pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from hyperopt import hp\n",
    "from sklearn.pipeline import Pipeline\n",
    "from transformers.categorical_transformer import CategoricalTransformer\n",
    "from transformers.numerical_transformer import NumericalTransformer\n",
    "from transformers.feature_selection_transformer import FeatureTransformer\n",
    "from estimators.lr_estimator import LogisticRegressionEstimator\n",
    "from estimators.rfc_estimator import RandomForestEstimator\n",
    "from functions import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./WA_Fn-UseC_-HR-Employee-Attrition.csv\")\n",
    "_target_column = \"Attrition\"\n",
    "df = df.sample(frac=1, random_state=1).reset_index(drop=True)\n",
    "df.drop(columns=\"EmployeeNumber\",inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>370</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>80</td>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>544</td>\n",
       "      <td>Sales</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>138</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1333</td>\n",
       "      <td>Sales</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1369</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
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       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
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       "      <td>9</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>827</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>29</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>694</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>898</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>43</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1034</td>\n",
       "      <td>Sales</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>24</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>830</td>\n",
       "      <td>Sales</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0      37       Yes      Travel_Rarely        370  Research & Development   \n",
       "1      18       Yes  Travel_Frequently        544                   Sales   \n",
       "2      30       Yes      Travel_Rarely        138  Research & Development   \n",
       "3      34        No      Travel_Rarely       1333                   Sales   \n",
       "4      56        No      Travel_Rarely       1369  Research & Development   \n",
       "...   ...       ...                ...        ...                     ...   \n",
       "1465   33        No  Travel_Frequently        827  Research & Development   \n",
       "1466   29        No      Travel_Rarely        694  Research & Development   \n",
       "1467   40        No      Travel_Rarely        898         Human Resources   \n",
       "1468   43        No      Travel_Rarely       1034                   Sales   \n",
       "1469   24        No         Non-Travel        830                   Sales   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "0                   10          4        Medical              1   \n",
       "1                    3          2        Medical              1   \n",
       "2                   22          3  Life Sciences              1   \n",
       "3                   10          4  Life Sciences              1   \n",
       "4                   23          3  Life Sciences              1   \n",
       "...                ...        ...            ...            ...   \n",
       "1465                 1          4          Other              1   \n",
       "1466                 1          3  Life Sciences              1   \n",
       "1467                 6          2        Medical              1   \n",
       "1468                16          3      Marketing              1   \n",
       "1469                13          2  Life Sciences              1   \n",
       "\n",
       "      EnvironmentSatisfaction  ... RelationshipSatisfaction  StandardHours  \\\n",
       "0                           4  ...                        2             80   \n",
       "1                           2  ...                        3             80   \n",
       "2                           1  ...                        2             80   \n",
       "3                           3  ...                        4             80   \n",
       "4                           4  ...                        1             80   \n",
       "...                       ...  ...                      ...            ...   \n",
       "1465                        3  ...                        1             80   \n",
       "1466                        4  ...                        2             80   \n",
       "1467                        3  ...                        4             80   \n",
       "1468                        4  ...                        3             80   \n",
       "1469                        4  ...                        3             80   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears TrainingTimesLastYear  \\\n",
       "0                    0                 10                     4   \n",
       "1                    0                  0                     2   \n",
       "2                    0                  7                     2   \n",
       "3                    1                  1                     2   \n",
       "4                    1                 33                     0   \n",
       "...                ...                ...                   ...   \n",
       "1465                 1                  6                     2   \n",
       "1466                 2                  9                     2   \n",
       "1467                 0                 21                     2   \n",
       "1468                 1                 22                     3   \n",
       "1469                 1                  1                     2   \n",
       "\n",
       "      WorkLifeBalance YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                   1             10                   3   \n",
       "1                   4              0                   0   \n",
       "2                   3              5                   2   \n",
       "3                   3              1                   1   \n",
       "4                   3             19                  16   \n",
       "...               ...            ...                 ...   \n",
       "1465                3              6                   5   \n",
