Outcome_regression_obesity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5563f26c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4d8a4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('./stroke_data_dummies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb60236f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_r = df_raw[df_raw.Gender_Other == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d940fa27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_r.drop(columns = ['Unnamed: 0','id','bmi','Gender_Other'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e652b83",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>hypertension</th>\n",
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       "      <th>avg_glucose_level</th>\n",
       "      <th>stroke</th>\n",
       "      <th>marriage</th>\n",
       "      <th>Obesity</th>\n",
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       "      <th>Residence_Rural</th>\n",
       "      <th>Residence_Urban</th>\n",
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      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level  stroke  marriage  \\\n",
       "0  67.0             0              1             228.69       1         1   \n",
       "1  80.0             0              1             105.92       1         1   \n",
       "2  49.0             0              0             171.23       1         1   \n",
       "3  79.0             1              0             174.12       1         1   \n",
       "4  81.0             0              0             186.21       1         1   \n",
       "\n",
       "   Obesity  Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0        1              0            1              0                  0   \n",
       "1        1              0            1              0                  0   \n",
       "2        1              1            0              0                  0   \n",
       "3        0              1            0              0                  0   \n",
       "4        0              0            1              0                  0   \n",
       "\n",
       "   Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0             1                   0              0                0   \n",
       "1             1                   0              0                0   \n",
       "2             1                   0              0                0   \n",
       "3             0                   1              0                0   \n",
       "4             1                   0              0                0   \n",
       "\n",
       "   smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                        1                     0               0   \n",
       "1                        0                     1               0   \n",
       "2                        0                     0               1   \n",
       "3                        0                     1               0   \n",
       "4                        1                     0               0   \n",
       "\n",
       "   Residence_Rural  Residence_Urban  \n",
       "0                0                1  \n",
       "1                1                0  \n",
       "2                0                1  \n",
       "3                1                0  \n",
       "4                0                1  "
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09a10860",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       age  hypertension  heart_disease  avg_glucose_level  marriage  \\\n",
       "0     67.0             0              1             228.69         1   \n",
       "1     80.0             0              1             105.92         1   \n",
       "2     49.0             0              0             171.23         1   \n",
       "3     79.0             1              0             174.12         1   \n",
       "4     81.0             0              0             186.21         1   \n",
       "...    ...           ...            ...                ...       ...   \n",
       "4904  13.0             0              0             103.08         0   \n",
       "4905  81.0             0              0             125.20         1   \n",
       "4906  35.0             0              0              82.99         1   \n",
       "4907  51.0             0              0             166.29         1   \n",
       "4908  44.0             0              0              85.28         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0                 0            1              0                  0   \n",
       "1                 0            1              0                  0   \n",
       "2                 1            0              0                  0   \n",
       "3                 1            0              0                  0   \n",
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       "4906              1            0              0                  0   \n",
       "4907              0            1              0                  0   \n",
       "4908              1            0              1                  0   \n",
       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                1                   0              0                0   \n",
       "1                1                   0              0                0   \n",
       "2                1                   0              0                0   \n",
       "3                0                   1              0                0   \n",
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       "...            ...                 ...            ...              ...   \n",
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       "4906             0                   1              0                0   \n",
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       "4908             0                   0              0                1   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "1                           0                     1               0   \n",
       "2                           0                     0               1   \n",
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       "...                       ...                   ...             ...   \n",
       "4904                        0                     0               0   \n",
       "4905                        0                     1               0   \n",
       "4906                        0                     1               0   \n",
       "4907                        1                     0               0   \n",
       "4908                        0                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "1                   1                0  \n",
       "2                   0                1  \n",
       "3                   1                0  \n",
       "4                   0                1  \n",
       "...               ...              ...  \n",
       "4904                1                0  \n",
       "4905                0                1  \n",
       "4906                1                0  \n",
       "4907                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[4908 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns = ['stroke', 'Obesity'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cdff4d9",
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    "scrolled": false
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    {
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      "text/plain": [
       "       age  hypertension  heart_disease  avg_glucose_level  stroke  marriage  \\\n",
       "3     79.0             1              0             174.12       1         1   \n",
       "4     81.0             0              0             186.21       1         1   \n",
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       "4907  51.0             0              0             166.29       0         1   \n",
       "4908  44.0             0              0              85.28       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "3                 1            0              0                  0   \n",
       "4                 0            1              0                  0   \n",
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       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "3                0                   1              0                0   \n",
       "4                1                   0              0                0   \n",
       "5                1                   0              0                0   \n",
       "6                1                   0              0                0   \n",
       "7                1                   0              0                1   \n",
       "...            ...                 ...            ...              ...   \n",
