " 'chlorpropamide-repaglinide': 972,\n",
       " 'chlorpropamide-nateglinide': 503,\n",
       " 'chlorpropamide-glimepiride': 3768,\n",
       " 'chlorpropamide-acetohexamide': 1,\n",
       " 'chlorpropamide-glipizide': 9085,\n",
       " 'chlorpropamide-glyburide': 7761,\n",
       " 'chlorpropamide-tolbutamide': 20,\n",
       " 'chlorpropamide-pioglitazone': 5315,\n",
       " 'chlorpropamide-rosiglitazone': 4581,\n",
       " 'chlorpropamide-acarbose': 191,\n",
       " 'chlorpropamide-miglitol': 18,\n",
       " 'chlorpropamide-troglitazone': 3,\n",
       " 'chlorpropamide-tolazamide': 30,\n",
       " 'chlorpropamide-insulin': 37644,\n",
       " 'glimepiride-metformin': 14907,\n",
       " 'glimepiride-repaglinide': 972,\n",
       " 'glimepiride-nateglinide': 503,\n",
       " 'glimepiride-chlorpropamide': 66,\n",
       " 'glimepiride-acetohexamide': 1,\n",
       " 'glimepiride-glipizide': 9085,\n",
       " 'glimepiride-glyburide': 7761,\n",
       " 'glimepiride-tolbutamide': 20,\n",
       " 'glimepiride-pioglitazone': 5315,\n",
       " 'glimepiride-rosiglitazone': 4581,\n",
       " 'glimepiride-acarbose': 191,\n",
       " 'glimepiride-miglitol': 18,\n",
       " 'glimepiride-troglitazone': 3,\n",
       " 'glimepiride-tolazamide': 30,\n",
       " 'glimepiride-insulin': 37644,\n",
       " 'acetohexamide-metformin': 14907,\n",
       " 'acetohexamide-repaglinide': 972,\n",
       " 'acetohexamide-nateglinide': 503,\n",
       " 'acetohexamide-chlorpropamide': 66,\n",
       " 'acetohexamide-glimepiride': 3768,\n",
       " 'acetohexamide-glipizide': 9085,\n",
       " 'acetohexamide-glyburide': 7761,\n",
       " 'acetohexamide-tolbutamide': 20,\n",
       " 'acetohexamide-pioglitazone': 5315,\n",
       " 'acetohexamide-rosiglitazone': 4581,\n",
       " 'acetohexamide-acarbose': 191,\n",
       " 'acetohexamide-miglitol': 18,\n",
       " 'acetohexamide-troglitazone': 3,\n",
       " 'acetohexamide-tolazamide': 30,\n",
       " 'acetohexamide-insulin': 37644,\n",
       " 'glipizide-metformin': 14907,\n",
       " 'glipizide-repaglinide': 972,\n",
       " 'glipizide-nateglinide': 503,\n",
       " 'glipizide-chlorpropamide': 66,\n",
       " 'glipizide-glimepiride': 3768,\n",
       " 'glipizide-acetohexamide': 1,\n",
       " 'glipizide-glyburide': 7761,\n",
       " 'glipizide-tolbutamide': 20,\n",
       " 'glipizide-pioglitazone': 5315,\n",
       " 'glipizide-rosiglitazone': 4581,\n",
       " 'glipizide-acarbose': 191,\n",
       " 'glipizide-miglitol': 18,\n",
       " 'glipizide-troglitazone': 3,\n",
       " 'glipizide-tolazamide': 30,\n",
       " 'glipizide-insulin': 37644,\n",
       " 'glyburide-metformin': 14907,\n",
       " 'glyburide-repaglinide': 972,\n",
       " 'glyburide-nateglinide': 503,\n",
       " 'glyburide-chlorpropamide': 66,\n",
       " 'glyburide-glimepiride': 3768,\n",
       " 'glyburide-acetohexamide': 1,\n",
       " 'glyburide-glipizide': 9085,\n",
       " 'glyburide-tolbutamide': 20,\n",
       " 'glyburide-pioglitazone': 5315,\n",
       " 'glyburide-rosiglitazone': 4581,\n",
       " 'glyburide-acarbose': 191,\n",
       " 'glyburide-miglitol': 18,\n",
       " 'glyburide-troglitazone': 3,\n",
       " 'glyburide-tolazamide': 30,\n",
       " 'glyburide-insulin': 37644,\n",
       " 'tolbutamide-metformin': 14907,\n",
       " 'tolbutamide-repaglinide': 972,\n",
       " 'tolbutamide-nateglinide': 503,\n",
       " 'tolbutamide-chlorpropamide': 66,\n",
       " 'tolbutamide-glimepiride': 3768,\n",
       " 'tolbutamide-acetohexamide': 1,\n",
       " 'tolbutamide-glipizide': 9085,\n",
       " 'tolbutamide-glyburide': 7761,\n",
       " 'tolbutamide-pioglitazone': 5315,\n",
       " 'tolbutamide-rosiglitazone': 4581,\n",
       " 'tolbutamide-acarbose': 191,\n",
       " 'tolbutamide-miglitol': 18,\n",
       " 'tolbutamide-troglitazone': 3,\n",
       " 'tolbutamide-tolazamide': 30,\n",
       " 'tolbutamide-insulin': 37644,\n",
       " 'pioglitazone-metformin': 14907,\n",
       " 'pioglitazone-repaglinide': 972,\n",
       " 'pioglitazone-nateglinide': 503,\n",
       " 'pioglitazone-chlorpropamide': 66,\n",
       " 'pioglitazone-glimepiride': 3768,\n",
       " 'pioglitazone-acetohexamide': 1,\n",
       " 'pioglitazone-glipizide': 9085,\n",
       " 'pioglitazone-glyburide': 7761,\n",
       " 'pioglitazone-tolbutamide': 20,\n",
       " 'pioglitazone-rosiglitazone': 4581,\n",
       " 'pioglitazone-acarbose': 191,\n",
       " 'pioglitazone-miglitol': 18,\n",
       " 'pioglitazone-troglitazone': 3,\n",
       " 'pioglitazone-tolazamide': 30,\n",
       " 'pioglitazone-insulin': 37644,\n",
       " 'rosiglitazone-metformin': 14907,\n",
       " 'rosiglitazone-repaglinide': 972,\n",
       " 'rosiglitazone-nateglinide': 503,\n",
       " 'rosiglitazone-chlorpropamide': 66,\n",
       " 'rosiglitazone-glimepiride': 3768,\n",
       " 'rosiglitazone-acetohexamide': 1,\n",
       " 'rosiglitazone-glipizide': 9085,\n",
       " 'rosiglitazone-glyburide': 7761,\n",
       " 'rosiglitazone-tolbutamide': 20,\n",
       " 'rosiglitazone-pioglitazone': 5315,\n",
       " 'rosiglitazone-acarbose': 191,\n",
       " 'rosiglitazone-miglitol': 18,\n",
       " 'rosiglitazone-troglitazone': 3,\n",
       " 'rosiglitazone-tolazamide': 30,\n",
       " 'rosiglitazone-insulin': 37644,\n",
       " 'acarbose-metformin': 14907,\n",
       " 'acarbose-repaglinide': 972,\n",
       " 'acarbose-nateglinide': 503,\n",
       " 'acarbose-chlorpropamide': 66,\n",
       " 'acarbose-glimepiride': 3768,\n",
       " 'acarbose-acetohexamide': 1,\n",
       " 'acarbose-glipizide': 9085,\n",
       " 'acarbose-glyburide': 7761,\n",
       " 'acarbose-tolbutamide': 20,\n",
       " 'acarbose-pioglitazone': 5315,\n",
       " 'acarbose-rosiglitazone': 4581,\n",
       " 'acarbose-miglitol': 18,\n",
       " 'acarbose-troglitazone': 3,\n",
       " 'acarbose-tolazamide': 30,\n",
       " 'acarbose-insulin': 37644,\n",
       " 'miglitol-metformin': 14907,\n",
       " 'miglitol-repaglinide': 972,\n",
       " 'miglitol-nateglinide': 503,\n",
       " 'miglitol-chlorpropamide': 66,\n",
       " 'miglitol-glimepiride': 3768,\n",
       " 'miglitol-acetohexamide': 1,\n",
       " 'miglitol-glipizide': 9085,\n",
       " 'miglitol-glyburide': 7761,\n",
       " 'miglitol-tolbutamide': 20,\n",
       " 'miglitol-pioglitazone': 5315,\n",
       " 'miglitol-rosiglitazone': 4581,\n",
       " 'miglitol-acarbose': 191,\n",
       " 'miglitol-troglitazone': 3,\n",
       " 'miglitol-tolazamide': 30,\n",
       " 'miglitol-insulin': 37644,\n",
       " 'troglitazone-metformin': 14907,\n",
       " 'troglitazone-repaglinide': 972,\n",
       " 'troglitazone-nateglinide': 503,\n",
       " 'troglitazone-chlorpropamide': 66,\n",
       " 'troglitazone-glimepiride': 3768,\n",
       " 'troglitazone-acetohexamide': 1,\n",
       " 'troglitazone-glipizide': 9085,\n",
       " 'troglitazone-glyburide': 7761,\n",
       " 'troglitazone-tolbutamide': 20,\n",
       " 'troglitazone-pioglitazone': 5315,\n",
       " 'troglitazone-rosiglitazone': 4581,\n",
       " 'troglitazone-acarbose': 191,\n",
       " 'troglitazone-miglitol': 18,\n",
       " 'troglitazone-tolazamide': 30,\n",
       " 'troglitazone-insulin': 37644,\n",
       " 'tolazamide-metformin': 14907,\n",
       " 'tolazamide-repaglinide': 972,\n",
       " 'tolazamide-nateglinide': 503,\n",
       " 'tolazamide-chlorpropamide': 66,\n",
       " 'tolazamide-glimepiride': 3768,\n",
       " 'tolazamide-acetohexamide': 1,\n",
       " 'tolazamide-glipizide': 9085,\n",
       " 'tolazamide-glyburide': 7761,\n",
       " 'tolazamide-tolbutamide': 20,\n",
       " 'tolazamide-pioglitazone': 5315,\n",
       " 'tolazamide-rosiglitazone': 4581,\n",
       " 'tolazamide-acarbose': 191,\n",
       " 'tolazamide-miglitol': 18,\n",
       " 'tolazamide-troglitazone': 3,\n",
       " 'tolazamide-insulin': 37644,\n",
       " 'insulin-metformin': 14907,\n",
       " 'insulin-repaglinide': 972,\n",
       " 'insulin-nateglinide': 503,\n",
       " 'insulin-chlorpropamide': 66,\n",
       " 'insulin-glimepiride': 3768,\n",
       " 'insulin-acetohexamide': 1,\n",
       " 'insulin-glipizide': 9085,\n",
       " 'insulin-glyburide': 7761,\n",
       " 'insulin-tolbutamide': 20,\n",
       " 'insulin-pioglitazone': 5315,\n",
       " 'insulin-rosiglitazone': 4581,\n",
       " 'insulin-acarbose': 191,\n",
       " 'insulin-miglitol': 18,\n",
       " 'insulin-troglitazone': 3,\n",
       " 'insulin-tolazamide': 30}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Drug combinations as features are: \n",
    "1. glyburide-metformin\n",
    "2. glipizide-metformin\n",
    "3. metformin-rosiglitazone\n",
    "\n",
    "Individual drugs remaining are\n",
    "1. metformin\n",
    "2. repaglinide\n",
    "3. nateglinide\n",
    "4. chlorpropamide\n",
    "5. glimepiride\n",
    "6. acetohexamide\n",
