credit_default_risk/2_mortcudf_XGB_Pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################################################################\n",
    "##\n",
    "## Copyright (C) 2021 NVIDIA Corporation.  All rights reserved.\n",
    "##\n",
    "## NVIDIA Sample Code\n",
    "##\n",
    "## Please refer to the NVIDIA end user license agreement (EULA) associated\n",
    "## with this source code for terms and conditions that govern your use of\n",
    "## this software. Any use, reproduction, disclosure, or distribution of\n",
    "## this software and related documentation outside the terms of the EULA\n",
    "## is strictly prohibited.\n",
    "##\n",
    "############################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true",
    "tags": []
   },
   "source": [
    "# Modeling and Explainability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:51:17.351780Z",
     "start_time": "2021-07-15T00:51:15.545826Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1272610/937460551.py:33: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "###### https://degravek.github.io/project-pages/project1/2016/11/12/New-Notebook/ #################\n",
    "# with modifications and GPU acceleration by Mark J Bennett mbennett@nvidia.com and Emanuel Scoullos escoullos@nvidia.com\n",
    "\n",
    "###### Best when compared to:\n",
    "###### https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2019/\n",
    "######        machine-learning-explainability-in-finance-an-application-to-default-risk-analysis.pdf\n",
    "%matplotlib inline\n",
    "import time\n",
    "total_start = time.time()\n",
    "from collections import OrderedDict\n",
    "import gc\n",
    "import glob\n",
    "import os\n",
    "import pickle\n",
    "import re\n",
    "import sys\n",
    "\n",
    "from typing import Union\n",
    "\n",
    "import cudf\n",
    "import cupy as cp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import (classification_report, confusion_matrix, roc_auc_score, roc_curve, auc,\n",
    "                             precision_score, recall_score, precision_recall_curve, f1_score, \n",
    "                             accuracy_score)\n",
    "\n",
    "from tqdm.autonotebook import tqdm\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:51:17.351780Z",
     "start_time": "2021-07-15T00:51:15.545826Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using GPU\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1272610/779400077.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m#import shap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mclfmodel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbinaryClassification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_printoptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/markGPU/credit_default_risk/clfmodel.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m############################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
     ]
    }
   ],
   "source": [
    "##############\n",
    "##############\n",
    "use_cpu = False  # whether to use CPU or GPU\n",
    "\n",
    "##############\n",
    "##############\n",
    "\n",
    "if use_cpu:\n",
    "    print('using CPU')\n",
    "    from pandas import read_parquet, concat, merge, DataFrame, get_dummies, Series, isnull\n",
    "    from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "    from numpy import zeros, double, int8, int16, int64, concatenate, argmax, argmin\n",
    "else:\n",
    "    print('using GPU')\n",
    "    from cudf import read_parquet, concat, merge, DataFrame, get_dummies, Series\n",
    "    from cupy import zeros,double, int8, int16, int64, concatenate, argmax, argmin\n",
    "    from cuml.model_selection import train_test_split\n",
    "    from cuml.preprocessing import OneHotEncoder, StandardScaler\n",
    "    from cuml.metrics import precision_recall_curve, roc_auc_score, confusion_matrix, accuracy_score\n",
    "    \n",
    "import shap\n",
    "\n",
    "from clfmodel import binaryClassification\n",
    "\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# make sure the path below points to the data directory where the FannieMae data was downloaded.\n",
    "clean_data_path = '/rapids/notebooks/cleaned_data'\n",
    "figures_path = '/rapids/notebooks/figures'\n",
    "os.makedirs(figures_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cupy2numpy(obj, use_cpu):\n",
    "    if use_cpu and isinstance(obj, cp._core.core.ndarray):\n",
    "        return obj.get()\n",
    "    elif use_cpu and isinstance(obj, cudf.core.series.Series):\n",
    "        return obj.to_array()\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:51:19.993669Z",
     "start_time": "2021-07-15T00:51:19.989946Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel\n",
      "SellerName\n",
      "FTHomeBuyer\n",
      "LoanPurpose\n",
      "PropertyType\n",
      "OccStatus\n",
      "PropertyState\n",
      "Zip\n",
      "ProductType\n",
      "MortInsType\n"
     ]
    }
   ],
   "source": [
    "with open(f'{clean_data_path}/fnma_mappings.pkl', 'rb') as f:\n",
    "    mappings = pickle.load(f)\n",
    "print('\\n'.join(mappings.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Read cleaned FannieMae Dataset from notebook 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:05:19.257386Z",
     "start_time": "2021-07-14T20:05:18.024198Z"
    }
   },
   "outputs": [],
   "source": [
    "df = read_parquet(f'{clean_data_path}/fnma_cleaned.pq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-13T20:25:50.480966Z",
     "start_time": "2021-07-13T20:25:50.477282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(cudf.core.dataframe.DataFrame, (11232359, 22))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df), df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xcolumns=['Channel',\n",
    "         'SellerName',\n",
    "         'OrInterestRate',\n",
    "         'OrUnpaidPrinc',\n",
    "         'OrLoanTerm',\n",
    "         'OrCLTV',\n",
    "         'NumBorrow',\n",
    "         'DTIRat',\n",
    "         'CreditScore',\n",
    "         'FTHomeBuyer',\n",
    "         'LoanPurpose',\n",
    "         'PropertyType',\n",
    "         'NumUnits',\n",
    "         'OccStatus',\n",
    "         'PropertyState',\n",
    "         'Zip',\n",
    "         'ProductType',\n",
    "         'CoCreditScore',\n",
    "         'Extra',\n",
    "         'MortInsType']\n",
    "CONTINUOUS_COLUMNS = ['OrInterestRate', 'OrUnpaidPrinc', 'OrLoanTerm', 'OrCLTV', 'NumBorrow', 'DTIRat', \n",
    "                      'CreditScore', 'NumUnits', 'CoCreditScore', 'Extra']\n",
    "CATEGORICAL_COLUMNS = sorted([i for i in Xcolumns if i not in CONTINUOUS_COLUMNS])\n",
    "LABEL_COLUMNS = ['Default']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Predicting Delinquent Loans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Train test split and apply Random Oversampling to minority class (if desired)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:05:21.640255Z",
     "start_time": "2021-07-14T20:05:21.032244Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "y = df['Default']#.values\n",
    "X = df.drop(['Default','LoanID'], axis=1)#.values\n",
