notebooks/dataprocessing/module_tests/rebalance_sdv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "193967f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../../notebooks')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from raimitigations.dataprocessing import Synthesizer\n",
    "from download import download_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f32308",
   "metadata": {},
   "source": [
    "# Synthesizer Class (SDV)\n",
    "\n",
    "This class is a wrapper over the generative models for tabular data available in the SDV library. The **Synthesizer** enhances the interface of these models by allowing the user to specify predefined strategies similar to those used in the imblearn library. This notebook will explore different ways to use this class for generating synthetic data and for data balance.\n",
    "\n",
    "First of all, let's load the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "514b2d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              department     region         education gender  \\\n",
       "0      Sales & Marketing   region_7  Master's & above      f   \n",
       "1             Operations  region_22        Bachelor's      m   \n",
       "2      Sales & Marketing  region_19        Bachelor's      m   \n",
       "3      Sales & Marketing  region_23        Bachelor's      m   \n",
       "4             Technology  region_26        Bachelor's      m   \n",
       "...                  ...        ...               ...    ...   \n",
       "54803         Technology  region_14        Bachelor's      m   \n",
       "54804         Operations  region_27  Master's & above      f   \n",
       "54805          Analytics   region_1        Bachelor's      m   \n",
       "54806  Sales & Marketing   region_9               NaN      m   \n",
       "54807                 HR  region_22        Bachelor's      m   \n",
       "\n",
       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                sourcing                1   35                   5.0   \n",
       "1                   other                1   30                   5.0   \n",
       "2                sourcing                1   34                   3.0   \n",
       "3                   other                2   39                   1.0   \n",
       "4                   other                1   45                   3.0   \n",
       "...                   ...              ...  ...                   ...   \n",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = '../../../datasets/'\n",
    "download_datasets(data_dir)\n",
    "dataset =  pd.read_csv(data_dir + 'hr_promotion/train.csv')\n",
    "dataset.drop(columns=['employee_id'], inplace=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219e2d1f",
   "metadata": {},
   "source": [
    "We can check that this dataset is imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df700e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50140\n",
       "1     4668\n",
       "Name: is_promoted, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['is_promoted'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31be917",
   "metadata": {},
   "source": [
    "We can now instantiate the class. We'll use the default values for the constructor for now, with the exception of the epochs parameter: we'll use a lower number of epochs just to run the fit method faster. But usually, the more epochs, the more the model manages to understand the structure of the dataset provided, and thus is capable of generating more realistic artificial data.\n",
    "\n",
    "The fit method will train the generative model specified. The default model used is called CTGAN, but there are also other models, which can be changed using the \"model\" parameter. The allowed values for this parameter are: [\"ctgan\", \"copula\", \"copula_gan\", \"tvae\"]. It is important to note that these models are usually large, and it takes a considerable amount of time to train them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8cb1cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/sklearn/mixture/_base.py:277: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n",
      "  warnings.warn(\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/sklearn/mixture/_base.py:143: ConvergenceWarning: Number of distinct clusters (6) found smaller than n_clusters (10). Possibly due to duplicate points in X.\n",
      "  cluster.KMeans(\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/sklearn/mixture/_base.py:277: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n",
      "  warnings.warn(\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/sklearn/mixture/_base.py:277: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n",
      "  warnings.warn(\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/ctgan/data_transformer.py:111: SettingWithCopyWarning: \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",
      "  data[column_name] = data[column_name].to_numpy().flatten()\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/ctgan/data_transformer.py:111: SettingWithCopyWarning: \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",
      "  data[column_name] = data[column_name].to_numpy().flatten()\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/ctgan/data_transformer.py:111: SettingWithCopyWarning: \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",
      "  data[column_name] = data[column_name].to_numpy().flatten()\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/ctgan/data_transformer.py:111: SettingWithCopyWarning: \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",
      "  data[column_name] = data[column_name].to_numpy().flatten()\n",