    "7. glipizide\n",
    "8. glyburide\n",
    "9. tolbutamide\n",
    "10. pioglitazone\n",
    "11. rosiglitazone\n",
    "12. acarbose\n",
    "13. miglitol\n",
    "14. troglitazone\n",
    "15. tolazamide\n",
    "16. insulin\n",
    "\"\"\"\n",
    "drugs = ['metformin', 'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone', 'tolazamide', 'insulin']\n",
    "drug_combination_dict = {}\n",
    "for drug in drugs:\n",
    "    other_drugs = drugs[:] # deep copy list\n",
    "    other_drugs.remove(drug)\n",
    "    for other in other_drugs:\n",
    "        intersection_name = drug + \"-\" + other\n",
    "        intersection = data[(data[other] != 'No') & (data[other] != 'No')]\n",
    "        drug_combination_dict[intersection_name] = len(intersection)\n",
    "drug_combination_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('drug_combinations.txt', 'w') as f:\n",
    "    for (k,v) in drug_combination_dict.items():\n",
    "        f.write(k + \": \" + str(v) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('metformin', 'repaglinide', 972)\n",
      "('metformin', 'nateglinide', 503)\n",
      "('metformin', 'chlorpropamide', 66)\n",
      "('metformin', 'glimepiride', 3768)\n",
      "('metformin', 'acetohexamide', 1)\n",
      "('metformin', 'glipizide', 9085)\n",
      "('metformin', 'glyburide', 7761)\n",
      "('metformin', 'tolbutamide', 20)\n",
      "('metformin', 'pioglitazone', 5315)\n",
      "('metformin', 'rosiglitazone', 4581)\n",
      "('metformin', 'acarbose', 191)\n",
      "('metformin', 'miglitol', 18)\n",
      "('metformin', 'troglitazone', 3)\n",
      "('metformin', 'tolazamide', 30)\n",
      "('metformin', 'insulin', 37644)\n",
      "('repaglinide', 'metformin', 14907)\n",
      "('repaglinide', 'nateglinide', 503)\n",
      "('repaglinide', 'chlorpropamide', 66)\n",
      "('repaglinide', 'glimepiride', 3768)\n",
      "('repaglinide', 'acetohexamide', 1)\n",
      "('repaglinide', 'glipizide', 9085)\n",
      "('repaglinide', 'glyburide', 7761)\n",
      "('repaglinide', 'tolbutamide', 20)\n",
      "('repaglinide', 'pioglitazone', 5315)\n",
      "('repaglinide', 'rosiglitazone', 4581)\n",
      "('repaglinide', 'acarbose', 191)\n",
      "('repaglinide', 'miglitol', 18)\n",
      "('repaglinide', 'troglitazone', 3)\n",
      "('repaglinide', 'tolazamide', 30)\n",
      "('repaglinide', 'insulin', 37644)\n",
      "('nateglinide', 'metformin', 14907)\n",
      "('nateglinide', 'repaglinide', 972)\n",
      "('nateglinide', 'chlorpropamide', 66)\n",
      "('nateglinide', 'glimepiride', 3768)\n",
      "('nateglinide', 'acetohexamide', 1)\n",
      "('nateglinide', 'glipizide', 9085)\n",
      "('nateglinide', 'glyburide', 7761)\n",
      "('nateglinide', 'tolbutamide', 20)\n",
      "('nateglinide', 'pioglitazone', 5315)\n",
      "('nateglinide', 'rosiglitazone', 4581)\n",
      "('nateglinide', 'acarbose', 191)\n",
      "('nateglinide', 'miglitol', 18)\n",
      "('nateglinide', 'troglitazone', 3)\n",
      "('nateglinide', 'tolazamide', 30)\n",
      "('nateglinide', 'insulin', 37644)\n",
      "('chlorpropamide', 'metformin', 14907)\n",
      "('chlorpropamide', 'repaglinide', 972)\n",
      "('chlorpropamide', 'nateglinide', 503)\n",
      "('chlorpropamide', 'glimepiride', 3768)\n",
      "('chlorpropamide', 'acetohexamide', 1)\n",
      "('chlorpropamide', 'glipizide', 9085)\n",
      "('chlorpropamide', 'glyburide', 7761)\n",
      "('chlorpropamide', 'tolbutamide', 20)\n",
      "('chlorpropamide', 'pioglitazone', 5315)\n",
      "('chlorpropamide', 'rosiglitazone', 4581)\n",
      "('chlorpropamide', 'acarbose', 191)\n",
      "('chlorpropamide', 'miglitol', 18)\n",
      "('chlorpropamide', 'troglitazone', 3)\n",
      "('chlorpropamide', 'tolazamide', 30)\n",
      "('chlorpropamide', 'insulin', 37644)\n",
      "('glimepiride', 'metformin', 14907)\n",
      "('glimepiride', 'repaglinide', 972)\n",
      "('glimepiride', 'nateglinide', 503)\n",
      "('glimepiride', 'chlorpropamide', 66)\n",
      "('glimepiride', 'acetohexamide', 1)\n",
      "('glimepiride', 'glipizide', 9085)\n",
      "('glimepiride', 'glyburide', 7761)\n",
      "('glimepiride', 'tolbutamide', 20)\n",
      "('glimepiride', 'pioglitazone', 5315)\n",
      "('glimepiride', 'rosiglitazone', 4581)\n",
      "('glimepiride', 'acarbose', 191)\n",
      "('glimepiride', 'miglitol', 18)\n",
      "('glimepiride', 'troglitazone', 3)\n",
      "('glimepiride', 'tolazamide', 30)\n",
      "('glimepiride', 'insulin', 37644)\n",
      "('acetohexamide', 'metformin', 14907)\n",
      "('acetohexamide', 'repaglinide', 972)\n",
      "('acetohexamide', 'nateglinide', 503)\n",
      "('acetohexamide', 'chlorpropamide', 66)\n",
      "('acetohexamide', 'glimepiride', 3768)\n",
      "('acetohexamide', 'glipizide', 9085)\n",
      "('acetohexamide', 'glyburide', 7761)\n",
      "('acetohexamide', 'tolbutamide', 20)\n",
      "('acetohexamide', 'pioglitazone', 5315)\n",
      "('acetohexamide', 'rosiglitazone', 4581)\n",
      "('acetohexamide', 'acarbose', 191)\n",
      "('acetohexamide', 'miglitol', 18)\n",
      "('acetohexamide', 'troglitazone', 3)\n",
      "('acetohexamide', 'tolazamide', 30)\n",
      "('acetohexamide', 'insulin', 37644)\n",
      "('glipizide', 'metformin', 14907)\n",
      "('glipizide', 'repaglinide', 972)\n",
      "('glipizide', 'nateglinide', 503)\n",
      "('glipizide', 'chlorpropamide', 66)\n",
      "('glipizide', 'glimepiride', 3768)\n",
      "('glipizide', 'acetohexamide', 1)\n",
      "('glipizide', 'glyburide', 7761)\n",
      "('glipizide', 'tolbutamide', 20)\n",
      "('glipizide', 'pioglitazone', 5315)\n",
      "('glipizide', 'rosiglitazone', 4581)\n",
      "('glipizide', 'acarbose', 191)\n",
      "('glipizide', 'miglitol', 18)\n",
      "('glipizide', 'troglitazone', 3)\n",
      "('glipizide', 'tolazamide', 30)\n",
      "('glipizide', 'insulin', 37644)\n",
      "('glyburide', 'metformin', 14907)\n",
      "('glyburide', 'repaglinide', 972)\n",
      "('glyburide', 'nateglinide', 503)\n",
      "('glyburide', 'chlorpropamide', 66)\n",
      "('glyburide', 'glimepiride', 3768)\n",
      "('glyburide', 'acetohexamide', 1)\n",
      "('glyburide', 'glipizide', 9085)\n",
      "('glyburide', 'tolbutamide', 20)\n",
      "('glyburide', 'pioglitazone', 5315)\n",
      "('glyburide', 'rosiglitazone', 4581)\n",
      "('glyburide', 'acarbose', 191)\n",
      "('glyburide', 'miglitol', 18)\n",
      "('glyburide', 'troglitazone', 3)\n",
      "('glyburide', 'tolazamide', 30)\n",
      "('glyburide', 'insulin', 37644)\n",
      "('tolbutamide', 'metformin', 14907)\n",
      "('tolbutamide', 'repaglinide', 972)\n",
      "('tolbutamide', 'nateglinide', 503)\n",
      "('tolbutamide', 'chlorpropamide', 66)\n",
      "('tolbutamide', 'glimepiride', 3768)\n",
      "('tolbutamide', 'acetohexamide', 1)\n",
      "('tolbutamide', 'glipizide', 9085)\n",
      "('tolbutamide', 'glyburide', 7761)\n",
      "('tolbutamide', 'pioglitazone', 5315)\n",
      "('tolbutamide', 'rosiglitazone', 4581)\n",
      "('tolbutamide', 'acarbose', 191)\n",
      "('tolbutamide', 'miglitol', 18)\n",
      "('tolbutamide', 'troglitazone', 3)\n",
      "('tolbutamide', 'tolazamide', 30)\n",
      "('tolbutamide', 'insulin', 37644)\n",
      "('pioglitazone', 'metformin', 14907)\n",
      "('pioglitazone', 'repaglinide', 972)\n",
      "('pioglitazone', 'nateglinide', 503)\n",
      "('pioglitazone', 'chlorpropamide', 66)\n",
      "('pioglitazone', 'glimepiride', 3768)\n",
      "('pioglitazone', 'acetohexamide', 1)\n",
      "('pioglitazone', 'glipizide', 9085)\n",
      "('pioglitazone', 'glyburide', 7761)\n",
      "('pioglitazone', 'tolbutamide', 20)\n",
      "('pioglitazone', 'rosiglitazone', 4581)\n",
      "('pioglitazone', 'acarbose', 191)\n",
      "('pioglitazone', 'miglitol', 18)\n",
      "('pioglitazone', 'troglitazone', 3)\n",
      "('pioglitazone', 'tolazamide', 30)\n",
      "('pioglitazone', 'insulin', 37644)\n",
      "('rosiglitazone', 'metformin', 14907)\n",
      "('rosiglitazone', 'repaglinide', 972)\n",
      "('rosiglitazone', 'nateglinide', 503)\n",
      "('rosiglitazone', 'chlorpropamide', 66)\n",
      "('rosiglitazone', 'glimepiride', 3768)\n",
      "('rosiglitazone', 'acetohexamide', 1)\n",
      "('rosiglitazone', 'glipizide', 9085)\n",
      "('rosiglitazone', 'glyburide', 7761)\n",
      "('rosiglitazone', 'tolbutamide', 20)\n",
      "('rosiglitazone', 'pioglitazone', 5315)\n",
      "('rosiglitazone', 'acarbose', 191)\n",
      "('rosiglitazone', 'miglitol', 18)\n",
      "('rosiglitazone', 'troglitazone', 3)\n",
      "('rosiglitazone', 'tolazamide', 30)\n",
      "('rosiglitazone', 'insulin', 37644)\n",
      "('acarbose', 'metformin', 14907)\n",
      "('acarbose', 'repaglinide', 972)\n",
      "('acarbose', 'nateglinide', 503)\n",
      "('acarbose', 'chlorpropamide', 66)\n",