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       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "3                           0                     1               0   \n",
       "4                           1                     0               0   \n",
       "5                           0                     1               0   \n",
       "6                           0                     1               0   \n",
       "7                           0                     0               0   \n",
       "...                       ...                   ...             ...   \n",
       "4901                        0                     0               0   \n",
       "4902                        0                     1               0   \n",
       "4904                        0                     0               0   \n",
       "4907                        1                     0               0   \n",
       "4908                        0                     0               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "3                   1                0  \n",
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       "7                   0                1  \n",
       "...               ...              ...  \n",
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       "4902                1                0  \n",
       "4904                1                0  \n",
       "4907                1                0  \n",
       "4908                0                1  \n",
       "\n",
       "[2988 rows x 19 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_0 = df[df['Obesity'] == 0]\n",
    "df_0 = df_0.drop(columns = ['Obesity'])\n",
    "df_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4d3b7a0",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4898</th>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4903</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.85</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125.20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4906</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1920 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease  avg_glucose_level  stroke  marriage  \\\n",
       "0     67.0             0              1             228.69       1         1   \n",
       "1     80.0             0              1             105.92       1         1   \n",
       "2     49.0             0              0             171.23       1         1   \n",
       "9     61.0             0              1             120.46       1         1   \n",
       "12    50.0             1              0             167.41       1         1   \n",
       "...    ...           ...            ...                ...     ...       ...   \n",
       "4894  76.0             0              0              82.35       0         1   \n",
       "4898  68.0             0              0              91.68       0         1   \n",
       "4903  18.0             0              0              82.85       0         0   \n",
       "4905  81.0             0              0             125.20       0         1   \n",
       "4906  35.0             0              0              82.99       0         1   \n",
       "\n",
       "      Gender_Female  Gender_Male  Work_Govt_job  Work_Never_worked  \\\n",
       "0                 0            1              0                  0   \n",
       "1                 0            1              0                  0   \n",
       "2                 1            0              0                  0   \n",
       "9                 1            0              1                  0   \n",
       "12                1            0              0                  0   \n",
       "...             ...          ...            ...                ...   \n",
       "4894              0            1              1                  0   \n",
       "4898              0            1              0                  0   \n",
       "4903              1            0              0                  0   \n",
       "4905              1            0              0                  0   \n",
       "4906              1            0              0                  0   \n",
       "\n",
       "      Work_Private  Work_Self-employed  Work_children  smoking_Unknown  \\\n",
       "0                1                   0              0                0   \n",
       "1                1                   0              0                0   \n",
       "2                1                   0              0                0   \n",
       "9                0                   0              0                0   \n",
       "12               0                   1              0                0   \n",
       "...            ...                 ...            ...              ...   \n",
       "4894             0                   0              0                0   \n",
       "4898             0                   1              0                1   \n",
       "4903             1                   0              0                1   \n",
       "4905             0                   1              0                0   \n",
       "4906             0                   1              0                0   \n",
       "\n",
       "      smoking_formerly smoked  smoking_never smoked  smoking_smokes  \\\n",
       "0                           1                     0               0   \n",
       "1                           0                     1               0   \n",
       "2                           0                     0               1   \n",
       "9                           0                     0               1   \n",
       "12                          0                     1               0   \n",
       "...                       ...                   ...             ...   \n",
       "4894                        0                     1               0   \n",
       "4898                        0                     0               0   \n",
       "4903                        0                     0               0   \n",
       "4905                        0                     1               0   \n",
       "4906                        0                     1               0   \n",
       "\n",
       "      Residence_Rural  Residence_Urban  \n",
       "0                   0                1  \n",
       "1                   1                0  \n",
       "2                   0                1  \n",
       "9                   1                0  \n",
       "12                  1                0  \n",
       "...               ...              ...  \n",
       "4894                0                1  \n",
       "4898                0                1  \n",
       "4903                0                1  \n",
       "4905                0                1  \n",
       "4906                1                0  \n",
       "\n",
       "[1920 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df[df['Obesity'] == 1]\n",
    "df_1 = df_1.drop(columns = ['Obesity'])\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a80fb9c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_0 = df_0['stroke']\n",
    "x_0 = df_0.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2612ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = df_1['stroke']\n",
    "x_1 = df_1.drop(columns = ['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8bfa4273",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445966eb",
   "metadata": {},
   "source": [
    "# Model for controlled group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2250af41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9618454591799578"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=60)\n",
    "score_pre_0 = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "score_pre_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75951716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9625149157157022 41\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(0,200,10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=60)\n",
    "    score = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),(scorel.index(max(scorel))*10)+1)\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(1,201,10),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50caa82a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.962850484305586 43\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 4320x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(30,50):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=60)\n",
    "    score = cross_val_score(rfc,x_0,y_0,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),([*range(30,50)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[60,20])\n",
    "plt.plot(range(30,50),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5fc99af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=43, random_state=60),\n",