    "Xcolumns = X.columns.tolist()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state=11)\n",
    "X_train = X_train[CATEGORICAL_COLUMNS + CONTINUOUS_COLUMNS]\n",
    "X_test = X_test[CATEGORICAL_COLUMNS + CONTINUOUS_COLUMNS]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:05:21.856392Z",
     "start_time": "2021-07-14T20:05:21.851322Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10109124, 20) (10109124,)\n",
      "399797 32439\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)\n",
    "print(y_train.sum(), y_test.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:05:22.449773Z",
     "start_time": "2021-07-14T20:05:22.352774Z"
    }
   },
   "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>Channel</th>\n",
       "      <th>FTHomeBuyer</th>\n",
       "      <th>LoanPurpose</th>\n",
       "      <th>MortInsType</th>\n",
       "      <th>OccStatus</th>\n",
       "      <th>ProductType</th>\n",
       "      <th>PropertyState</th>\n",
       "      <th>PropertyType</th>\n",
       "      <th>SellerName</th>\n",
       "      <th>Zip</th>\n",
       "      <th>OrInterestRate</th>\n",
       "      <th>OrUnpaidPrinc</th>\n",
       "      <th>OrLoanTerm</th>\n",
       "      <th>OrCLTV</th>\n",
       "      <th>NumBorrow</th>\n",
       "      <th>DTIRat</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>NumUnits</th>\n",
       "      <th>CoCreditScore</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550205</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>902</td>\n",
       "      <td>5.99</td>\n",
       "      <td>93000</td>\n",
       "      <td>180</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>1</td>\n",
       "      <td>766.868035</td>\n",
       "      <td>1.077555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137124</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>120</td>\n",
       "      <td>6.50</td>\n",
       "      <td>206000</td>\n",
       "      <td>360</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1</td>\n",
       "      <td>766.868035</td>\n",
       "      <td>1.077555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541402</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>437</td>\n",
       "      <td>6.50</td>\n",
       "      <td>85000</td>\n",
       "      <td>360</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>788.0</td>\n",
       "      <td>1</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>1.077555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5374781</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>195</td>\n",
       "      <td>5.25</td>\n",
       "      <td>59000</td>\n",
       "      <td>180</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>1</td>\n",
       "      <td>766.868035</td>\n",
       "      <td>1.077555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269471</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>475</td>\n",
       "      <td>6.25</td>\n",
       "      <td>366000</td>\n",
       "      <td>360</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>1</td>\n",
       "      <td>766.868035</td>\n",
       "      <td>1.077555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Channel  FTHomeBuyer  LoanPurpose  MortInsType  OccStatus  ProductType  PropertyState  PropertyType  SellerName  Zip  OrInterestRate  OrUnpaidPrinc  OrLoanTerm  OrCLTV  NumBorrow  DTIRat  CreditScore  NumUnits  CoCreditScore     Extra\n",
       "1550205        2            0            2            0          1            0              4             4          31  902            5.99          93000         180    19.0        1.0    23.0        805.0         1     766.868035  1.077555\n",
       "1137124        1            0            0            0          1            0             21             3          48  120            6.50         206000         360    58.0        1.0    64.0        649.0         1     766.868035  1.077555\n",
       "541402         2            0            0            0          1            0             23             4          31  437            6.50          85000         360    25.0        2.0    35.0        788.0         1     799.000000  1.077555\n",
       "5374781        2            0            2            0          0            0             28             3           3  195            5.25          59000         180    74.0        1.0    38.0        676.0         1     766.868035  1.077555\n",
       "2269471        1            0            2            0          1            0             51             4           3  475            6.25         366000         360    79.0        1.0    48.0        770.0         1     766.868035  1.077555"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prep_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, target_names, title='Confusion Matrix', save_path=None, cmap=plt.cm.cool):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(target_names))\n",
    "    plt.xticks(tick_marks, target_names, rotation=45)\n",
    "    plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    width, height = cm.shape\n",
    "\n",
    "    for x in range(width):\n",
    "        for y in range(height):\n",
    "            plt.annotate(str(cm[x][y]), xy=(y, x), \n",
    "                        horizontalalignment='center',\n",
    "                        verticalalignment='center')\n",
    "    plt.ylabel('True Label')\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.tight_layout()\n",
    "    if save_path and isinstance(save_path, str):\n",
    "        plt.savefig(save_path, dpi=600)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, now we’re ready to make some predictions! We first randomly split the data into a training set and a test set using the Scikit-Learn train_test_split_function. From these two sets, we idenfiy the target (“Default”) vector, and feature arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:06:25.263450Z",
     "start_time": "2021-07-14T20:06:25.258841Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.039548134932364074"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sum()/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:05:24.135141Z",
     "start_time": "2021-07-14T20:05:24.130299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaling: 24.285642463550253\n"
     ]
    }
   ],
   "source": [
    "scaling = (len(y_train)-y_train.sum())/y_train.sum()\n",
    "scaling = float(scaling)\n",
    "\n",
    "print('scaling:', scaling )   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:05:25.544216Z",
     "start_time": "2021-07-14T20:05:25.540293Z"
    }
   },
   "outputs": [],
   "source": [
    "# parameters here are not optimized. Feel free to experiment!\n",
    "params = {\n",
    "    'num_rounds':        100,\n",
    "    'max_depth':         12,\n",
    "    'max_leaves':        0,\n",
    "    'alpha':             3,\n",
    "    'lambda':            1,\n",
    "    'eta':               0.17,\n",
    "    'subsample':         1,\n",
    "    'sampling_method':   'gradient_based',\n",
    "    'scale_pos_weight':  scaling,\n",
    "    'max_delta_step':    1,\n",
    "    'max_bin':           2048,\n",
    "    'tree_method':       'gpu_hist',\n",
    "    'grow_policy':       'lossguide',\n",