      "('acarbose', 'glimepiride', 3768)\n",
      "('acarbose', 'acetohexamide', 1)\n",
      "('acarbose', 'glipizide', 9085)\n",
      "('acarbose', 'glyburide', 7761)\n",
      "('acarbose', 'tolbutamide', 20)\n",
      "('acarbose', 'pioglitazone', 5315)\n",
      "('acarbose', 'rosiglitazone', 4581)\n",
      "('acarbose', 'miglitol', 18)\n",
      "('acarbose', 'troglitazone', 3)\n",
      "('acarbose', 'tolazamide', 30)\n",
      "('acarbose', 'insulin', 37644)\n",
      "('miglitol', 'metformin', 14907)\n",
      "('miglitol', 'repaglinide', 972)\n",
      "('miglitol', 'nateglinide', 503)\n",
      "('miglitol', 'chlorpropamide', 66)\n",
      "('miglitol', 'glimepiride', 3768)\n",
      "('miglitol', 'acetohexamide', 1)\n",
      "('miglitol', 'glipizide', 9085)\n",
      "('miglitol', 'glyburide', 7761)\n",
      "('miglitol', 'tolbutamide', 20)\n",
      "('miglitol', 'pioglitazone', 5315)\n",
      "('miglitol', 'rosiglitazone', 4581)\n",
      "('miglitol', 'acarbose', 191)\n",
      "('miglitol', 'troglitazone', 3)\n",
      "('miglitol', 'tolazamide', 30)\n",
      "('miglitol', 'insulin', 37644)\n",
      "('troglitazone', 'metformin', 14907)\n",
      "('troglitazone', 'repaglinide', 972)\n",
      "('troglitazone', 'nateglinide', 503)\n",
      "('troglitazone', 'chlorpropamide', 66)\n",
      "('troglitazone', 'glimepiride', 3768)\n",
      "('troglitazone', 'acetohexamide', 1)\n",
      "('troglitazone', 'glipizide', 9085)\n",
      "('troglitazone', 'glyburide', 7761)\n",
      "('troglitazone', 'tolbutamide', 20)\n",
      "('troglitazone', 'pioglitazone', 5315)\n",
      "('troglitazone', 'rosiglitazone', 4581)\n",
      "('troglitazone', 'acarbose', 191)\n",
      "('troglitazone', 'miglitol', 18)\n",
      "('troglitazone', 'tolazamide', 30)\n",
      "('troglitazone', 'insulin', 37644)\n",
      "('tolazamide', 'metformin', 14907)\n",
      "('tolazamide', 'repaglinide', 972)\n",
      "('tolazamide', 'nateglinide', 503)\n",
      "('tolazamide', 'chlorpropamide', 66)\n",
      "('tolazamide', 'glimepiride', 3768)\n",
      "('tolazamide', 'acetohexamide', 1)\n",
      "('tolazamide', 'glipizide', 9085)\n",
      "('tolazamide', 'glyburide', 7761)\n",
      "('tolazamide', 'tolbutamide', 20)\n",
      "('tolazamide', 'pioglitazone', 5315)\n",
      "('tolazamide', 'rosiglitazone', 4581)\n",
      "('tolazamide', 'acarbose', 191)\n",
      "('tolazamide', 'miglitol', 18)\n",
      "('tolazamide', 'troglitazone', 3)\n",
      "('tolazamide', 'insulin', 37644)\n",
      "('insulin', 'metformin', 14907)\n",
      "('insulin', 'repaglinide', 972)\n",
      "('insulin', 'nateglinide', 503)\n",
      "('insulin', 'chlorpropamide', 66)\n",
      "('insulin', 'glimepiride', 3768)\n",
      "('insulin', 'acetohexamide', 1)\n",
      "('insulin', 'glipizide', 9085)\n",
      "('insulin', 'glyburide', 7761)\n",
      "('insulin', 'tolbutamide', 20)\n",
      "('insulin', 'pioglitazone', 5315)\n",
      "('insulin', 'rosiglitazone', 4581)\n",
      "('insulin', 'acarbose', 191)\n",
      "('insulin', 'miglitol', 18)\n",
      "('insulin', 'troglitazone', 3)\n",
      "('insulin', 'tolazamide', 30)\n"
     ]
    }
   ],
   "source": [
    "list_tuples = []\n",
    "for (k,v) in drug_combination_dict.items():\n",
    "    drugs = k.split('-')\n",
    "    new_tuple = tuple([drugs[0], drugs[1], v])\n",
    "    print(new_tuple)\n",
    "    list_tuples.append(new_tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "with open('drug_combinations.csv','w') as out:\n",
    "    csv_out=csv.writer(out)\n",
    "    csv_out.writerow(['source','target','weight'])\n",
    "    for row in list_tuples:\n",
    "        csv_out.writerow(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>patient_nbr</th>\n",
       "      <th>metformin</th>\n",
       "      <th>repaglinide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51345</th>\n",
       "      <td>46773162</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8610</th>\n",
       "      <td>68193882</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94795</th>\n",
       "      <td>30325446</td>\n",
       "      <td>Up</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92048</th>\n",
       "      <td>98259993</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24253</th>\n",
       "      <td>106186518</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82181</th>\n",
       "      <td>90694521</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40662</th>\n",
       "      <td>79620867</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56677</th>\n",
       "      <td>66950640</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84444</th>\n",
       "      <td>41957361</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48626</th>\n",
       "      <td>84095847</td>\n",
       "      <td>Steady</td>\n",
       "      <td>Steady</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_nbr metformin repaglinide\n",
       "51345     46773162    Steady      Steady\n",
       "8610      68193882    Steady          Up\n",
       "94795     30325446        Up      Steady\n",
       "92048     98259993    Steady      Steady\n",
       "24253    106186518    Steady      Steady\n",
       "...            ...       ...         ...\n",
       "82181     90694521    Steady      Steady\n",
       "40662     79620867    Steady      Steady\n",
       "56677     66950640    Steady      Steady\n",
       "84444     41957361    Steady      Steady\n",
       "48626     84095847    Steady      Steady\n",
       "\n",
       "[214 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking logic above is correct\n",
    "data[(data['metformin'] != 'No') & (data['repaglinide'] != 'No')][['patient_nbr', 'metformin', 'repaglinide']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Drug list:\\nmetformin\\nrepaglinicde\\nacetohexamide\\nglipizide\\nglyburide\\ntolbutamide\\nchlorpropamide\\n'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" Drug list:\n",
    "metformin\n",
    "repaglinicde\n",
    "acetohexamide\n",
    "glipizide\n",
    "glyburide\n",
    "tolbutamide\n",
    "chlorpropamide\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Types of drugs: sulfonylurea 1 and 2\n",
    "Generation 1: chlorpropamide, acetohexamide, tolbutamide, tolazamide. These are not prescribed as much. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glimepiride: 3308\n",
      "13522\n",
      "849\n",
      "472\n",
      "60\n",
      "1\n",
      "8017\n",
      "849\n",
      "Tolbutamide: 18\n",
      "30\n",
      "6650\n",
      "21750\n",
      "4993\n",
      "4333\n",
      "3\n",
      "180\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "print(\"Glimepiride: \" + str(len(data[data['glimepiride'] == 'Steady']))) # 3308\n",
    "print(len(data[data['metformin'] == 'Steady'])) # 13522\n",
    "print(len(data[data['repaglinide'] == 'Steady'])) # 849\n",
    "print(len(data[data['nateglinide'] == 'Steady'])) # 472\n",
    "print(len(data[data['chlorpropamide'] == 'Steady'])) # 60 not many people take this, more \"risky\" because of longer half-life?\n",
    "print(len(data[data['acetohexamide'] == 'Steady'])) # 1\n",
    "print(len(data[data['glipizide'] == 'Steady'])) # 8017\n",
    "print(len(data[data['repaglinide'] == 'Steady'])) # 849\n",
    "print(\"Tolbutamide: \" + str(len(data[data['tolbutamide'] == 'Steady']))) # 18\n",
    "print(len(data[data['tolazamide'] == 'Steady'])) # 30\n",
    "print(len(data[data['glyburide'] == 'Steady'])) # 6650\n",
    "print(len(data[data['insulin'] == 'Steady'])) # 21750\n",
    "print(len(data[data['pioglitazone'] == 'Steady']))\n",
    "print(len(data[data['rosiglitazone'] == 'Steady']))\n",
    "print(len(data[data['troglitazone'] == 'Steady']))\n",
    "print(len(data[data['acarbose'] == 'Steady']))\n",
    "print(len(data[data['miglitol'] == 'Steady']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109\n"
     ]
    }
   ],
   "source": [
    "# analyze gen 1 drugs\n",
    "gen1_cohort = data[(data['chlorpropamide'] == 'Steady') | (data['acetohexamide'] == 'Steady') \n",
    "    | (data['tolazamide'] == 'Steady') | (data['tolbutamide'] == 'Steady')]\n",
    "print(len(gen1_cohort))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17745\n"
     ]
    }
   ],
   "source": [
    "# analyze gen 2 drugs\n",
    "gen2_cohort = data[(data['glipizide'] == 'Steady') | (data['glyburide'] == 'Steady') \n",
    "    | (data['glimepiride'] == 'Steady')]\n",
    "print(len(gen2_cohort))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26086</th>\n",
       "      <td>Female</td>\n",
       "      <td>1.5</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>o</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79027</th>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62149</th>\n",
       "      <td>Female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2740</th>\n",