      "/home/mmendonca/anaconda3/envs/resp/lib/python3.9/site-packages/ctgan/data_transformer.py:111: SettingWithCopyWarning: \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",
      "  data[column_name] = data[column_name].to_numpy().flatten()\n"
     ]
    }
   ],
   "source": [
    "synth = Synthesizer(epochs=10)\n",
    "synth.fit(df=dataset, label_col='is_promoted')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b6d420",
   "metadata": {},
   "source": [
    "We can now generate synthetic data. First of all, let's just create a sample of synthetic data without specify anything else. We can do this using the sample() method, where we must specify the number of samples to be generated, and we can also pass a set of conditions for these samples (this last parameter is optional). For now, let's just create 500 samples without any specific set of conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ef800f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HR</td>\n",
       "      <td>region_10</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HR</td>\n",
       "      <td>region_28</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_34</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_8</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            department     region         education gender  \\\n",
       "0                   HR  region_10        Bachelor's      m   \n",
       "1           Operations  region_26        Bachelor's      f   \n",
       "2                   HR  region_28        Bachelor's      f   \n",
       "3            Analytics   region_7        Bachelor's      m   \n",
       "4           Operations  region_34        Bachelor's      m   \n",
       "..                 ...        ...               ...    ...   \n",
       "495                 HR  region_22        Bachelor's      f   \n",
       "496         Operations   region_8        Bachelor's      f   \n",
       "497         Technology   region_2  Master's & above      f   \n",
       "498         Operations  region_23        Bachelor's      m   \n",
       "499  Sales & Marketing  region_26        Bachelor's      m   \n",
       "\n",
       "    recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0              sourcing                1   28                   4.0   \n",
       "1              sourcing                1   21                   3.0   \n",
       "2                 other                1   34                   3.0   \n",
       "3              sourcing                1   50                   NaN   \n",
       "4              sourcing                2   20                   3.0   \n",
       "..                  ...              ...  ...                   ...   \n",
       "495            sourcing                1   29                   3.0   \n",
       "496               other                1   27                   NaN   \n",
       "497               other                1   34                   3.0   \n",
       "498            sourcing                4   37                   2.0   \n",
       "499               other                2   22                   NaN   \n",
       "\n",
       "     length_of_service  awards_won?  avg_training_score  is_promoted  \n",
       "0                   11            1                  52            1  \n",
       "1                   12            0                  91            0  \n",
       "2                    1            0                  64            0  \n",
       "3                    1            0                  50            1  \n",
       "4                    6            0                  57            0  \n",
       "..                 ...          ...                 ...          ...  \n",
       "495                  9            0                  67            0  \n",
       "496                  6            0                  74            0  \n",
       "497                  3            0                  50            0  \n",
       "498                  6            0                  62            0  \n",
       "499                  3            1                  95            0  \n",
       "\n",
       "[500 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample = synth.sample(500)\n",
    "df_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd0441c",
   "metadata": {},
   "source": [
    "We can also create a set of samples and automatically add it to the original dataset. For that, we use the fit_resample() method, which must receive a dataset as a mandatory parameter, along with the number of samples to be created (optional):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4c56278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    51038\n",
      "1     4770\n",
      "Name: is_promoted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_resample = synth.fit_resample(df=dataset, label_col='is_promoted', n_samples=1000)\n",
    "print(df_resample['is_promoted'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8678a379",
   "metadata": {},
   "source": [
    "If we don't specify any of the optional parameters to the transform() method, the default behavior will be to use the \"minority\" strategy, which creates synthetic data of the minority class until the number of samples of the minority class is equal to the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de8cb0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    50140\n",
      "1    50140\n",
      "Name: is_promoted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_resample = synth.fit_resample(df=dataset, label_col='is_promoted')\n",
    "print(df_resample['is_promoted'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1075b572",
   "metadata": {},
   "source": [