    "    'n_gpus':            1,\n",
    "    'objective':         'binary:logistic',#'binary:hinge'\n",
    "    'eval_metric':       'aucpr',#'error',#'auc', 'logloss'\n",
    "    'predictor':         'gpu_predictor',\n",
    "    'num_parallel_tree': 1,\n",
    "    \"min_child_weight\":  2,\n",
    "    'verbose':           True\n",
    "}\n",
    "\n",
    "if use_cpu:\n",
    "    print('training XGBoost model on cpu')\n",
    "    params['tree_method'] = 'hist'\n",
    "    params['sampling_method'] = 'uniform'\n",
    "    params['predictor'] = 'cpu_predictor'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:06:17.885437Z",
     "start_time": "2021-07-14T20:06:17.735664Z"
    }
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "dtest = xgb.DMatrix(X_test, label=y_test)\n",
    "    \n",
    "evals = [(dtest, 'test'), (dtrain, 'train')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:07:54.574531Z",
     "start_time": "2021-07-14T20:06:30.373248Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22:27:03] WARNING: /opt/conda/envs/rapids/conda-bld/xgboost_1623267594558/work/include/xgboost/generic_parameters.h:38: \n",
      "n_gpus: \n",
      "\tDeprecated. Single process multi-GPU training is no longer supported.\n",
      "\tPlease switch to distributed training with one process per GPU.\n",
      "\tThis can be done using Dask or Spark.  See documentation for details.\n",
      "[22:27:03] WARNING: /opt/conda/envs/rapids/conda-bld/xgboost_1623267594558/work/src/learner.cc:573: \n",
      "Parameters: { \"num_rounds\", \"verbose\" } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\ttest-aucpr:0.16717\ttrain-aucpr:0.18319\n",
      "[1]\ttest-aucpr:0.18538\ttrain-aucpr:0.20131\n",
      "[2]\ttest-aucpr:0.20371\ttrain-aucpr:0.22097\n",
      "[3]\ttest-aucpr:0.22488\ttrain-aucpr:0.24358\n",
      "[4]\ttest-aucpr:0.24571\ttrain-aucpr:0.26775\n",
      "[5]\ttest-aucpr:0.26445\ttrain-aucpr:0.28947\n",
      "[6]\ttest-aucpr:0.28098\ttrain-aucpr:0.30799\n",
      "[7]\ttest-aucpr:0.29790\ttrain-aucpr:0.32879\n",
      "[8]\ttest-aucpr:0.30841\ttrain-aucpr:0.34197\n",
      "[9]\ttest-aucpr:0.31448\ttrain-aucpr:0.35073\n",
      "[10]\ttest-aucpr:0.31892\ttrain-aucpr:0.35649\n",
      "[11]\ttest-aucpr:0.32189\ttrain-aucpr:0.36103\n",
      "[12]\ttest-aucpr:0.32468\ttrain-aucpr:0.36470\n",
      "[13]\ttest-aucpr:0.32751\ttrain-aucpr:0.36847\n",
      "[14]\ttest-aucpr:0.32925\ttrain-aucpr:0.37074\n",
      "[15]\ttest-aucpr:0.33097\ttrain-aucpr:0.37435\n",
      "[16]\ttest-aucpr:0.33244\ttrain-aucpr:0.37724\n",
      "[17]\ttest-aucpr:0.33337\ttrain-aucpr:0.37947\n",
      "[18]\ttest-aucpr:0.33459\ttrain-aucpr:0.38204\n",
      "[19]\ttest-aucpr:0.33559\ttrain-aucpr:0.38411\n",
      "[20]\ttest-aucpr:0.33754\ttrain-aucpr:0.38681\n",
      "[21]\ttest-aucpr:0.33873\ttrain-aucpr:0.38872\n",
      "[22]\ttest-aucpr:0.34043\ttrain-aucpr:0.39092\n",
      "[23]\ttest-aucpr:0.34167\ttrain-aucpr:0.39285\n",
      "[24]\ttest-aucpr:0.34266\ttrain-aucpr:0.39434\n",
      "[25]\ttest-aucpr:0.34326\ttrain-aucpr:0.39557\n",
      "[26]\ttest-aucpr:0.34412\ttrain-aucpr:0.39712\n",
      "[27]\ttest-aucpr:0.34482\ttrain-aucpr:0.39861\n",
      "[28]\ttest-aucpr:0.34550\ttrain-aucpr:0.39963\n",
      "[29]\ttest-aucpr:0.34611\ttrain-aucpr:0.40062\n",
      "[30]\ttest-aucpr:0.34652\ttrain-aucpr:0.40157\n",
      "[31]\ttest-aucpr:0.34676\ttrain-aucpr:0.40310\n",
      "[32]\ttest-aucpr:0.34820\ttrain-aucpr:0.40507\n",
      "[33]\ttest-aucpr:0.34881\ttrain-aucpr:0.40632\n",
      "[34]\ttest-aucpr:0.34921\ttrain-aucpr:0.40728\n",
      "[35]\ttest-aucpr:0.34975\ttrain-aucpr:0.40844\n",
      "[36]\ttest-aucpr:0.35030\ttrain-aucpr:0.40960\n",
      "[37]\ttest-aucpr:0.35109\ttrain-aucpr:0.41050\n",
      "[38]\ttest-aucpr:0.35167\ttrain-aucpr:0.41186\n",
      "[39]\ttest-aucpr:0.35207\ttrain-aucpr:0.41271\n",
      "[40]\ttest-aucpr:0.35228\ttrain-aucpr:0.41325\n",
      "[41]\ttest-aucpr:0.35269\ttrain-aucpr:0.41382\n",
      "[42]\ttest-aucpr:0.35295\ttrain-aucpr:0.41454\n",
      "[43]\ttest-aucpr:0.35350\ttrain-aucpr:0.41531\n",
      "[44]\ttest-aucpr:0.35370\ttrain-aucpr:0.41617\n",
      "[45]\ttest-aucpr:0.35392\ttrain-aucpr:0.41676\n",
      "[46]\ttest-aucpr:0.35402\ttrain-aucpr:0.41702\n",
      "[47]\ttest-aucpr:0.35427\ttrain-aucpr:0.41734\n",
      "[48]\ttest-aucpr:0.35444\ttrain-aucpr:0.41809\n",
      "[49]\ttest-aucpr:0.35464\ttrain-aucpr:0.41867\n",
      "[50]\ttest-aucpr:0.35476\ttrain-aucpr:0.41926\n",
      "[51]\ttest-aucpr:0.35494\ttrain-aucpr:0.41958\n",
      "[52]\ttest-aucpr:0.35503\ttrain-aucpr:0.41991\n",
      "[53]\ttest-aucpr:0.35532\ttrain-aucpr:0.42077\n",
      "[54]\ttest-aucpr:0.35540\ttrain-aucpr:0.42140\n",
      "[55]\ttest-aucpr:0.35557\ttrain-aucpr:0.42182\n",
      "[56]\ttest-aucpr:0.35574\ttrain-aucpr:0.42212\n",
      "[57]\ttest-aucpr:0.35574\ttrain-aucpr:0.42243\n",
      "[58]\ttest-aucpr:0.35585\ttrain-aucpr:0.42284\n",
      "[59]\ttest-aucpr:0.35600\ttrain-aucpr:0.42331\n",
      "[60]\ttest-aucpr:0.35610\ttrain-aucpr:0.42358\n",
      "[61]\ttest-aucpr:0.35630\ttrain-aucpr:0.42409\n",
      "[62]\ttest-aucpr:0.35641\ttrain-aucpr:0.42471\n",
      "[63]\ttest-aucpr:0.35647\ttrain-aucpr:0.42490\n",
      "[64]\ttest-aucpr:0.35655\ttrain-aucpr:0.42515\n",
      "[65]\ttest-aucpr:0.35657\ttrain-aucpr:0.42540\n",
      "[66]\ttest-aucpr:0.35661\ttrain-aucpr:0.42563\n",
      "[67]\ttest-aucpr:0.35661\ttrain-aucpr:0.42624\n",
      "[68]\ttest-aucpr:0.35662\ttrain-aucpr:0.42633\n",
      "[69]\ttest-aucpr:0.35666\ttrain-aucpr:0.42649\n",
      "[70]\ttest-aucpr:0.35660\ttrain-aucpr:0.42663\n",
      "[71]\ttest-aucpr:0.35672\ttrain-aucpr:0.42720\n",
      "[72]\ttest-aucpr:0.35685\ttrain-aucpr:0.42761\n",
      "[73]\ttest-aucpr:0.35690\ttrain-aucpr:0.42781\n",
      "[74]\ttest-aucpr:0.35744\ttrain-aucpr:0.42885\n",
      "[75]\ttest-aucpr:0.35747\ttrain-aucpr:0.42898\n",
      "[76]\ttest-aucpr:0.35748\ttrain-aucpr:0.42928\n",
      "[77]\ttest-aucpr:0.35754\ttrain-aucpr:0.42960\n",
      "[78]\ttest-aucpr:0.35752\ttrain-aucpr:0.42992\n",
      "[79]\ttest-aucpr:0.35754\ttrain-aucpr:0.43016\n",
      "[80]\ttest-aucpr:0.35760\ttrain-aucpr:0.43030\n",
      "[81]\ttest-aucpr:0.35767\ttrain-aucpr:0.43070\n",
      "[82]\ttest-aucpr:0.35767\ttrain-aucpr:0.43089\n",
      "[83]\ttest-aucpr:0.35772\ttrain-aucpr:0.43133\n",
      "[84]\ttest-aucpr:0.35775\ttrain-aucpr:0.43144\n",
      "[85]\ttest-aucpr:0.35790\ttrain-aucpr:0.43200\n",
      "[86]\ttest-aucpr:0.35793\ttrain-aucpr:0.43228\n",
      "[87]\ttest-aucpr:0.35794\ttrain-aucpr:0.43242\n",
      "[88]\ttest-aucpr:0.35799\ttrain-aucpr:0.43266\n",
      "[89]\ttest-aucpr:0.35798\ttrain-aucpr:0.43278\n",
      "[90]\ttest-aucpr:0.35802\ttrain-aucpr:0.43291\n",
      "[91]\ttest-aucpr:0.35796\ttrain-aucpr:0.43330\n",
      "[92]\ttest-aucpr:0.35796\ttrain-aucpr:0.43345\n",
      "[93]\ttest-aucpr:0.35794\ttrain-aucpr:0.43357\n",
      "[94]\ttest-aucpr:0.35803\ttrain-aucpr:0.43397\n",
      "[95]\ttest-aucpr:0.35803\ttrain-aucpr:0.43406\n",
      "[96]\ttest-aucpr:0.35803\ttrain-aucpr:0.43416\n",
      "[97]\ttest-aucpr:0.35810\ttrain-aucpr:0.43444\n",
      "[98]\ttest-aucpr:0.35823\ttrain-aucpr:0.43482\n",