       "      <td>Female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40768</th>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>u</td>\n",
       "      <td>d</td>\n",
       "      <td>u</td>\n",
       "      <td>11</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47659</th>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>o</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47074</th>\n",
       "      <td>Female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25744</th>\n",
       "      <td>Male</td>\n",
       "      <td>1.5</td>\n",
       "      <td>u</td>\n",
       "      <td>d</td>\n",
       "      <td>u</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27551</th>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>u</td>\n",
       "      <td>d</td>\n",
       "      <td>o</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36722</th>\n",
       "      <td>Female</td>\n",
       "      <td>1.5</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>o</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender  age admission_type_id discharge_disposition_id  \\\n",
       "26086  Female  1.5                 k                        d   \n",
       "79027    Male  2.0                 k                        d   \n",
       "62149  Female  2.0                 k                        d   \n",
       "2740   Female  2.0                 k                        d   \n",
       "40768    Male  2.0                 u                        d   \n",
       "...       ...  ...               ...                      ...   \n",
       "47659    Male  1.0                 k                        d   \n",
       "47074  Female  2.0                 k                        d   \n",
       "25744    Male  1.5                 u                        d   \n",
       "27551    Male  2.0                 u                        d   \n",
       "36722  Female  1.5                 k                        d   \n",
       "\n",
       "      admission_source_id  time_in_hospital  num_lab_procedures  \\\n",
       "26086                   o                14                  68   \n",
       "79027                   r                14                  43   \n",
       "62149                   r                13                  43   \n",
       "2740                    r                13                  35   \n",
       "40768                   u                11                  77   \n",
       "...                   ...               ...                 ...   \n",
       "47659                   o                 1                  46   \n",
       "47074                   r                 1                   9   \n",
       "25744                   u                 1                  19   \n",
       "27551                   o                 1                  41   \n",
       "36722                   o                 1                  37   \n",
       "\n",
       "       num_procedures  num_medications  number_outpatient  ...  citoglipton  \\\n",
       "26086               4               34                  0  ...           No   \n",
       "79027               2               23                  1  ...           No   \n",
       "62149               6               39                  0  ...           No   \n",
       "2740                1               18                  0  ...           No   \n",
       "40768               1               17                  0  ...           No   \n",
       "...               ...              ...                ...  ...          ...   \n",
       "47659               1               13                  0  ...           No   \n",
       "47074               3                7                  0  ...           No   \n",
       "25744               0                8                  0  ...           No   \n",
       "27551               0               19                  0  ...           No   \n",
       "36722               0                6                  0  ...           No   \n",
       "\n",
       "       insulin glyburide-metformin glipizide-metformin  \\\n",
       "26086   Steady                  No                  No   \n",
       "79027       Up                  No                  No   \n",
       "62149   Steady                  No                  No   \n",
       "2740    Steady                  No                  No   \n",
       "40768   Steady                  No                  No   \n",
       "...        ...                 ...                 ...   \n",
       "47659       No                  No                  No   \n",
       "47074   Steady                  No                  No   \n",
       "25744       No                  No                  No   \n",
       "27551   Steady                  No                  No   \n",
       "36722   Steady                  No                  No   \n",
       "\n",
       "      glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "26086                       No                       No   \n",
       "79027                       No                       No   \n",
       "62149                       No                       No   \n",
       "2740                        No                       No   \n",
       "40768                       No                       No   \n",
       "...                        ...                      ...   \n",
       "47659                       No                       No   \n",
       "47074                       No                       No   \n",
       "25744                       No                       No   \n",
       "27551                       No                       No   \n",
       "36722                       No                       No   \n",
       "\n",
       "      metformin-pioglitazone change diabetesMed label  \n",
       "26086                     No     Ch         Yes     1  \n",
       "79027                     No     Ch         Yes     1  \n",
       "62149                     No     Ch         Yes     0  \n",
       "2740                      No     Ch         Yes     0  \n",
       "40768                     No     Ch         Yes     1  \n",
       "...                      ...    ...         ...   ...  \n",
       "47659                     No     No         Yes     0  \n",
       "47074                     No     Ch         Yes     0  \n",
       "25744                     No     Ch         Yes     0  \n",
       "27551                     No     Ch         Yes     1  \n",
       "36722                     No     Ch         Yes     1  \n",
       "\n",
       "[109 rows x 44 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen1_cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46621\n",
      "23592\n"
     ]
    }
   ],
   "source": [
    "# consider imbalance in the data\n",
    "print(len(data[data['label'] == 0]))\n",
    "print(len(data[data['label'] == 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One hot encoding categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'admission_type_id', 'discharge_disposition_id',\n",
       "       'admission_source_id', 'diag_1', 'diag_2', 'diag_3', 'max_glu_serum',\n",
       "       'A1Cresult', 'metformin', 'repaglinide', 'nateglinide',\n",
       "       'chlorpropamide', 'glimepiride', 'acetohexamide', 'glipizide',\n",
       "       'glyburide', 'tolbutamide', 'pioglitazone', 'rosiglitazone', 'acarbose',\n",
       "       'miglitol', 'troglitazone', 'tolazamide', 'examide', 'citoglipton',\n",
       "       'insulin', 'glyburide-metformin', 'glipizide-metformin',\n",
       "       'glimepiride-pioglitazone', 'metformin-rosiglitazone',\n",
       "       'metformin-pioglitazone', 'change', 'diabetesMed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prints out just the categorical variables\n",
    "cat_data = data.select_dtypes(include = 'object')\n",
    "categorical_columns = cat_data.columns\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>max_glu_serum</th>\n",
       "      <th>A1Cresult</th>\n",
       "      <th>metformin</th>\n",
       "      <th>...</th>\n",
       "      <th>examide</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28819</th>\n",
       "      <td>Male</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>None</td>\n",
       "      <td>Norm</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19368</th>\n",
       "      <td>Female</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>o</td>\n",
       "      <td>Circulatory</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Genitourinary</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74178</th>\n",
       "      <td>Male</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>Other</td>\n",
       "      <td>Circulatory</td>\n",
       "      <td>Circulatory</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38988</th>\n",