       "             param_grid={'max_depth': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
       "       35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67,\n",
       "       69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99])})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 100, 2)}\n",
    "rfc = RandomForestClassifier(n_estimators=43\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35099b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 7}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56a53ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9631866131101438"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8438727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=43, random_state=60),\n",
       "             param_grid={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=43\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2ce1964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 4}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "61f4ca98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9621804675551671"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "041a4745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=43, random_state=60),\n",
       "             param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=43\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "69b5605a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 3}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e6e4d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9628516047349344"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d577bcce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=43, random_state=60),\n",
       "             param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21])})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=43\n",
    "                             ,random_state=60\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_0,y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fff77457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 9}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5315b785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9631866131101438"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35d5a303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9628516047349344"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_0 = RandomForestClassifier(n_estimators=43,max_depth = 7,max_features = 4, min_samples_split = 9, random_state=60)\n",
    "score_0 = cross_val_score(rfc_0,x_0,y_0,cv=5).mean()\n",
    "score_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "625befab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0010061455549765785"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_0 - score_pre_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d8b047",
   "metadata": {},
   "source": [
    "# Model for treatment group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "118c2d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9427083333333334"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100,random_state=90)\n",
    "score_pre_1 = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "score_pre_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "518fd43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9447916666666668 51\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(0,200,10):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=80)\n",
    "    score = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),(scorel.index(max(scorel))*10)+1)\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(1,201,10),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b90ae43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9453125 45\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scorel = []\n",
    "for i in range(20,80):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,\n",
    "                                 n_jobs=-1,\n",
    "                                random_state=80)\n",
    "    score = cross_val_score(rfc,x_1,y_1,cv=5).mean()\n",
    "    scorel.append(score)\n",
    "\n",
    "print(max(scorel),([*range(20,60)][scorel.index(max(scorel))]))\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(20,80),scorel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e791d5b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=45, random_state=80),\n",
       "             param_grid={'max_depth': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
       "       35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67,\n",
       "       69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99])})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth':np.arange(1, 100, 2)}\n",
    "rfc = RandomForestClassifier(n_estimators=45\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8e4fe8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 1}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed762c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9489583333333333"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e4d62d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=45, random_state=80),\n",
       "             param_grid={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features':np.arange(1,20,1)}\n",
    "rfc = RandomForestClassifier(n_estimators=45\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5b8d2a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 3}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "044f4cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9447916666666666"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b9afecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=45, random_state=80),\n",
       "             param_grid={'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_leaf':np.arange(1, 20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=45\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d06c6f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_leaf': 5}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b877f4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9489583333333333"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5d217c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=RandomForestClassifier(n_estimators=45, random_state=80),\n",
       "             param_grid={'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21])})"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'min_samples_split':np.arange(2, 2+20, 1)}\n",
    "rfc = RandomForestClassifier(n_estimators=45\n",
    "                             ,random_state=80\n",
    "                             )\n",
    "GS = GridSearchCV(rfc,param_grid,cv=5)\n",
    "GS.fit(x_1,y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "41f2c63b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 9}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4d44cd8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9484375"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0d8cb3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9489583333333333"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_1 = RandomForestClassifier(n_estimators=45,max_depth = 1, random_state=80)\n",
    "score_1 = cross_val_score(rfc_1,x_1,y_1,cv=5).mean()\n",
    "score_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e6311425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006249999999999978"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_1 - score_pre_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dee8914",
   "metadata": {},
   "source": [
    "# Outcome modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7cb6c940",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "727458a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc0 = rfc_0.fit(x_0, y_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7a4faaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00020374898125509371"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_0 = rfc0.predict(X)\n",
    "mean_0 = predict_0.mean()\n",
    "mean_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a7806102",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc1 = rfc_1.fit(x_1, y_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39676bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_1 = rfc1.predict(X)\n",
    "mean_1 = predict_1.mean()\n",
    "mean_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "20398cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.00020374898125509371"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATE_outcome = mean_1 - mean_0\n",
    "ATE_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1fb1d47a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998 0.8413200916568588\n"
     ]
    }
   ],
   "source": [
    "t,pval = scipy.stats.ttest_ind(predict_0,predict_1,equal_var=False,alternative='less')\n",
    "print(t,pval)"
   ]
  }
 ],
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