       "1466                2              7                   7   \n",
       "1467                3             21                   7   \n",
       "1468                3             17                  13   \n",
       "1469                3              1                   0   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                           0                     8  \n",
       "1                           0                     0  \n",
       "2                           0                     1  \n",
       "3                           0                     0  \n",
       "4                          15                     9  \n",
       "...                       ...                   ...  \n",
       "1465                        1                     2  \n",
       "1466                        1                     7  \n",
       "1467                        7                     7  \n",
       "1468                        1                     9  \n",
       "1469                        0                     0  \n",
       "\n",
       "[1470 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = distribute_data_equally(df,_target_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding target variables\n",
    "label_encoder = LabelEncoder()\n",
    "df[_target_column] = label_encoder.fit_transform(df[_target_column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in train data: 402\n",
      "Number of rows in test data: 72\n"
     ]
    }
   ],
   "source": [
    "train_data, train_labels, test_data, test_labels = split_dataset(df, _target_column, 0.15)\n",
    "print(f\"Number of rows in train data: {len(train_data)}\")\n",
    "print(f\"Number of rows in test data: {len(test_data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CategoricalTransformer constructor is called\n",
      ">>>>> NumericalTransformer constructor called\n",
      ">>>>>> feature transformer constructor called\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Github Profile\\churn-prediction-pipeline\\transformers\\categorical_transformer.py:14: FutureWarning:\n",
      "\n",
      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cat_transformer = CategoricalTransformer(drop_single_column=True)\n",
    "preprocessing_pipeline = Pipeline(steps=[\n",
    "        (\"categorical_transformer\",cat_transformer),\n",
    "        (\"numerical_transformer\",NumericalTransformer(target_column=_target_column))\n",
    "        ])\n",
    "feature_selection_pipeline = Pipeline(steps=[\n",
    "       (\"feature_importance_transformer\", FeatureTransformer(threshold=10))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_space = {\n",
    "    \"solver\": hp.choice(\"solver\",['lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga']),\n",
    "    \"C\": hp.quniform(\"C\",1,30,2),\n",
    "    \"max_iter\": hp.quniform(\"max_iter\",100,1000,100)\n",
    "}\n",
    "parameters={\n",
    "       \"iterations\":120,\n",
    "       \"kfolds\":5\n",
    "}\n",
    "lr_estimator = LogisticRegressionEstimator(target_column=_target_column, tune_hyperparameter=True, search_space= search_space, parameters = parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_space = {\n",
    "    \"n_estimators\":hp.uniformint(\"n_estimators\",100,1000),\n",
    "    \"bootstrap\":hp.choice(\"bootstrap\",[True, False]),\n",
    "    \"max_depth\": hp.uniformint(\"max_depth\",10,100),\n",
    "    \"max_features\":hp.choice(\"max_features\",[\"sqrt\", \"log2\", None]),\n",
    "    \n",
    "}\n",
    "parameters={\n",
    "       \"iterations\":120,\n",
    "       \"kfolds\":5\n",
    "}\n",
    "rfc_estimator = RandomForestEstimator(target_column=_target_column, tune_hyperparameter=True, search_space= search_space, parameters = parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pipeline = Pipeline(steps=[   \n",
    "                                    (\"preprocessing_pipeline\",preprocessing_pipeline),\n",
    "                                    (\"feature_selection\",feature_selection_pipeline),    \n",
    "                                    (\"estimator\", lr_estimator)\n",
    "                                ]\n",
    "                          )\n",
    "rfc_pipeline = Pipeline(steps=[   \n",
    "                                    (\"preprocessing_pipeline\",preprocessing_pipeline),\n",
    "                                    (\"feature_selection\",feature_selection_pipeline),    \n",
    "                                    (\"model\", rfc_estimator)\n",
    "                                ]\n",
    "                          )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>> Categorically fit_transformer called\n",
      ">>>>>> Categorically fit called\n",
      ">>>>>> Categorically transformer called\n",
      ">>>>> Numerical Transformer transform method called\n",
      ">>>>>> feature transformer fit method called\n",
      ">>>>>> feature transformer transform called\n",
      ">>>>>>>>>> Estimator Fit method is called\n",
      "Tuning Hyper-parameter for Logistic Regression\n",
      "100%|██████████| 120/120 [00:05<00:00, 20.75trial/s, best loss: 0.27089506172839506]\n",
      "Tuning completed\n",
      "Best parameters for Logistic Regression are: {'C': 26.0, 'max_iter': 200.0, 'solver': 'newton-cg'}\n"
     ]
    },
    {
     "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>Pipeline(steps=[(&#x27;preprocessing_pipeline&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;categorical_transformer&#x27;,\n",
       "                                  CategoricalTransformer()),\n",
       "                                 (&#x27;numerical_transformer&#x27;,\n",
       "                                  NumericalTransformer())])),\n",
       "                (&#x27;feature_selection&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;feature_importance_transformer&#x27;,\n",
       "                                  FeatureTransformer(threshold=10))])),\n",
       "                (&#x27;estimator&#x27;,\n",
       "                 &lt;estimators.lr_estimator.LogisticRegressionEstimator object at 0x0000022980382350&gt;)])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing_pipeline&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;categorical_transformer&#x27;,\n",
       "                                  CategoricalTransformer()),\n",
       "                                 (&#x27;numerical_transformer&#x27;,\n",
       "                                  NumericalTransformer())])),\n",