       "      <td>Female</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>t</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>Male</td>\n",
       "      <td>u</td>\n",
       "      <td>d</td>\n",
       "      <td>u</td>\n",
       "      <td>Circulatory</td>\n",
       "      <td>Respiratory</td>\n",
       "      <td>Genitourinary</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Steady</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Down</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33525</th>\n",
       "      <td>Male</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>Musculoskeletal</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Down</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60165</th>\n",
       "      <td>Female</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>o</td>\n",
       "      <td>Respiratory</td>\n",
       "      <td>Circulatory</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33520</th>\n",
       "      <td>Female</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33510</th>\n",
       "      <td>Male</td>\n",
       "      <td>k</td>\n",
       "      <td>d</td>\n",
       "      <td>r</td>\n",
       "      <td>Circulatory</td>\n",
       "      <td>Neoplasms</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>u</td>\n",
       "      <td>u</td>\n",
       "      <td>r</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70213 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender admission_type_id discharge_disposition_id admission_source_id  \\\n",
       "28819    Male                 k                        d                   r   \n",
       "19368  Female                 k                        d                   o   \n",
       "74178    Male                 k                        d                   r   \n",
       "38988  Female                 k                        d                   t   \n",
       "24998    Male                 u                        d                   u   \n",
       "...       ...               ...                      ...                 ...   \n",
       "33525    Male                 k                        d                   r   \n",
       "60165  Female                 k                        d                   o   \n",
       "33520  Female                 k                        d                   r   \n",
       "33510    Male                 k                        d                   r   \n",
       "0      Female                 u                        u                   r   \n",
       "\n",
       "                diag_1       diag_2         diag_3 max_glu_serum A1Cresult  \\\n",
       "28819         Diabetes    Neoplasms      Neoplasms          None      Norm   \n",
       "19368      Circulatory     Diabetes  Genitourinary          None      None   \n",
       "74178            Other  Circulatory    Circulatory          None      None   \n",
       "38988         Diabetes    Neoplasms          Other          None      None   \n",
       "24998      Circulatory  Respiratory  Genitourinary          None      None   \n",
       "...                ...          ...            ...           ...       ...   \n",
       "33525  Musculoskeletal     Diabetes      Neoplasms          None      None   \n",
       "60165      Respiratory  Circulatory       Diabetes          None      None   \n",
       "33520         Diabetes    Neoplasms      Neoplasms          None      None   \n",
       "33510      Circulatory    Neoplasms       Diabetes          None      None   \n",
       "0             Diabetes        Other          Other          None      None   \n",
       "\n",
       "      metformin  ... examide citoglipton insulin glyburide-metformin  \\\n",
       "28819        No  ...      No          No  Steady                  No   \n",
       "19368        No  ...      No          No    Down                  No   \n",
       "74178        No  ...      No          No      No                  No   \n",
       "38988        No  ...      No          No    Down                  No   \n",
       "24998    Steady  ...      No          No    Down                  No   \n",
       "...         ...  ...     ...         ...     ...                 ...   \n",
       "33525      Down  ...      No          No      Up                  No   \n",
       "60165        No  ...      No          No  Steady                  No   \n",
       "33520        No  ...      No          No      No                  No   \n",
       "33510        No  ...      No          No      No                  No   \n",
       "0            No  ...      No          No      No                  No   \n",
       "\n",
       "      glipizide-metformin glimepiride-pioglitazone metformin-rosiglitazone  \\\n",
       "28819                  No                       No                      No   \n",
       "19368                  No                       No                      No   \n",
       "74178                  No                       No                      No   \n",
       "38988                  No                       No                      No   \n",
       "24998                  No                       No                      No   \n",
       "...                   ...                      ...                     ...   \n",
       "33525                  No                       No                      No   \n",
       "60165                  No                       No                      No   \n",
       "33520                  No                       No                      No   \n",
       "33510                  No                       No                      No   \n",
       "0                      No                       No                      No   \n",
       "\n",
       "      metformin-pioglitazone change diabetesMed  \n",
       "28819                     No     Ch         Yes  \n",
       "19368                     No     Ch         Yes  \n",
       "74178                     No     No          No  \n",
       "38988                     No     Ch         Yes  \n",
       "24998                     No     Ch         Yes  \n",
       "...                      ...    ...         ...  \n",
       "33525                     No     Ch         Yes  \n",
       "60165                     No     No         Yes  \n",
       "33520                     No     No         Yes  \n",
       "33510                     No     No          No  \n",
       "0                         No     No          No  \n",
       "\n",
       "[70213 rows x 34 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_data # one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : 2\n",
      "admission_type_id : 2\n",
      "discharge_disposition_id : 4\n",
      "admission_source_id : 5\n",
      "diag_1 : 9\n",
      "diag_2 : 9\n",
      "diag_3 : 9\n",
      "max_glu_serum : 4\n",
      "A1Cresult : 4\n",
      "metformin : 4\n",
      "repaglinide : 4\n",
      "nateglinide : 4\n",
      "chlorpropamide : 4\n",
      "glimepiride : 4\n",
      "acetohexamide : 2\n",
      "glipizide : 4\n",
      "glyburide : 4\n",
      "tolbutamide : 2\n",
      "pioglitazone : 4\n",
      "rosiglitazone : 4\n",
      "acarbose : 3\n",
      "miglitol : 4\n",
      "troglitazone : 2\n",
      "tolazamide : 2\n",
      "examide : 1\n",
      "citoglipton : 1\n",
      "insulin : 4\n",
      "glyburide-metformin : 4\n",
      "glipizide-metformin : 2\n",
      "glimepiride-pioglitazone : 1\n",
      "metformin-rosiglitazone : 2\n",
      "metformin-pioglitazone : 2\n",
      "change : 2\n",
      "diabetesMed : 2\n"
     ]
    }
   ],
   "source": [
    "# unique values in each columns\n",
    "for x in cat_data.columns:\n",
    "    #prinfting unique values\n",
    "    print(x ,':', len(cat_data[x].unique()))\n",
    "\n",
    "# notice how examide has only 1 value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneHotEncoder()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing numpy\n",
    "import numpy as np\n",
    "# importing OneHotEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "\n",
    "enc = OneHotEncoder(handle_unknown='error')\n",
    "enc.fit(cat_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Female', 'Male'], dtype=object),\n",
       " array(['k', 'u'], dtype=object),\n",
       " array(['d', 'h', 'o', 'u'], dtype=object),\n",
       " array(['b', 'o', 'r', 't', 'u'], dtype=object),\n",
       " array(['Circulatory', 'Diabetes', 'Digestive', 'Genitourinary', 'Injury',\n",
       "        'Musculoskeletal', 'Neoplasms', 'Other', 'Respiratory'],\n",
       "       dtype=object),\n",
       " array(['Circulatory', 'Diabetes', 'Digestive', 'Genitourinary', 'Injury',\n",
       "        'Musculoskeletal', 'Neoplasms', 'Other', 'Respiratory'],\n",
       "       dtype=object),\n",
       " array(['Circulatory', 'Diabetes', 'Digestive', 'Genitourinary', 'Injury',\n",