      "[99]\ttest-aucpr:0.35825\ttrain-aucpr:0.43516\n",
      "Elapsed time:  59.289 s\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "model = xgb.train(params, dtrain, params['num_rounds'], evals=evals,\n",
    "                          early_stopping_rounds=10)\n",
    "print(f'Elapsed time: {time.time() - start: 0.3f} s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model(f'{clean_data_path}/xgb_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:08:19.561530Z",
     "start_time": "2021-07-14T20:08:19.090841Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold:  0.8486\n",
      "Precision:  0.3536\n",
      "Recall:  0.4813\n",
      "Predicted 44170\n",
      "Actual 32453\n",
      "AUC  0.9229\n",
      "Accuracy:  0.9596\n",
      "F1:  0.4077\n",
      "AUPRC:  0.3592\n"
     ]
    }
   ],
   "source": [
    "# make predictions on the test set using our trained model\n",
    "cp.cuda.Device(0).use()\n",
    "use_f1_measure = True\n",
    "\n",
    "preds = model.predict(dtest)\n",
    "if isinstance(preds, np.ndarray) and not use_cpu:\n",
    "    preds = cp.array(preds)\n",
    "    \n",
    "true_labels = y_test.astype(int).values.squeeze()\n",
    "true_labels = cupy2numpy(true_labels, use_cpu)\n",
    "precision, recall, thresholds = precision_recall_curve(true_labels, preds)\n",
    "\n",
    "if use_f1_measure:\n",
    "    # apply the threshold using F1 measure to the prediction.\n",
    "    threshold_idx = cupy2numpy(np.argmax((2 * precision * recall)/ (precision + recall+1e-7)), True)\n",
    "    pred_labels = (preds >= thresholds[threshold_idx]).astype(int)\n",
    "else:\n",
    "    # apply the threshold TPR-FPR to the prediction.\n",
    "    fpr, tpr, thresholds = roc_curve(cupy2numpy(true_labels, True), cupy2numpy(preds, True))\n",
    "    threshold_idx = cupy2numpy(np.argmax(tpr - fpr), True)\n",
    "    pred_labels = (preds >= thresholds[threshold_idx]).astype(int)\n",
    "    print(f'TPR: {tpr[threshold_idx]: 0.4f}')\n",
    "    print(f'FPR: {fpr[threshold_idx]: 0.4f}')\n",
    "    \n",
    "    \n",
    "print(f'Threshold: {thresholds[threshold_idx]: 0.4f}')\n",
    "print(f'Precision: {precision[threshold_idx]: 0.4f}')\n",
    "print(f'Recall: {recall[threshold_idx]: 0.4f}')\n",
    "print('Predicted', pred_labels.sum())\n",
    "print('Actual', true_labels.sum())\n",
    "# compute the auc, this should match the last test auc output above\n",
    "#\n",
    "auc_measure = roc_auc_score(true_labels, preds)\n",
    "\n",
    "print(f'AUC {auc_measure: 0.4f}')\n",
    "\n",
    "print (f'Accuracy: {accuracy_score(true_labels, pred_labels): 0.4f}')\n",
    "\n",
    "print (f'F1: {(2 * precision[threshold_idx] * recall[threshold_idx]) / (precision[threshold_idx] + recall[threshold_idx]): 0.4f}')\n",
    "recall = cupy2numpy(recall, True)\n",
    "precision = cupy2numpy(precision, True)\n",
    "# area under precision recall curve\n",
    "auprc= auc(recall, precision)\n",
    "print(f'AUPRC: {auprc: 0.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:08:22.415065Z",
     "start_time": "2021-07-14T20:08:21.728655Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "no_skill = y_test.sum()/len(y_test)\n",
    "ax[0].plot(recall, precision, color='darkorange', lw=1, label='PR curve (area = %0.2f)' % auprc)\n",
    "ax[0].plot([0, 1], [no_skill, no_skill], color='navy', linestyle='--', label='No Skill')\n",
    "\n",
    "ax[0].plot(recall[threshold_idx],\n",
    "           precision[threshold_idx], 'ro', label='F1 threshold')\n",
    "\n",
    "ax[0].set_xlabel('Recall (Positive Label: 1)')\n",
    "ax[0].set_ylabel('Precision (Positive Label: 1)')\n",
    "ax[0].set_title('2-class Precision-Recall curve')\n",
    "ax[0].set_xlim([-0.02, 1.02])\n",
    "ax[0].set_ylim([-0.02, 1.02])\n",
    "ax[0].legend()\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(cupy2numpy(true_labels, True), cupy2numpy(preds, True))\n",
    "# roc_auc = roc_auc_score(true_labels, pred_labels)\n",
    "roc_auc = roc_auc_score(true_labels, preds)\n",
    "ax[1].plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "ax[1].plot([-0, 1], [0, 1], color='navy', linestyle='--')\n",
    "ax[1].set_xlim([-0.02, 1.02])\n",
    "ax[1].set_ylim([-0.02, 1.02])\n",
    "ax[1].set_xlabel('False Positive Rate')\n",
    "ax[1].set_ylabel('True Positive Rate')\n",
    "ax[1].set_title('Receiver operating characteristic')\n",
    "ax[1].legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{figures_path}/xgb_pr_roc_curves.png', dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:08:24.505069Z",
     "start_time": "2021-07-14T20:08:24.188926Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = confusion_matrix(true_labels, pred_labels)\n",
    "results = cupy2numpy(results, True)\n",
    "\n",
    "print ('Confusion Matrix :')\n",
    "\n",
    "plot_confusion_matrix(results, ['Normal','Anomaly'], title='Confusion Matrix', save_path=f'{figures_path}/xgb_confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:08:26.876032Z",
     "start_time": "2021-07-14T20:08:26.872248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1123235, 1123235)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(true_labels), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cupy._core.core.ndarray"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:08:33.797781Z",
     "start_time": "2021-07-14T20:08:32.723305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for XGBoost Classifier: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98   1090782\n",
      "           1       0.35      0.48      0.41     32453\n",
      "\n",
      "    accuracy                           0.96   1123235\n",
      "   macro avg       0.67      0.73      0.69   1123235\n",
      "weighted avg       0.97      0.96      0.96   1123235\n",
      "\n",
      "Confusion Matrix of XGBoost Classifier: \n",
      " [[1062232   28550]\n",
      " [  16833   15620]]\n"
     ]
    }
   ],
   "source": [
    "# flag\n",
    "print(\"Classification Report for XGBoost Classifier: \\n\", \n",
    "      classification_report(cupy2numpy(y_test, not use_cpu), cupy2numpy(pred_labels, not use_cpu)))\n",
    "print(\"Confusion Matrix of XGBoost Classifier: \\n\", \n",
    "      confusion_matrix(cupy2numpy(y_test, not use_cpu), cupy2numpy(pred_labels, not use_cpu)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Explainability with Shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:08:46.709127Z",
     "start_time": "2021-07-14T20:08:45.254006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = plt.rcParams[\"figure.figsize\"] = [10,6]\n",
    "fig, ax = plt.subplots()\n",
    "xgb.plot_importance(model, max_num_features=10, ax = ax)\n",
    "plt.savefig(f'{figures_path}/xgb_feat_importance.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:09:06.391330Z",
     "start_time": "2021-07-14T20:09:06.385744Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function for plotting Precision Recall curve and measuring AUC\n",
    "def plot_precision_recall(y_test: np.array, y_score: np.array, isPR = True) -> None:\n",
    "    #print(y_test[0:20],y_score[0:20])\n",
    "    precision, recall, _ = precision_recall_curve(y_test, y_score) if isPR else roc_curve(y_test, y_score)\n",
    "    auc_score = auc(recall, precision)\n",