    "The **strategy** parameter is similar to imblearn's strategy parameter: it specifies predefined behaviors for balancing the dataset. Here is the description for this parameter:\n",
    "\n",
    "* **strategy:** represents the strategy used to generate the artificial instances. This parameter is ignored when n_samples is provided. Strategy can assume the following values:\n",
    "    - String: one of the following predefined strategies:\n",
    "        * 'minority': generates synthetic samples for only the minority class;\n",
    "        * 'not majority': generates synthetic samples for all classes but the majority class;\n",
    "        * 'auto': equivalent to 'minority';\n",
    "        Note that for a binary classification problem, \"minority\" is similar to \"not majority\";\n",
    "    - Dictionary: the dictionary must have one key for each of the possible classes found in the label column, and the value associated with each key represents the number of instances desired for that class after the undersampling process is done. Note: this parameter only works with undersampling approaches that allow controlling the number of instances to be undersampled, such as RandomUnderSampler, ClusterCentroids (from imblearn). If any other undersampler is provided in the under_sampler parameter along with a float value for the strategy_under parameter, an error will be raised;\n",
    "    - Float: a value between [0, 1] that represents the desired ratio between the number of instances of the minority class over the majority class after undersampling. The ratio 'r' is given by: $r = N_m/N_M$ where $N_m$ is the number of instances of the minority class and $N_M$ is the number of instances of the majority class after undersampling. Note: this parameter only works with undersampling approaches that allow controlling the number of instances to be undersampled, such as RandomUnderSampler, ClusterCentroids (from imblearn). If any other undersampler is provided in the under_sampler parameter along with a float value for the strategy_under parameter, an error will be raised;\n",
    "    \n",
    "    If None, the default value is set to \"auto\", which is the same as \"minority\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a39a5c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    50140\n",
      "1    50140\n",
      "Name: is_promoted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df_resample = synth.fit_resample(df=dataset, label_col='is_promoted', strategy='not majority')\n",
    "print(df_resample['is_promoted'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae1cfe1",
   "metadata": {},
   "source": [
    "Let's show an example of how to use the **conditions** parameter. Here is a description of this parameter:\n",
    "\n",
    "* **conditions:** a set of conditions, specified by a dictionary, that defines the characteristics of the synthetic instances that should be created. This parameter indicates the values for certain features that the synthetic instances should have. If None, then no restrictions will be imposed on how to generate the synthetic data;\n",
    "\n",
    "Let's use the following conditions: the **education** feature must be \"Below Secondary\" and the **is_promoted** feature must be set to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1996ce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    50140\n",
      "1     9668\n",
      "Name: is_promoted, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_16</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59808 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             department     region         education gender  \\\n",
       "0     Sales & Marketing   region_7  Master's & above      f   \n",
       "1            Operations  region_22        Bachelor's      m   \n",
       "2     Sales & Marketing  region_19        Bachelor's      m   \n",
       "3     Sales & Marketing  region_23        Bachelor's      m   \n",
       "4            Technology  region_26        Bachelor's      m   \n",
       "...                 ...        ...               ...    ...   \n",
       "4995         Operations  region_23   Below Secondary      m   \n",
       "4996         Technology   region_2   Below Secondary      f   \n",
       "4997        Procurement   region_2   Below Secondary      m   \n",
       "4998  Sales & Marketing  region_22   Below Secondary      m   \n",
       "4999        Procurement  region_16   Below Secondary      m   \n",
       "\n",
       "     recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0               sourcing                1   35                   5.0   \n",
       "1                  other                1   30                   5.0   \n",
       "2               sourcing                1   34                   3.0   \n",
       "3                  other                2   39                   1.0   \n",
       "4                  other                1   45                   3.0   \n",
       "...                  ...              ...  ...                   ...   \n",
       "4995            sourcing                1   24                   3.0   \n",
       "4996               other                1   23                   3.0   \n",
       "4997            sourcing                1   37                   3.0   \n",
       "4998            sourcing                1   22                   NaN   \n",
       "4999            sourcing                1   25                   4.0   \n",
       "\n",
       "      length_of_service  awards_won?  avg_training_score  is_promoted  \n",
       "0                     8            0                  49            0  \n",
       "1                     4            0                  60            0  \n",
       "2                     7            0                  50            0  \n",