       "        'Musculoskeletal', 'Neoplasms', 'Other', 'Respiratory'],\n",
       "       dtype=object),\n",
       " array(['>200', '>300', 'None', 'Norm'], dtype=object),\n",
       " array(['>7', '>8', 'None', 'Norm'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['No'], dtype=object),\n",
       " array(['No'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['Down', 'No', 'Steady', 'Up'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['No'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['No', 'Steady'], dtype=object),\n",
       " array(['Ch', 'No'], dtype=object),\n",
       " array(['No', 'Yes'], dtype=object)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                      2\n",
       "admission_type_id           2\n",
       "discharge_disposition_id    4\n",
       "admission_source_id         5\n",
       "diag_1                      9\n",
       "diag_2                      9\n",
       "diag_3                      9\n",
       "max_glu_serum               4\n",
       "A1Cresult                   4\n",
       "metformin                   4\n",
       "repaglinide                 4\n",
       "nateglinide                 4\n",
       "chlorpropamide              4\n",
       "glimepiride                 4\n",
       "acetohexamide               2\n",
       "glipizide                   4\n",
       "glyburide                   4\n",
       "tolbutamide                 2\n",
       "pioglitazone                4\n",
       "rosiglitazone               4\n",
       "acarbose                    3\n",
       "miglitol                    4\n",
       "troglitazone                2\n",
       "tolazamide                  2\n",
       "examide                     1\n",
       "citoglipton                 1\n",
       "insulin                     4\n",
       "glyburide-metformin         4\n",
       "glipizide-metformin         2\n",
       "glimepiride-pioglitazone    1\n",
       "metformin-rosiglitazone     2\n",
       "metformin-pioglitazone      2\n",
       "change                      2\n",
       "diabetesMed                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_data.nunique() # none are binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>label</th>\n",
       "      <th>...</th>\n",
       "      <th>glipizide-metformin_Steady</th>\n",
       "      <th>glimepiride-pioglitazone_No</th>\n",
       "      <th>metformin-rosiglitazone_No</th>\n",
       "      <th>metformin-rosiglitazone_Steady</th>\n",
       "      <th>metformin-pioglitazone_No</th>\n",
       "      <th>metformin-pioglitazone_Steady</th>\n",
       "      <th>change_Ch</th>\n",
       "      <th>change_No</th>\n",
       "      <th>diabetesMed_No</th>\n",
       "      <th>diabetesMed_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28819</th>\n",
       "      <td>1.5</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19368</th>\n",
       "      <td>2.0</td>\n",
       "      <td>14</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74178</th>\n",
       "      <td>2.0</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38988</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>2.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33525</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60165</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33520</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33510</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70213 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "28819  1.5                14                  59               5   \n",
       "19368  2.0                14                  67               0   \n",
       "74178  2.0                14                  61               4   \n",
       "38988  1.0                14                  45               2   \n",
       "24998  2.0                14                  68               0   \n",
       "...    ...               ...                 ...             ...   \n",
       "33525  1.5                 1                  32               1   \n",
       "60165  1.5                 1                  25               0   \n",
       "33520  1.5                 1                  48               1   \n",
       "33510  1.5                 1                  47               5   \n",
       "0      1.0                 1                  41               0   \n",
       "\n",
       "       num_medications  number_outpatient  number_emergency  number_inpatient  \\\n",
       "28819               28                  0                 0                 1   \n",
       "19368               19                  0                 0                 0   \n",
       "74178               16                  0                 0                 1   \n",
       "38988               37                  0                 1                 1   \n",
       "24998               23                  0                 0                 1   \n",
       "...                ...                ...               ...               ...   \n",
       "33525               29                  0                 0                 0   \n",
       "60165                8                  0                 0                 0   \n",
       "33520                8                  0                 0                 0   \n",
       "33510                5                  0                 0                 0   \n",
       "0                    1                  0                 0                 0   \n",
       "\n",
       "       number_diagnoses  label  ...  glipizide-metformin_Steady  \\\n",
       "28819                 5      1  ...                           0   \n",
       "19368                 9      0  ...                           0   \n",
       "74178                 9      0  ...                           0   \n",
       "38988                 9      0  ...                           0   \n",
       "24998                 9      1  ...                           0   \n",
       "...                 ...    ...  ...                         ...   \n",
       "33525                 9      1  ...                           0   \n",
       "60165                 7      1  ...                           0   \n",
       "33520                 9      0  ...                           0   \n",
       "33510                 5      0  ...                           0   \n",
       "0                     1      0  ...                           0   \n",
       "\n",
       "       glimepiride-pioglitazone_No  metformin-rosiglitazone_No  \\\n",
       "28819                            1                           1   \n",
       "19368                            1                           1   \n",
       "74178                            1                           1   \n",
       "38988                            1                           1   \n",
       "24998                            1                           1   \n",
       "...                            ...                         ...   \n",
       "33525                            1                           1   \n",
       "60165                            1                           1   \n",
       "33520                            1                           1   \n",
       "33510                            1                           1   \n",
       "0                                1                           1   \n",
       "\n",
       "       metformin-rosiglitazone_Steady  metformin-pioglitazone_No  \\\n",
       "28819                               0                          1   \n",
       "19368                               0                          1   \n",
       "74178                               0                          1   \n",
       "38988                               0                          1   \n",
       "24998                               0                          1   \n",
       "...                               ...                        ...   \n",
       "33525                               0                          1   \n",
       "60165                               0                          1   \n",
       "33520                               0                          1   \n",
       "33510                               0                          1   \n",
       "0                                   0                          1   \n",
       "\n",
       "       metformin-pioglitazone_Steady  change_Ch  change_No  diabetesMed_No  \\\n",
       "28819                              0          1          0               0   \n",
       "19368                              0          1          0               0   \n",
       "74178                              0          0          1               1   \n",
       "38988                              0          1          0               0   \n",
       "24998                              0          1          0               0   \n",