    "    plt.step(recall, precision, color='b', alpha=0.2,\n",
    "             where='post')\n",
    "    plt.fill_between(recall, precision, alpha=0.2, color='b')\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlim([0.0, 1.05])\n",
    "    plt.title('Precision-Recall curve: AUC={0:0.4f}'.format(auc_score))\n",
    "    plt.show()\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [10,6]\n",
    "plot_precision_recall(cupy2numpy(y_test.values, not use_cpu), cupy2numpy(preds, True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the key step of computing GPU-accelerated Shapley values for all rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:00.191496Z",
     "start_time": "2021-07-14T20:16:27.832036Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed:  79.1308 sec\n",
      "(1123235, 20)\n",
      "XGBoost version: 1.4.2\n"
     ]
    }
   ],
   "source": [
    "## shap Tree Explainer\n",
    "start = time.time()\n",
    "    \n",
    "expl = shap.TreeExplainer(model)\n",
    "shap_values = expl.shap_values(X_test)\n",
    "tshap = time.time()-start\n",
    "print(f'Elapsed: {time.time()-start: 0.4f} sec')\n",
    "print(shap_values.shape)\n",
    "print(f'XGBoost version: {xgb.__version__}')\n",
    "\n",
    "start = time.time()\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(f'{clean_data_path}/xgb_shap.npy', shap_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save for dashboarding\n",
    "X_train.to_parquet(f'{clean_data_path}/xgb_xtrain.parquet')\n",
    "y_train.to_frame().to_parquet(f'{clean_data_path}/xgb_ytrain.parquet')\n",
    "\n",
    "X_test.to_parquet(f'{clean_data_path}/xgb_xtest.parquet')\n",
    "y_test.to_frame().to_parquet(f'{clean_data_path}/xgb_ytest.parquet')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:00.191496Z",
     "start_time": "2021-07-14T20:16:27.832036Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "  if use_cpu: \n",
    "    shap.summary_plot(shap_values, X_test)\n",
    "  else: \n",
    "    shap.summary_plot(shap_values, X_test.to_pandas(), sort=False, show=False)\n",
    "    \n",
    "  plt.tight_layout()\n",
    "\n",
    "if False:\n",
    "    ax = f.gca()\n",
    "    ax.set_xlim([-5, 5])\n",
    "\n",
    "    f = plt.gcf()\n",
    "    f.savefig(f'{figures_path}/Shap_XGBoost_1200.png', dpi=6000)\n",
    "    print(f'Elapsed: {time.time()-start: 0.4f} sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the magnitude of the OrInterestRate and CreditScore on Default = 1 rows below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:51:24.355593Z",
     "start_time": "2021-07-15T00:51:24.348029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "EPOCHS = 20\n",
    "BATCH_SIZE = 2048\n",
    "LEARNING_RATE = 0.0001\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() and not use_cpu else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyt_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:51:26.008118Z",
     "start_time": "2021-07-15T00:51:26.003721Z"
    }
   },
   "outputs": [],
   "source": [
    "Xcolumns=['Channel',\n",
    "         'SellerName',\n",
    "         'OrInterestRate',\n",
    "         'OrUnpaidPrinc',\n",
    "         'OrLoanTerm',\n",
    "         'OrCLTV',\n",
    "         'NumBorrow',\n",
    "         'DTIRat',\n",
    "         'CreditScore',\n",
    "         'FTHomeBuyer',\n",
    "         'LoanPurpose',\n",
    "         'PropertyType',\n",
    "         'NumUnits',\n",
    "         'OccStatus',\n",
    "         'PropertyState',\n",
    "         'Zip',\n",
    "         'ProductType',\n",
    "         'CoCreditScore',\n",
    "         'Extra',\n",
    "         'MortInsType']\n",
    "CONTINUOUS_COLUMNS = ['OrInterestRate', 'OrUnpaidPrinc', 'OrLoanTerm', 'OrCLTV', 'NumBorrow', 'DTIRat', \n",
    "                      'CreditScore', 'NumUnits', 'CoCreditScore', 'Extra']\n",
    "CATEGORICAL_COLUMNS = sorted([i for i in Xcolumns if i not in CONTINUOUS_COLUMNS])\n",
    "LABEL_COLUMNS = ['Default']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Scale numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:22.960071Z",
     "start_time": "2021-07-14T20:20:22.957425Z"
    }
   },
   "outputs": [],
   "source": [
    "std_scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:23.629305Z",
     "start_time": "2021-07-14T20:20:23.596343Z"
    }
   },
   "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>OrInterestRate</th>\n",
       "      <th>OrUnpaidPrinc</th>\n",
       "      <th>OrLoanTerm</th>\n",
       "      <th>OrCLTV</th>\n",
       "      <th>NumBorrow</th>\n",
       "      <th>DTIRat</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>NumUnits</th>\n",
       "      <th>CoCreditScore</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550205</th>\n",
       "      <td>1.117746</td>\n",
       "      <td>-0.996131</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>-2.909084</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>-0.904846</td>\n",
       "      <td>0.985658</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137124</th>\n",
       "      <td>1.616116</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-0.650462</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>2.717158</td>\n",
       "      <td>-2.276635</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541402</th>\n",
       "      <td>1.616116</td>\n",
       "      <td>-1.060646</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-2.561604</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>0.155253</td>\n",
       "      <td>0.630152</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1.013539</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5374781</th>\n",
       "      <td>0.394622</td>\n",
       "      <td>-1.270318</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>0.276153</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>0.420278</td>\n",
       "      <td>-1.712007</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269471</th>\n",
       "      <td>1.371817</td>\n",
       "      <td>1.205431</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>0.565720</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>1.303693</td>\n",
       "      <td>0.253734</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         OrInterestRate  OrUnpaidPrinc  OrLoanTerm    OrCLTV  NumBorrow    DTIRat  CreditScore  NumUnits  CoCreditScore     Extra\n",
       "1550205        1.117746      -0.996131   -1.531372 -2.909084  -1.164326 -0.904846     0.985658 -0.133009       0.004986 -0.000487\n",
       "1137124        1.616116      -0.084862    0.637020 -0.650462  -1.164326  2.717158    -2.276635 -0.133009       0.004986 -0.000487\n",
       "541402         1.616116      -1.060646    0.637020 -2.561604   0.816595  0.155253     0.630152 -0.133009       1.013539 -0.000487\n",
       "5374781        0.394622      -1.270318   -1.531372  0.276153  -1.164326  0.420278    -1.712007 -0.133009       0.004986 -0.000487\n",
       "2269471        1.371817       1.205431    0.637020  0.565720  -1.164326  1.303693     0.253734 -0.133009       0.004986 -0.000487"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[CONTINUOUS_COLUMNS].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:25.355141Z",
