01_feature_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import featuretools as ft\n",
    "from IPython.display import Image\n",
    "warnings.filterwarnings('ignore')\n",
    "# !pip install featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = os.path.abspath('../../Data/home_credit_default_risk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automated Feature Engineering with *FeatureTools*\n",
    "* The goal is to do automated feature engineering with [FeatureTools](https://www.featuretools.com). \n",
    "* A dataset of Kaggle Competition, [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk/overview/description) was downloaded for testing. The dataset consists of 4 tables, and the relationship diagram is as follows. Various derived variables could be created automatically using *FeatureTools*.\n",
    "* I referenced [Will Koehrsen's post](https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219 ) and [his code on Kaggle](https://www.kaggle.com/willkoehrsen/feature-engineering-using-feature-tools)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('home_credit.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_credit_columns_description = pd.read_csv(os.path.join(DATA_DIR, 'HomeCredit_columns_description.csv'), \n",
    "                                              index_col=0, encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Table</th>\n",
       "      <th>Row</th>\n",
       "      <th>Description</th>\n",
       "      <th>Special</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>application_{train|test}.csv</td>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>ID of loan in our sample</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>application_{train|test}.csv</td>\n",
       "      <td>TARGET</td>\n",
       "      <td>Target variable (1 - client with payment diffi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>application_{train|test}.csv</td>\n",
       "      <td>NAME_CONTRACT_TYPE</td>\n",
       "      <td>Identification if loan is cash or revolving</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>application_{train|test}.csv</td>\n",
       "      <td>CODE_GENDER</td>\n",
       "      <td>Gender of the client</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>application_{train|test}.csv</td>\n",
       "      <td>FLAG_OWN_CAR</td>\n",
       "      <td>Flag if the client owns a car</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Table                 Row  \\\n",
       "1  application_{train|test}.csv          SK_ID_CURR   \n",
       "2  application_{train|test}.csv              TARGET   \n",
       "5  application_{train|test}.csv  NAME_CONTRACT_TYPE   \n",
       "6  application_{train|test}.csv         CODE_GENDER   \n",
       "7  application_{train|test}.csv        FLAG_OWN_CAR   \n",
       "\n",
       "                                         Description Special  \n",
       "1                           ID of loan in our sample     NaN  \n",
       "2  Target variable (1 - client with payment diffi...     NaN  \n",
       "5        Identification if loan is cash or revolving     NaN  \n",
       "6                               Gender of the client     NaN  \n",
       "7                      Flag if the client owns a car     NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_credit_columns_description.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train = pd.read_csv(os.path.join(DATA_DIR, 'application_train.csv')) \n",
    "application_test = pd.read_csv(os.path.join(DATA_DIR, 'application_test.csv')) \n",
    "previous_application = pd.read_csv(os.path.join(DATA_DIR, 'previous_application.csv')) \n",
    "pos_cash_balance = pd.read_csv(os.path.join(DATA_DIR, 'POS_CASH_balance.csv')) \n",
    "installments_payments = pd.read_csv(os.path.join(DATA_DIR, 'installments_payments.csv')) \n",
    "credit_card_balance = pd.read_csv(os.path.join(DATA_DIR, 'credit_card_balance.csv')) \n",
    "bureau = pd.read_csv(os.path.join(DATA_DIR, 'bureau.csv')) \n",
    "bureau_balance = pd.read_csv(os.path.join(DATA_DIR, 'bureau_balance.csv')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train['dataset'] = 'train'\n",
    "application_test['dataset'] = 'test'\n",
    "application_test['TARGET'] = -999\n",
    "application = application_train.append(application_test, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = ft.EntitySet(id='home_credit_default_risk')\n",
    "\n",
    "es = es.entity_from_dataframe(entity_id='application', dataframe=application, \n",