       "...                              ...        ...        ...             ...   \n",
       "33525                              0          1          0               0   \n",
       "60165                              0          0          1               0   \n",
       "33520                              0          0          1               0   \n",
       "33510                              0          0          1               1   \n",
       "0                                  0          0          1               1   \n",
       "\n",
       "       diabetesMed_Yes  \n",
       "28819                1  \n",
       "19368                1  \n",
       "74178                0  \n",
       "38988                1  \n",
       "24998                1  \n",
       "...                ...  \n",
       "33525                1  \n",
       "60165                1  \n",
       "33520                1  \n",
       "33510                0  \n",
       "0                    0  \n",
       "\n",
       "[70213 rows x 130 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/55229301/one-hot-encoding-multiple-columns-in-sklearn-and-naming-columns \n",
    "ohe_data = pd.get_dummies(data, columns = categorical_columns)\n",
    "ohe_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ohe_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xp/yp2gh__s5j188pt1jsvz_1_r0000gr/T/ipykernel_49828/1212378970.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(ohe_data.age)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Box plot\n",
    "sns.boxplot(ohe_data.age)\n",
    "\n",
    "# Distribution plot\n",
    "#\n",
    "sns.distplot(ohe_data.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70213, 130)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Convert the dataframe to numpy\"\"\"\n",
    "np_data = data.to_numpy()\n",
    "np_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 0., ..., 1., 0., 0.])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isolate just the feature set\n",
    "np_features = np_data[:, :-1]\n",
    "# label column\n",
    "np_label = np_data[:, -1]\n",
    "np_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try clustering with DBSCAN. To do that, we need to reduce dimensionality to 2 components with PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Let's try PCA\"\"\"\n",
    "# first, you need to standardize data\n",
    "# https://stackoverflow.com/questions/4544292/how-do-i-standardize-a-matrix\n",
    "# only look at features\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "standardized_data = StandardScaler().fit_transform(np_features)\n",
    "pca = PCA(n_components=2)\n",
    "transformed_values = pca.fit_transform(standardized_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "# n_neighbors = 5 as kneighbors function returns distance of point to itself (i.e. first column will be zeros) \n",
    "nbrs = NearestNeighbors(n_neighbors=5).fit(transformed_values)\n",
    "# Find the k-neighbors of a point\n",
    "neigh_dist, neigh_ind = nbrs.kneighbors(transformed_values)\n",
    "# sort the neighbor distances (lengths to points) in ascending order\n",
    "# axis = 0 represents sort along first axis i.e. sort along row\n",
    "sort_neigh_dist = np.sort(neigh_dist, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# use this plot to find the best eps\n",
    "k_dist = sort_neigh_dist[:, 4]\n",
    "plt.plot(k_dist)\n",
    "plt.axhline(y=2.5, linewidth=1, linestyle='dashed', color='k')\n",
    "plt.ylabel(\"k-NN distance\")\n",
    "plt.xlabel(\"Sorted observations (4th NN)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Let's try DBSCAN\"\"\"\n",
    "# first standardize data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import DBSCAN\n",
    "from collections import Counter\n",
    "clusters = DBSCAN(eps=0.25, min_samples=4, metric = 'euclidean').fit(transformed_values)\n",
    "c = Counter(clusters.labels_)\n",
    "num_clusters = len(c)\n",
    "num_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1376f8e80>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = clusters.fit_predict(transformed_values)\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.annotate('DBSCAN', xy=(0.03, 0.95), xycoords='axes fraction')\n",
    "plt.scatter(transformed_values[:, 0], transformed_values[:, 1], c=y_pred, s=50, cmap='Dark2')\n",
    "# green points are the outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of clusters: <br>\n",
    "Counter({0: 68328, 1: 919, -1: 61, 2: 887, 4: 14, 3: 4}) <br>\n",
    "Cluster 0: 33.75% readmit, 66.25% no <br>\n",
    "Cluster 1: 31.45% readmit, 68.6% no <br>\n",
    "Cluster 2: 23.11% readmit, 76.89% no <br>\n",
    "Cluster 3: 50% readmit, 50% no <br>\n",
    "Cluster 4: 35.71% readmit, 64.29% no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_proportion(cluster):\n",
    "    patients_indices = np.argwhere(clusters.labels_ == cluster)\n",
    "    patients_indices.flatten()\n",
    "    patients_indices.tolist()\n",
    "    flat_list = [item for sublist in patients_indices for item in sublist]\n",
    "    # print(flat_list) # list of indices\n",
    "    # print(type(flat_list))\n",
    "    patients = data.iloc[flat_list]\n",
    "    return patients['label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Get the percentages corresponding to each cluster\"\"\"\n",
    "print(get_proportion(-1))\n",
    "print(get_proportion(0))\n",
    "print(get_proportion(1))\n",
    "print(get_proportion(2))\n",
    "print(get_proportion(3))\n",
    "print(get_proportion(4))\n",
    "print(get_proportion(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.662496\n",
       "1    0.337504\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Get the patients corresponding to cluster 0\"\"\"\n",
    "c0_patients_indices = np.argwhere(clusters.labels_ == 0)\n",
    "c0_patients_indices.flatten()\n",
    "c0_patients_indices.tolist()\n",
    "flat_list = [item for sublist in c0_patients_indices for item in sublist]\n",
    "# print(flat_list) # list of indices\n",
    "# print(type(flat_list))\n",
    "c0_patients = data.iloc[flat_list]\n",
    "c0_patients['label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.685528\n",
       "1    0.314472\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Get the patients corresponding to cluster 1\"\"\"\n",
    "c1_patients_indices = np.argwhere(clusters.labels_ == 1)\n",
    "c1_patients_indices.flatten()\n",
    "c1_patients_indices.tolist()\n",
    "flat_list = [item for sublist in c1_patients_indices for item in sublist]\n",
    "# print(flat_list) # list of indices\n",
    "# print(type(flat_list))\n",
    "c1_patients = data.iloc[flat_list]\n",
    "c1_patients['label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.768884\n",
       "1    0.231116\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Get the patients corresponding to cluster 2\"\"\"\n",
    "c2_patients_indices = np.argwhere(clusters.labels_ == 2)\n",
    "c2_patients_indices.flatten()\n",
    "c2_patients_indices.tolist()\n",
    "flat_list = [item for sublist in c2_patients_indices for item in sublist]\n",
    "# print(flat_list) # list of indices\n",
    "# print(type(flat_list))\n",
    "c2_patients = data.iloc[flat_list]\n",
    "c2_patients['label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.5\n",
       "0    0.5\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Get the patients corresponding to cluster 3, only has 4 patients\"\"\"\n",
    "c3_patients_indices = np.argwhere(clusters.labels_ == 3)\n",
    "c3_patients_indices.flatten()\n",
    "c3_patients_indices.tolist()\n",
    "flat_list = [item for sublist in c3_patients_indices for item in sublist]\n",
    "# print(flat_list) # list of indices\n",
    "# print(type(flat_list))\n",
    "c3_patients = data.iloc[flat_list]\n",
    "c3_patients['label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.642857\n",
       "0    0.357143\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Get the patients corresponding to cluster 4, only has 14 patients\"\"\"\n",
    "c4_patients_indices = np.argwhere(clusters.labels_ == 4)\n",
    "c4_patients_indices.flatten()\n",
    "c4_patients_indices.tolist()\n",
    "flat_list = [item for sublist in c4_patients_indices for item in sublist]\n",
    "# print(flat_list) # list of indices\n",
    "# print(type(flat_list))\n",
    "c4_patients = data.iloc[flat_list]\n",
    "c4_patients['label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 68328, 1: 919, -1: 61, 2: 887, 4: 14, 3: 4})"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "InterpretML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip3 install interpret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import interpret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: change the data here\n",