     "start_time": "2021-07-14T20:20:24.084254Z"
    },
    "scrolled": true
   },
   "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>OrInterestRate</th>\n",
       "      <th>OrUnpaidPrinc</th>\n",
       "      <th>OrLoanTerm</th>\n",
       "      <th>OrCLTV</th>\n",
       "      <th>NumBorrow</th>\n",
       "      <th>DTIRat</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>NumUnits</th>\n",
       "      <th>CoCreditScore</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550205</th>\n",
       "      <td>1.117746</td>\n",
       "      <td>-0.996131</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>-2.909084</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>-0.904846</td>\n",
       "      <td>0.985658</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137124</th>\n",
       "      <td>1.616116</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-0.650462</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>2.717158</td>\n",
       "      <td>-2.276635</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541402</th>\n",
       "      <td>1.616116</td>\n",
       "      <td>-1.060646</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-2.561604</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>0.155253</td>\n",
       "      <td>0.630152</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1.013539</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5374781</th>\n",
       "      <td>0.394622</td>\n",
       "      <td>-1.270318</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>0.276153</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>0.420278</td>\n",
       "      <td>-1.712007</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269471</th>\n",
       "      <td>1.371817</td>\n",
       "      <td>1.205431</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>0.565720</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>1.303693</td>\n",
       "      <td>0.253734</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-0.000487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         OrInterestRate  OrUnpaidPrinc  OrLoanTerm    OrCLTV  NumBorrow    DTIRat  CreditScore  NumUnits  CoCreditScore     Extra\n",
       "1550205        1.117746      -0.996131   -1.531372 -2.909084  -1.164326 -0.904846     0.985658 -0.133009       0.004986 -0.000487\n",
       "1137124        1.616116      -0.084862    0.637020 -0.650462  -1.164326  2.717158    -2.276635 -0.133009       0.004986 -0.000487\n",
       "541402         1.616116      -1.060646    0.637020 -2.561604   0.816595  0.155253     0.630152 -0.133009       1.013539 -0.000487\n",
       "5374781        0.394622      -1.270318   -1.531372  0.276153  -1.164326  0.420278    -1.712007 -0.133009       0.004986 -0.000487\n",
       "2269471        1.371817       1.205431    0.637020  0.565720  -1.164326  1.303693     0.253734 -0.133009       0.004986 -0.000487"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit and transform data on train set\n",
    "scaled_col_data = std_scaler.fit_transform(X_train[CONTINUOUS_COLUMNS])\n",
    "\n",
    "if not use_cpu:\n",
    "    scaled_col_data = scaled_col_data.values\n",
    "\n",
    "for col_num, col in enumerate(CONTINUOUS_COLUMNS):\n",
    "    X_train[col] = scaled_col_data[:, col_num]\n",
    "\n",
    "del scaled_col_data\n",
    "X_train[CONTINUOUS_COLUMNS].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:26.659284Z",
     "start_time": "2021-07-14T20:20:26.604878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(OrInterestRate    0\n",
       " OrUnpaidPrinc     0\n",
       " OrLoanTerm        0\n",
       " OrCLTV            0\n",
       " NumBorrow         0\n",
       " DTIRat            0\n",
       " CreditScore       0\n",
       " NumUnits          0\n",
       " CoCreditScore     0\n",
       " Extra             0\n",
       " dtype: uint64,\n",
       " OrInterestRate    0\n",
       " OrUnpaidPrinc     0\n",
       " OrLoanTerm        0\n",
       " OrCLTV            0\n",
       " NumBorrow         0\n",
       " DTIRat            0\n",
       " CreditScore       0\n",
       " NumUnits          0\n",
       " CoCreditScore     0\n",
       " Extra             0\n",
       " dtype: uint64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[CONTINUOUS_COLUMNS].isna().sum(), X_test[CONTINUOUS_COLUMNS].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:28.746527Z",
     "start_time": "2021-07-14T20:20:28.688524Z"
    }
   },
   "outputs": [],
   "source": [
    "# transform data on test set\n",
    "test_scaled_col_data = std_scaler.transform(X_test[CONTINUOUS_COLUMNS])\n",
    "if not use_cpu:\n",
    "    test_scaled_col_data = test_scaled_col_data.values\n",
    "    \n",
    "for col_num, col in enumerate(CONTINUOUS_COLUMNS):\n",
    "    X_test[col] = test_scaled_col_data[:, col_num]\n",
    "del test_scaled_col_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:29.378545Z",
     "start_time": "2021-07-14T20:20:29.366774Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1550205     0\n",
       "1137124     1\n",
       "541402      0\n",
       "5374781     0\n",
       "2269471     0\n",
       "           ..\n",
       "9214207     0\n",
       "10613369    0\n",
       "4343224     0\n",
       "8759119     0\n",
       "2998321     0\n",
       "Name: Default, Length: 10109124, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### OPTIONAL: Apply random over sampler of minority class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-13T14:13:13.502615Z",
     "start_time": "2021-07-13T14:13:09.597506Z"
    }
   },
   "outputs": [],
   "source": [
    "# from imblearn.over_sampling import RandomOverSampler\n",
    "# from imblearn.combine import SMOTEENN\n",
    "\n",
    "# #oversampler = SMOTEENN()\n",
    "# oversampler = RandomOverSampler(sampling_strategy=.90) #was: .90 Apply to only X_train, y_train\n",
    "# if not use_cpu:\n",
    "#     X_train, y_train = oversampler.fit_resample(X_train, y_train)\n",
    "# else:\n",
    "#     X_train, y_train = oversampler.fit_resample(X_train.as_matrix(), cp.asnumpy(y_train))\n",
    "#     X_train = DataFrame(X_train, columns=CATEGORICAL_COLUMNS + CONTINUOUS_COLUMNS)\n",
    "#     y_train = Series(y_train, name=LABEL_COLUMNS[0])\n",
    "\n",
    "# print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:51:32.002158Z",
     "start_time": "2021-07-15T00:51:31.996466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel 3\n",
      "FTHomeBuyer 3\n",
      "LoanPurpose 4\n",
      "MortInsType 2\n",
      "OccStatus 3\n",
      "ProductType 1\n",
      "PropertyState 54\n",
      "PropertyType 5\n",
      "SellerName 49\n",
      "Zip 961\n"
     ]
    }
   ],
   "source": [
    "cards = []\n",
    "for key in sorted(mappings):\n",
    "    print(key, len(set(mappings[key])))\n",
    "    cards.append(len(set(mappings[key])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############################################################################\n",
      "##\n",
      "## Copyright (C) 2021 NVIDIA Corporation.  All rights reserved.\n",
      "##\n",
      "## NVIDIA Sample Code\n",
      "##\n",
      "## Please refer to the NVIDIA end user license agreement (EULA) associated\n",