    "                              variable_types={'TARGET': ft.variable_types.Categorical}, index='SK_ID_CURR')\n",
    "es = es.entity_from_dataframe(entity_id='bureau', dataframe=bureau, index='SK_ID_BUREAU')\n",
    "es = es.entity_from_dataframe(entity_id='bureau_balance', dataframe=bureau_balance, make_index=True, \n",
    "                              index='bureau_balance_id')\n",
    "es = es.entity_from_dataframe(entity_id='previous_application', dataframe=previous_application, \n",
    "                              index='SK_ID_PREV')\n",
    "es = es.entity_from_dataframe(entity_id='POS_CASH_balance', dataframe=pos_cash_balance, make_index=True, \n",
    "                              index='POS_CASH_balance_id')\n",
    "es = es.entity_from_dataframe(entity_id='installments_payments', dataframe=installments_payments, make_index=True, \n",
    "                              index='installments_payments_id')\n",
    "es = es.entity_from_dataframe(entity_id='credit_card_balance', dataframe=credit_card_balance, make_index=True, \n",
    "                              index='credit_card_balance_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_bureau = ft.Relationship(es['application']['SK_ID_CURR'], es['bureau']['SK_ID_CURR'])\n",
    "r_bureau_balance = ft.Relationship(es['bureau']['SK_ID_BUREAU'], es['bureau_balance']['SK_ID_BUREAU'])\n",
    "r_previous_application = ft.Relationship(es['application']['SK_ID_CURR'], es['previous_application']['SK_ID_CURR'])\n",
    "r_pos_cash_balance = ft.Relationship(es['previous_application']['SK_ID_PREV'], es['POS_CASH_balance']['SK_ID_PREV'])\n",
    "r_installments_payments = ft.Relationship(es['previous_application']['SK_ID_PREV'], \n",
    "                                          es['installments_payments']['SK_ID_PREV'])\n",
    "r_credit_card_balance = ft.Relationship(es['previous_application']['SK_ID_PREV'], \n",
    "                                        es['credit_card_balance']['SK_ID_PREV'])\n",
    "\n",
    "es = es.add_relationships([r_bureau, r_bureau_balance, r_previous_application, r_pos_cash_balance, \n",
    "                           r_installments_payments, r_credit_card_balance])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entityset: home_credit_default_risk\n",
      "  Entities:\n",
      "    application [Rows: 356255, Columns: 123]\n",
      "    bureau [Rows: 1716428, Columns: 17]\n",
      "    bureau_balance [Rows: 27299925, Columns: 4]\n",
      "    previous_application [Rows: 1670214, Columns: 37]\n",
      "    POS_CASH_balance [Rows: 10001358, Columns: 9]\n",
      "    installments_payments [Rows: 13605401, Columns: 9]\n",
      "    credit_card_balance [Rows: 3840312, Columns: 24]\n",
      "  Relationships:\n",
      "    bureau.SK_ID_CURR -> application.SK_ID_CURR\n",
      "    bureau_balance.SK_ID_BUREAU -> bureau.SK_ID_BUREAU\n",
      "    previous_application.SK_ID_CURR -> application.SK_ID_CURR\n",
      "    POS_CASH_balance.SK_ID_PREV -> previous_application.SK_ID_PREV\n",
      "    installments_payments.SK_ID_PREV -> previous_application.SK_ID_PREV\n",
      "    credit_card_balance.SK_ID_PREV -> previous_application.SK_ID_PREV\n"
     ]
    }
   ],
   "source": [
    "print(es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gc.enable()\n",
    "del application, bureau, bureau_balance, previous_application, pos_cash_balance, installments_payments, \\\n",
    "credit_card_balance\n",
    "_ = gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built 2221 features\n",
      "Elapsed: 3:32:09 | Progress: 100%|██████████\n"
     ]
    }
   ],
   "source": [
    "trans_primitives =  ['day', 'year', 'month', 'weekday', 'haversine', 'num_words', 'num_characters']\n",
    "agg_primitives =  ['sum', 'std', 'max', 'skew', 'min', 'mean', 'count', 'percent_true', 'num_unique', 'mode']\n",
    "\n",
    "feature_matrix, feature_names = ft.dfs(entityset=es, target_entity='application', \n",
    "                                       trans_primitives=trans_primitives, agg_primitives=agg_primitives, \n",
    "                                       max_depth=2, n_jobs=1, verbose=True)\n",
    "\n",
    "feature_matrix = feature_matrix.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_matrix.to_csv(os.path.join(DATA_DIR, 'feature_matrix.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A dataset with 2,221 features for 356,255 customers was finally created. Saved as a CSV file, it is about 4GB. \n",
    "* The whole process took 3 hours and a half on my iMac with 6 cores and 16GB of memory."
   ]
  }
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