    "from sklearn.model_selection import train_test_split\n",
    "# data = gen1_cohort\n",
    "train_cols = data.columns[0:-1]\n",
    "label = data.columns[-1]\n",
    "X = data[train_cols]\n",
    "y = data[label]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExplainableBoostingClassifier(feature_names=[&#x27;gender&#x27;, &#x27;age&#x27;,\n",
       "                                             &#x27;admission_type_id&#x27;,\n",
       "                                             &#x27;discharge_disposition_id&#x27;,\n",
       "                                             &#x27;admission_source_id&#x27;,\n",
       "                                             &#x27;time_in_hospital&#x27;,\n",
       "                                             &#x27;num_lab_procedures&#x27;,\n",
       "                                             &#x27;num_procedures&#x27;,\n",
       "                                             &#x27;num_medications&#x27;,\n",
       "                                             &#x27;number_outpatient&#x27;,\n",
       "                                             &#x27;number_emergency&#x27;,\n",
       "                                             &#x27;number_inpatient&#x27;, &#x27;diag_1&#x27;,\n",
       "                                             &#x27;diag_2&#x27;, &#x27;diag_3&#x27;,\n",
       "                                             &#x27;number_diagnoses&#x27;,\n",
       "                                             &#x27;max_glu_serum&#x27;, &#x27;A1Cresult&#x27;,\n",
       "                                             &#x27;metformin&#x27;, &#x27;r...\n",
       "                                             &#x27;categorical&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;, ...])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExplainableBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>ExplainableBoostingClassifier(feature_names=[&#x27;gender&#x27;, &#x27;age&#x27;,\n",
       "                                             &#x27;admission_type_id&#x27;,\n",
       "                                             &#x27;discharge_disposition_id&#x27;,\n",
       "                                             &#x27;admission_source_id&#x27;,\n",
       "                                             &#x27;time_in_hospital&#x27;,\n",
       "                                             &#x27;num_lab_procedures&#x27;,\n",
       "                                             &#x27;num_procedures&#x27;,\n",
       "                                             &#x27;num_medications&#x27;,\n",
       "                                             &#x27;number_outpatient&#x27;,\n",
       "                                             &#x27;number_emergency&#x27;,\n",
       "                                             &#x27;number_inpatient&#x27;, &#x27;diag_1&#x27;,\n",
       "                                             &#x27;diag_2&#x27;, &#x27;diag_3&#x27;,\n",
       "                                             &#x27;number_diagnoses&#x27;,\n",
       "                                             &#x27;max_glu_serum&#x27;, &#x27;A1Cresult&#x27;,\n",
       "                                             &#x27;metformin&#x27;, &#x27;r...\n",
       "                                             &#x27;categorical&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;,\n",
       "                                             &#x27;categorical&#x27;, &#x27;categorical&#x27;, ...])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExplainableBoostingClassifier(feature_names=['gender', 'age',\n",
       "                                             'admission_type_id',\n",
       "                                             'discharge_disposition_id',\n",
       "                                             'admission_source_id',\n",
       "                                             'time_in_hospital',\n",
       "                                             'num_lab_procedures',\n",
       "                                             'num_procedures',\n",
       "                                             'num_medications',\n",
       "                                             'number_outpatient',\n",
       "                                             'number_emergency',\n",
       "                                             'number_inpatient', 'diag_1',\n",
       "                                             'diag_2', 'diag_3',\n",
       "                                             'number_diagnoses',\n",
       "                                             'max_glu_serum', 'A1Cresult',\n",
       "                                             'metformin', 'r...\n",
       "                                             'categorical', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'continuous',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'categorical',\n",
       "                                             'categorical', 'categorical', ...])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from interpret.glassbox import ExplainableBoostingClassifier\n",
    "\n",
    "ebm = ExplainableBoostingClassifier()\n",
    "ebm.fit(X_train, y_train)\n",
    "\n",
    "# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...\n",
    "# EBM supports pandas dataframes, numpy arrays, and handles \"string\" data natively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/interpret/visual/udash.py:5: UserWarning: \n",
      "The dash_html_components package is deprecated. Please replace\n",
      "`import dash_html_components as html` with `from dash import html`\n",
      "  import dash_html_components as html\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/interpret/visual/udash.py:6: UserWarning: \n",
      "The dash_core_components package is deprecated. Please replace\n",
      "`import dash_core_components as dcc` with `from dash import dcc`\n",
      "  import dash_core_components as dcc\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/interpret/visual/udash.py:7: UserWarning: \n",
      "The dash_table package is deprecated. Please replace\n",
      "`import dash_table` with `from dash import dash_table`\n",
      "\n",
      "Also, if you're using any of the table format helpers (e.g. Group), replace \n",
      "`from dash_table.Format import Group` with \n",
      "`from dash.dash_table.Format import Group`\n",
      "  import dash_table as dt\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<!-- http://127.0.0.1:7001/5210850256/ -->\n",
       "<iframe src=\"http://127.0.0.1:7001/5210850256/\" width=100% height=800 frameBorder=\"0\"></iframe>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from interpret import show\n",
    "from interpret.data import ClassHistogram\n",
    "\n",
    "hist = ClassHistogram().explain_data(X_train, y_train, name = 'Train Data')\n",
    "show(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:180: RuntimeWarning:\n",
      "\n",
      "Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<!-- http://127.0.0.1:7001/4634306176/ -->\n",
       "<iframe src=\"http://127.0.0.1:7001/4634306176/\" width=100% height=800 frameBorder=\"0\"></iframe>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from interpret import show\n",
    "from interpret.data import Marginal\n",
    "\n",
    "marginal = Marginal().explain_data(X_train, y_train, name = 'Train Data')\n",
    "show(marginal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!-- http://127.0.0.1:7001/5266072928/ -->\n",
       "<iframe src=\"http://127.0.0.1:7001/5266072928/\" width=100% height=800 frameBorder=\"0\"></iframe>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from interpret import show\n",
    "ebm_global = ebm.explain_global()\n",
    "show(ebm_global)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!-- http://127.0.0.1:7001/5215965472/ -->\n",
       "<iframe src=\"http://127.0.0.1:7001/5215965472/\" width=100% height=800 frameBorder=\"0\"></iframe>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebm_local = ebm.explain_local(X_test[:5], y_test[:5])\n",
    "show(ebm_local)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  0,  4,  2,  5,  3,  6,  7, 15, 14,  8,  9, 10, 11, 12, 19, 18])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.number_inpatient.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!-- http://127.0.0.1:7001/5269936672/ -->\n",
       "<iframe src=\"http://127.0.0.1:7001/5269936672/\" width=100% height=800 frameBorder=\"0\"></iframe>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from interpret.perf import ROC\n",
    "\n",
    "ebm_perf = ROC(ebm.predict_proba).explain_perf(X_test, y_test, name='EBM')\n",
    "show(ebm_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/interpretml/interpret/blob/develop/examples/python/notebooks/Interpretable%20Classification%20Methods.ipynb"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}