      "## with this source code for terms and conditions that govern your use of\n",
      "## this software. Any use, reproduction, disclosure, or distribution of\n",
      "## this software and related documentation outside the terms of the EULA\n",
      "## is strictly prohibited.\n",
      "##\n",
      "############################################################################\n",
      "\n",
      "import torch\n",
      "import torch.nn as nn\n",
      "\n",
      "class binaryClassification(nn.Module):\n",
      "    def __init__(self, cat_cards, numer_dims=10):\n",
      "        \"\"\"\n",
      "        cat_cards (list): list of integers, where each integer is the cardinality of the SORTED column names\n",
      "        numer_dims (int): number of numerical dimensions\n",
      "        \"\"\"\n",
      "        super(binaryClassification, self).__init__()\n",
      "        self.num_cats = len(cat_cards)\n",
      "        \n",
      "#         Uncomment lines below to enable categorical embeddings as well as line in forward method\n",
      "#         self.embeddings = nn.ModuleList([nn.Embedding(cat_card, min(50, cat_card//2 + 1)) for \n",
      "#                                          cat_card in cat_cards])\n",
      "        \n",
      "#         total_embed_dims = sum(i.embedding_dim for i in self.embeddings)\n",
      "        \n",
      "        # Number of input features is X_train.shape[1].\n",
      "#         self.layer_1 = nn.Linear(numer_dims + total_embed_dims, 512)\n",
      "\n",
      "        self.layer_1 = nn.Linear(self.num_cats +  numer_dims, 512)\n",
      "        self.layer_2 = nn.Linear(512, 512)\n",
      "        self.layer_3 = nn.Linear(512, 512)\n",
      "        self.layer_4 = nn.Linear(512, 512)\n",
      "        self.layer_5 = nn.Linear(512, 512)\n",
      "        self.layer_out = nn.Linear(512, 1) \n",
      "        \n",
      "        self.prelu1 = nn.PReLU()\n",
      "        self.prelu2 = nn.PReLU()\n",
      "        self.prelu3 = nn.PReLU()\n",
      "        self.prelu4 = nn.PReLU()\n",
      "        self.prelu5 = nn.PReLU()\n",
      "\n",
      "        self.dropout1 = nn.Dropout(p=0.1)\n",
      "        self.dropout2 = nn.Dropout(p=0.1)\n",
      "        self.dropout3 = nn.Dropout(p=0.1)\n",
      "        self.dropout4 = nn.Dropout(p=0.1)\n",
      "\n",
      "\n",
      "        self.batchnorm1 = nn.BatchNorm1d(512)\n",
      "        self.batchnorm2 = nn.BatchNorm1d(512)\n",
      "        self.batchnorm3 = nn.BatchNorm1d(512)\n",
      "        self.batchnorm4 = nn.BatchNorm1d(512)\n",
      "        self.batchnorm5 = nn.BatchNorm1d(512)\n",
      "        \n",
      "    def forward(self, cat_inputs, numer_inputs):\n",
      "                \n",
      "#         inputs = torch.cat([self.embeddings[col](cat_inputs[:, col]) for col in range(self.num_cats)]+[numer_inputs], dim=1)\n",
      "        \n",
      "        inputs = torch.cat([cat_inputs, numer_inputs], dim=1)\n",
      "        \n",
      "        x = self.prelu1(self.layer_1(inputs))\n",
      "        x = self.batchnorm1(x)\n",
      "        x = self.dropout1(x)\n",
      "        x = self.prelu2(self.layer_2(x))\n",
      "        x = self.batchnorm2(x)\n",
      "        x = self.dropout2(x)\n",
      "        x = self.prelu3(self.layer_3(x))\n",
      "        x = self.batchnorm3(x)\n",
      "        x = self.dropout3(x)\n",
      "        x = self.prelu4(self.layer_4(x))\n",
      "        x = self.batchnorm4(x)\n",
      "        x = self.dropout4(x)\n",
      "        x = self.prelu5(self.layer_5(x))\n",
      "        x = self.batchnorm5(x)\n",
      "        x = self.layer_out(x)\n",
      "        return x\n"
     ]
    }
   ],
   "source": [
    "# view classifier model - it is imported above\n",
    "!cat /rapids/notebooks/clfmodel.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_checkpoint(model, optimizer, epoch: int, best_score, file_name, threshold, save_path: str, k_fold=0):\n",
    "    \"\"\"\n",
    "    Save checkpoint to pytorch .pt file.\n",
    "    Args:\n",
    "        model: Instance of Pytorch model.\n",
    "        optimizer: pytorch optimizer ex. torch.optim.Adam() instance\n",
    "        epoch: epoch number\n",
    "        best_score: model best score acccoring to appropriate metric.\n",
    "                    Can be multiple, just save as a dict. of {'metric': score}\n",
    "        k_fold: int or string for K-folds.\n",
    "        file_name (str): filename usually including the name of the model.:\n",
    "\n",
    "    Returns:\n",
    "\n",
    "    \"\"\"\n",
    "    checkpoint = {\n",
    "        'model': model.state_dict(),\n",
    "        'optimizer': optimizer.state_dict(),\n",
    "        'k_fold': k_fold,\n",
    "        'epoch': epoch,\n",
    "        'best_score': best_score,\n",
    "        'threshold': threshold\n",
    "    }\n",
    "    if not save_path:\n",
    "        save_path = '.'\n",
    "    os.makedirs(f'{save_path}/checkpoints/', exist_ok=True)\n",
    "\n",
    "    torch.save(checkpoint, f'{save_path}/checkpoints/{file_name}.pt')\n",
    "\n",
    "\n",
    "def load_checkpoint(file_name: str, model, device: str, save_path: str):\n",
    "    \"\"\"\n",
    "    Load Checkpoint from pytorch .pt file.\n",
    "\n",
    "    Args:\n",
    "        device: str\n",
    "        file_name (str): filename usually including the name of the model.\n",
    "        model_cls: (uninstantiated) class belonging to a custom nn.Module\n",
    "        **kwargs:\n",
    "\n",
    "    Returns:\n",
    "\n",
    "    \"\"\"\n",
    "    # instantiate the model and pass in input params as kwargs.\n",
    "    model = model.to(device)\n",
    "    if not save_path:\n",
    "        save_path = '.'\n",
    "    checkpoint = torch.load(f'{save_path}/checkpoints/{file_name}.pt')\n",
    "    model.load_state_dict(checkpoint['model'])\n",
    "    model.eval()\n",
    "    return model, checkpoint['threshold']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch(model, loader, optimizer, loss_func, metric, device, epoch=''):\n",
    "    model.train()\n",
    "    model.zero_grad()\n",
    "    train_loss = torch.zeros(len(loader), requires_grad=False, device=device)\n",
    "    train_acc = torch.zeros(len(loader), requires_grad=False, device=device)\n",
    "    batch = 0\n",
    "    pbar = tqdm(loader)\n",
    "    \n",
    "    train_logits = []\n",
    "    train_targets = []\n",
    "    \n",
    "\n",
    "    for x_cat, x_cont, target in pbar:\n",
    "        # make prediction\n",
    "        logits = model(x_cat, x_cont).squeeze()\n",
    "        \n",
    "        # calculate loss\n",
    "        loss = loss_func(logits, target)  # criterion\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        for param in model.parameters():\n",
    "            param.grad = None\n",
    "        \n",
    "        train_loss[batch] = loss.detach()\n",
    "        train_acc[batch] = metric(logits, target)\n",
    "        \n",
    "        train_targets.append(target.detach())\n",
    "        train_logits.append(logits.detach())\n",
    "        \n",
    "        batch += 1\n",
    "        if batch == len(loader) or not batch %100:\n",