       "                (&#x27;feature_selection&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;feature_importance_transformer&#x27;,\n",
       "                                  FeatureTransformer(threshold=10))])),\n",
       "                (&#x27;estimator&#x27;,\n",
       "                 &lt;estimators.lr_estimator.LogisticRegressionEstimator object at 0x0000022980382350&gt;)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing_pipeline: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;categorical_transformer&#x27;, CategoricalTransformer()),\n",
       "                (&#x27;numerical_transformer&#x27;, NumericalTransformer())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CategoricalTransformer</label><div class=\"sk-toggleable__content\"><pre>CategoricalTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NumericalTransformer</label><div class=\"sk-toggleable__content\"><pre>NumericalTransformer()</pre></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_selection: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;feature_importance_transformer&#x27;,\n",
       "                 FeatureTransformer(threshold=10))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FeatureTransformer</label><div class=\"sk-toggleable__content\"><pre>FeatureTransformer(threshold=10)</pre></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegressionEstimator</label><div class=\"sk-toggleable__content\"><pre>&lt;estimators.lr_estimator.LogisticRegressionEstimator object at 0x0000022980382350&gt;</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessing_pipeline',\n",
       "                 Pipeline(steps=[('categorical_transformer',\n",
       "                                  CategoricalTransformer()),\n",
       "                                 ('numerical_transformer',\n",
       "                                  NumericalTransformer())])),\n",
       "                ('feature_selection',\n",
       "                 Pipeline(steps=[('feature_importance_transformer',\n",
       "                                  FeatureTransformer(threshold=10))])),\n",
       "                ('estimator',\n",
       "                 <estimators.lr_estimator.LogisticRegressionEstimator object at 0x0000022980382350>)])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression pipeline fit\n",
    "lr_pipeline.fit(train_data,train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>> Categorically transformer called\n",
      ">>>>> Numerical Transformer transform method called\n",
      ">>>>>> feature transformer transform called\n",
      "Index(['Age', 'EnvironmentSatisfaction', 'JobInvolvement', 'JobLevel',\n",
      "       'JobRole', 'MaritalStatus', 'MonthlyIncome', 'OverTime',\n",
      "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
      "       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',\n",
      "       'YearsWithCurrManager'],\n",
      "      dtype='object')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.75      0.75        36\n",
      "           1       0.75      0.75      0.75        36\n",
      "\n",
      "    accuracy                           0.75        72\n",
      "   macro avg       0.75      0.75      0.75        72\n",
      "weighted avg       0.75      0.75      0.75        72\n",
      "\n",
      "Total accuracy: 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Github Profile\\churn-prediction-pipeline\\transformers\\categorical_transformer.py:85: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression Prediction\n",
    "y_pred = lr_pipeline.predict(test_data)\n",
    "show_confusion_matrix(test_labels, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>> Categorically fit_transformer called\n",
      ">>>>>> Categorically fit called\n",
      ">>>>>> Categorically transformer called\n",
      ">>>>> Numerical Transformer transform method called\n",
      ">>>>>> feature transformer fit method called\n",
      ">>>>>> feature transformer transform called\n",
      ">>>>>>>>>> Estimator Fit method is called\n",
      "Tuning Hyper-parameter for Logistic Regression\n",
      "100%|██████████| 120/120 [00:06<00:00, 18.59trial/s, best loss: 0.2958333333333334]\n",
      "Tuning completed\n",
      "Best parameters for Logistic Regression are: {'C': 2.0, 'max_iter': 100.0, 'solver': 'sag'}\n",
      ">>>>>> Categorically fit_transformer called\n",
      ">>>>>> Categorically fit called\n",
      ">>>>>> Categorically transformer called\n",
      ">>>>> Numerical Transformer transform method called\n",
      ">>>>>> feature transformer fit method called\n",
      ">>>>>> feature transformer transform called\n",
      ">> Training Random forest\n",
      "Tuning Hyper-parameter for Random Forest\n",
      "100%|██████████| 120/120 [09:38<00:00,  4.82s/trial, best loss: 0.28330246913580237]\n",
      "Tuning completed\n",
      "Best parameters for Random Forest are: {'bootstrap': True, 'max_depth': 11, 'max_features': 'log2', 'n_estimators': 169}\n"
     ]
    }
   ],
   "source": [
    "# Random forest pipeline\n",
    "rfc_pipeline.fit(train_data,train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>>>> Categorically transformer called\n",
      ">>>>> Numerical Transformer transform method called\n",
      ">>>>>> feature transformer transform called\n",
      "Index(['Age', 'DailyRate', 'DistanceFromHome', 'EducationField',\n",
      "       'EnvironmentSatisfaction', 'JobInvolvement', 'JobLevel',\n",
      "       'MaritalStatus', 'MonthlyIncome', 'OverTime', 'StockOptionLevel',\n",
      "       'TotalWorkingYears', 'YearsAtCompany', 'YearsInCurrentRole',\n",
      "       'YearsWithCurrManager'],\n",
      "      dtype='object')\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.70      0.66        33\n",
      "           1       0.71      0.64      0.68        39\n",
      "\n",
      "    accuracy                           0.67        72\n",
      "   macro avg       0.67      0.67      0.67        72\n",
      "weighted avg       0.67      0.67      0.67        72\n",
      "\n",
      "Total accuracy: 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\churn modelling\\pipeline - 2.0\\categorical_transformer.py:85: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Prediction\n",
    "y_pred = rfc_pipeline.predict(test_data)\n",
    "show_confusion_matrix(test_labels, y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "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
 },
 "nbformat": 4,
 "nbformat_minor": 2
}