       "3                    10            0                  50            0  \n",
       "4                     2            0                  73            0  \n",
       "...                 ...          ...                 ...          ...  \n",
       "4995                  3            0                  99            1  \n",
       "4996                  3            0                  79            1  \n",
       "4997                  1            0                  65            1  \n",
       "4998                  6            0                  57            1  \n",
       "4999                  5            0                  67            1  \n",
       "\n",
       "[59808 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditions = {\"education\": \"Below Secondary\", \"is_promoted\":1}\n",
    "df_resample = synth.fit_resample(df=dataset, label_col='is_promoted', n_samples=5000, conditions=conditions)\n",
    "print(df_resample['is_promoted'].value_counts())\n",
    "df_resample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1b049d",
   "metadata": {},
   "source": [
    "We can see that the new 5000 data instances created were automatically merged to the original dataset. This is because we are using the transform()) method. If we just want to create these samples with the predefined conditions, without the original dataset, we can use the sample() method, as previously mentioned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6322921",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    5000\n",
      "Name: is_promoted, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_15</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_8</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_9</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_17</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>HR</td>\n",
       "      <td>region_31</td>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             department     region        education gender  \\\n",
       "0     Sales & Marketing  region_15  Below Secondary      m   \n",
       "1            Technology  region_23  Below Secondary      m   \n",
       "2            Technology   region_2  Below Secondary      f   \n",
       "3     Sales & Marketing  region_27  Below Secondary      m   \n",
       "4           Procurement   region_8  Below Secondary      m   \n",
       "...                 ...        ...              ...    ...   \n",
       "4995         Operations   region_9  Below Secondary      m   \n",
       "4996  Sales & Marketing  region_17  Below Secondary      m   \n",
       "4997         Operations   region_2  Below Secondary      f   \n",
       "4998  Sales & Marketing  region_23  Below Secondary      f   \n",
       "4999                 HR  region_31  Below Secondary      m   \n",
       "\n",
       "     recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0               sourcing                1   23                   3.0   \n",
       "1                  other                1   23                   NaN   \n",
       "2               sourcing                1   37                   3.0   \n",
       "3               sourcing                1   28                   3.0   \n",
       "4                  other                1   39                   3.0   \n",
       "...                  ...              ...  ...                   ...   \n",
       "4995            sourcing                2   29                   3.0   \n",
       "4996            sourcing                1   33                   3.0   \n",
       "4997            sourcing                1   26                   4.0   \n",
       "4998               other                4   30                   3.0   \n",
       "4999               other                1   27                   3.0   \n",
       "\n",
       "      length_of_service  awards_won?  avg_training_score  is_promoted  \n",
       "0                     3            0                  53            1  \n",
       "1                     3            0                  95            1  \n",
       "2                    10            0                  93            1  \n",
       "3                     1            0                  65            1  \n",
       "4                     6            0                  77            1  \n",
       "...                 ...          ...                 ...          ...  \n",
       "4995                 14            0                  67            1  \n",
       "4996                  3            0                  66            1  \n",
       "4997                  2            0                  57            1  \n",
       "4998                 10            0                  65            1  \n",
       "4999                  1            0                  92            1  \n",
       "\n",
       "[5000 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditions = {\"education\": \"Below Secondary\", \"is_promoted\":1}\n",
    "sample_df = synth.sample(n_samples=5000, conditions=conditions)\n",
    "print(sample_df['is_promoted'].value_counts())\n",
    "sample_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be24bdb5",
   "metadata": {},
   "source": [
    "As we can see, now the sampled dataset contains only instances where the conditions are True.\n",
    "\n",
    "Note that generating data using the **conditions** parameter is slower than not using it."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('rai')",
   "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": "3.9.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "5215fd1cce9b64233b490e6c4e868a314fa8a5a2b064fe0583fd0c29542af034"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}