{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Save and Load Model\n",
    "import pickle\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luay9\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\base.py:299: UserWarning: Trying to unpickle estimator MinMaxScaler from version 1.2.2 when using version 1.2.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\luay9\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\base.py:299: UserWarning: Trying to unpickle estimator StandardScaler from version 1.2.2 when using version 1.2.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\luay9\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\base.py:299: UserWarning: Trying to unpickle estimator ColumnTransformer from version 1.2.2 when using version 1.2.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\luay9\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\base.py:299: UserWarning: Trying to unpickle estimator NearestNeighbors from version 1.2.2 when using version 1.2.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\luay9\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\base.py:299: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 1.2.2 when using version 1.2.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "C:\\Users\\luay9\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\sklearn\\base.py:299: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 1.2.2 when using version 1.2.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Loading Model Files\n",
    "\n",
    "with open('rf_gridcv_best.pkl', 'rb') as file_1:\n",
    "  rf_gridcv_best = pickle.load(file_1)\n",
    "\n",
    "with open('Drop_Columns.txt','r') as file_2:\n",
    "  Drop_Columns = json.load(file_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>5.00</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>215000.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>338000.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>281000.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7861.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.10</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>368000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330000.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>155000.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.18</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  65.0      0.0                      56.0       0.0               25.0   \n",
       "1  67.0      0.0                     213.0       0.0               38.0   \n",
       "2  75.0      0.0                     582.0       0.0               20.0   \n",
       "3  72.0      1.0                     943.0       0.0               25.0   \n",
       "4  65.0      0.0                     198.0       1.0               35.0   \n",
       "5  55.0      0.0                    7861.0       0.0               38.0   \n",
       "6  75.0      1.0                      81.0       0.0               38.0   \n",
       "7  53.0      1.0                     707.0       0.0               38.0   \n",
       "8  62.0      0.0                      61.0       1.0               38.0   \n",
       "9  75.0      0.0                     582.0       0.0               40.0   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                  0.0  237000.00              5.00         130.0  0.0   \n",
       "1                  0.0  215000.00              1.20         133.0  0.0   \n",
       "2                  1.0  265000.00              1.90         130.0  1.0   \n",
       "3                  1.0  338000.00              1.70         139.0  1.0   \n",
       "4                  1.0  281000.00              0.90         137.0  1.0   \n",
       "5                  0.0  263358.03              1.10         136.0  1.0   \n",
       "6                  1.0  368000.00              4.00         131.0  1.0   \n",
       "7                  0.0  330000.00              1.40         137.0  1.0   \n",
       "8                  1.0  155000.00              1.10         143.0  1.0   \n",
       "9                  0.0  263358.03              1.18         137.0  1.0   \n",
       "\n",
       "   smoking   time  DEATH_EVENT  \n",
       "0      0.0  207.0          0.0  \n",
       "1      0.0  245.0          0.0  \n",
       "2      0.0    4.0          1.0  \n",
       "3      1.0  111.0          1.0  \n",
       "4      1.0  146.0          0.0  \n",
       "5      0.0    6.0          1.0  \n",
       "6      1.0   10.0          1.0  \n",
       "7      1.0  209.0          0.0  \n",
       "8      1.0  270.0          0.0  \n",
       "9      0.0  107.0          0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data inference\n",
    "df_inf = pd.read_csv('G:\\My Drive\\FTDS\\phase 1\\p1gc3\\h8dsft_p1g3_Ahmad Luay Adnani_inference.csv')\n",
    "df_inf_copy = df_inf.copy()\n",
    "df_inf_copy = df_inf.drop(['Unnamed: 0'],axis=1)\n",
    "df_inf_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>237000.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>130.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>215000.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>133.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>130.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>338000.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>139.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>281000.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>137.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.0</td>\n",
       "      <td>7861.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.10</td>\n",
       "      <td>136.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>368000.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>131.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>53.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>330000.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>137.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>155000.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>143.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>75.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.18</td>\n",
       "      <td>137.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  creatinine_phosphokinase  ejection_fraction  platelets  \\\n",
       "0  65.0                      56.0               25.0  237000.00   \n",
       "1  67.0                     213.0               38.0  215000.00   \n",
       "2  75.0                     582.0               20.0  265000.00   \n",
       "3  72.0                     943.0               25.0  338000.00   \n",
       "4  65.0                     198.0               35.0  281000.00   \n",
       "5  55.0                    7861.0               38.0  263358.03   \n",
       "6  75.0                      81.0               38.0  368000.00   \n",
       "7  53.0                     707.0               38.0  330000.00   \n",
       "8  62.0                      61.0               38.0  155000.00   \n",
       "9  75.0                     582.0               40.0  263358.03   \n",
       "\n",
       "   serum_creatinine  serum_sodium   time  DEATH_EVENT  \n",
       "0              5.00         130.0  207.0          0.0  \n",
       "1              1.20         133.0  245.0          0.0  \n",
       "2              1.90         130.0    4.0          1.0  \n",
       "3              1.70         139.0  111.0          1.0  \n",
       "4              0.90         137.0  146.0          0.0  \n",
       "5              1.10         136.0    6.0          1.0  \n",
       "6              4.00         131.0   10.0          1.0  \n",
       "7              1.40         137.0  209.0          0.0  \n",
       "8              1.10         143.0  270.0          0.0  \n",
       "9              1.18         137.0  107.0          0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing unnecessary features\n",
    "df_inf_final = df_inf_copy.drop(Drop_Columns,axis=1).sort_index()\n",
    "df_inf_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., 1., 0., 1., 1., 0., 0., 0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict using RandomForest\n",
    "\n",
    "y_pred_inf = rf_gridcv_best.predict(df_inf_final) #rf_gridcv_best from load files\n",
    "y_pred_inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEATH_EVENT_Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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      ],
      "text/plain": [
       "   DEATH_EVENT_Prediction\n",
       "0                     1.0\n",
       "1                     0.0\n",
       "2                     1.0\n",
       "3                     1.0\n",
       "4                     0.0\n",
       "5                     1.0\n",
       "6                     1.0\n",
       "7                     0.0\n",
       "8                     0.0\n",
       "9                     0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "# Create DEATH_EVENT Prediction DataFrame\n",
    "\n",
    "y_pred_inf_df = pd.DataFrame(y_pred_inf, columns=['DEATH_EVENT_Prediction'],index=df_inf_final.index)\n",
    "round(y_pred_inf_df.head(10),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "      <th>DEATH_EVENT_Prediction</th>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>9</th>\n",
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      "text/plain": [
       "   DEATH_EVENT  DEATH_EVENT_Prediction\n",
       "0          0.0                     1.0\n",
       "1          0.0                     0.0\n",
       "2          1.0                     1.0\n",
       "3          1.0                     1.0\n",
       "4          0.0                     0.0\n",
       "5          1.0                     1.0\n",
       "6          1.0                     1.0\n",
       "7          0.0                     0.0\n",
       "8          0.0                     0.0\n",
       "9          0.0                     0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the real default_payment_next_month and predicted classification\n",
    "model_inf = pd.concat([df_inf_copy['DEATH_EVENT'], y_pred_inf_df['DEATH_EVENT_Prediction']], axis=1).sort_index()\n",
    "model_inf"
   ]
  }
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