    "            pbar.set_description(f'Epoch: {epoch} mean loss {train_loss[:batch].mean().cpu(): 0.5f}, ' \n",
    "                                 f'mean acc: {train_acc[:batch].mean().cpu(): 0.5f}')\n",
    "        \n",
    "    return train_loss, train_logits, train_targets  \n",
    "\n",
    "\n",
    "def val_epoch(model, loader, loss_func, device):\n",
    "    model.eval()\n",
    "    val_logits = []\n",
    "    val_targets = []\n",
    "    val_loss = torch.zeros(len(loader), requires_grad=False, device=device)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        batch=0\n",
    "        for x_cat, x_cont, target in tqdm(iter(loader)):\n",
    "            # make prediction\n",
    "            logits = model(x_cat, x_cont).squeeze()\n",
    "            \n",
    "            # calculate loss\n",
    "            loss = loss_func(logits, target)  # criterion\n",
    "            \n",
    "            # save some data\n",
    "            val_targets.append(target.detach())\n",
    "            val_logits.append(logits.detach())\n",
    "            val_loss[batch] = loss.detach()\n",
    "            batch+=1\n",
    "        val_loss = val_loss.mean()\n",
    "    return val_loss, val_logits, val_targets\n",
    "\n",
    "\n",
    "def calc_metrics(model, _targets, _logits, tst_loader, loss_func, device, use_cpu):\n",
    "    # scoring on train\n",
    "    if not use_cpu:\n",
    "        _targets = cp.asarray(torch.cat(_targets)).astype(int)\n",
    "        _probs = cp.asarray(torch.sigmoid(torch.cat(_logits)))\n",
    "    else:\n",
    "        _targets = np.array(torch.cat(_targets)).astype(int)\n",
    "        _probs = np.array(torch.sigmoid(torch.cat(_logits)))\n",
    "\n",
    "    # # calc precision, recall and find threshold for maximum F1 score on train.\n",
    "    precision, recall, thresholds = precision_recall_curve(_targets, _probs)\n",
    "    \n",
    "    threshold_idx = argmax((2 * precision * recall) / (precision + recall))\n",
    "    threshold = thresholds[threshold_idx]\n",
    "\n",
    "    # now on test set\n",
    "    _, _logits, _targets = val_epoch(model, tst_loader, loss_func, device)\n",
    "    if not use_cpu:\n",
    "        _targets = cp.asarray(torch.cat(_targets)).astype(int)\n",
    "        _probs = cp.asarray(torch.sigmoid(torch.cat(_logits)))\n",
    "    else:\n",
    "        _targets = np.array(torch.cat(_targets)).astype(int)\n",
    "        _probs = np.array(torch.sigmoid(torch.cat(_logits)))\n",
    "    \n",
    "    precision, recall, thresholds = precision_recall_curve(_targets, _probs)\n",
    "    # find threshold index closest to threshold value on train set.\n",
    "    \n",
    "    if not use_cpu:\n",
    "        threshold_idx = cp.argmin(cp.abs(thresholds - threshold))\n",
    "    else:\n",
    "        threshold_idx = np.argmin(np.abs(thresholds - threshold))\n",
    "    \n",
    "    pred_labels = (_probs >= threshold).astype(int)\n",
    "\n",
    "    auc_sc = roc_auc_score(_targets, _probs)\n",
    "    acc_sc = accuracy_score(_targets, pred_labels)\n",
    "    f1_sc = (2 * precision[threshold_idx] * recall[threshold_idx]) / (precision[threshold_idx] + recall[threshold_idx])\n",
    "    conf_mat = confusion_matrix(_targets, pred_labels)\n",
    "    print(f'F1: {f1_sc.item(): 0.4f} '\n",
    "          f'AUC: {auc_sc: 0.4f} '\n",
    "          f'Pr: {precision[threshold_idx].item(): 0.4f} '\n",
    "          f'Rec: {recall[threshold_idx].item(): 0.4f} ', \n",
    "          f'Acc: {acc_sc: 0.4f} '\n",
    "          f'Thresh: {threshold.item(): 0.4f} '\n",
    "          f'Pred Def: {pred_labels.sum().item()} '\n",
    "          f'Actual: {int(_targets.sum())} '\n",
    "          )\n",
    "    print('Confusion Matrix: \\n:', conf_mat)\n",
    "    return f1_sc.item(), threshold.item()\n",
    "    \n",
    "    \n",
    "def test_em(model, loader, loss_func, threshold, device, use_cpu, save_path: str = None):\n",
    "    sns.set()\n",
    "    test_loss, test_logits, test_targets = val_epoch(model, loader, loss_func, device)\n",
    "    \n",
    "    # scoring\n",
    "    if not use_cpu:\n",
    "        test_targets = cp.asarray(torch.cat(test_targets)).astype(int)\n",
    "        test_probs = cp.asarray(torch.sigmoid(torch.cat(test_logits)))\n",
    "    else:\n",
    "        test_targets = torch.cat(test_targets).numpy().astype(int)\n",
    "        test_probs = torch.sigmoid(torch.cat(test_logits)).numpy()\n",
    "    pred_labels = (test_probs >= threshold).astype(int)\n",
    "\n",
    "    precision, recall, thresholds = precision_recall_curve(test_targets, test_probs)\n",
    "    \n",
    "    if not use_cpu:\n",
    "        threshold_idx = cp.argmin(cp.abs(thresholds - threshold))\n",
    "    else:\n",
    "        threshold_idx = np.argmin(np.abs(thresholds - threshold))\n",
    "    \n",
    "    auc_sc = roc_auc_score(test_targets, test_probs)\n",
    "    acc_sc = accuracy_score(test_targets, pred_labels)\n",
    "    f1_sc = (2 * precision[threshold_idx] * recall[threshold_idx]) / (precision[threshold_idx] + recall[threshold_idx])\n",
    "    conf_mat = confusion_matrix(test_targets, pred_labels)\n",
    "    \n",
    "    auprc= auc(cupy2numpy(recall, not use_cpu), cupy2numpy(precision, not use_cpu))\n",
    "\n",
    "    print(f'Thresh: {threshold: 0.4f}', f'Prec: {precision[threshold_idx].item(): 0.4f}',\n",
    "          f'Rec: {recall[threshold_idx].item(): 0.4f}', f'Pred Def: {pred_labels.sum()}',\n",
    "          f'Actual: {int(test_targets.sum())}', f'AUC: {auc_sc: 0.4f}',\n",
    "          f'Acc: {acc_sc: 0.4f}',\n",
    "          f'F1: {f1_sc.item(): 0.4f}',\n",
    "          f'AUPRC: {auprc: 0.4f}',\n",
    "         )\n",
    "    print('Confusion Matrix: \\n:', conf_mat)\n",
    "    \n",
    "    fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "    no_skill = cupy2numpy(test_targets.mean(), not use_cpu) \n",
    "    ax[0].plot(cupy2numpy(recall, not use_cpu), cupy2numpy(precision, not use_cpu), color='darkorange', lw=1, label='PR curve (area = %0.2f)' % auprc)\n",
    "    ax[0].plot([0,1], [no_skill, no_skill], color='navy', linestyle='--', label='No Skill')\n",
    "    ax[0].plot(cupy2numpy(recall[threshold_idx], not use_cpu), cupy2numpy(precision[threshold_idx], not use_cpu), 'ro', label='F1 threshold')\n",
    "    ax[0].set_xlabel('Recall')\n",
    "    ax[0].set_ylabel('Precision')\n",
    "    ax[0].set_xlim([-0.02, 1.02])\n",
    "    ax[0].set_ylim([-0.02, 1.02])\n",
    "    ax[0].set_title('Precision-Recall curve')\n",
    "    ax[0].legend()\n",
    "    \n",
    "    fpr, tpr, threshold = roc_curve(cupy2numpy(test_targets, not use_cpu), cupy2numpy(test_probs, not use_cpu))\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    ax[1].set_title('Receiver Operating Characteristic')\n",
    "    ax[1].plot(fpr, tpr, 'b', label = 'ROC curve (area = %0.2f)' % roc_auc)\n",
    "    ax[1].legend(loc = 'lower right')\n",
    "    ax[1].plot([0, 1], [0, 1],'r--')\n",
    "    ax[1].set_xlim([-0.02, 1.02])\n",
    "    ax[1].set_ylim([-0.02, 1.02])\n",
    "    ax[1].set_ylabel('True Positive Rate')\n",
    "    ax[1].set_xlabel('False Positive Rate')   \n",
    "    \n",
    "    plt.tight_layout()\n",
    "    if save_path and isinstance(save_path, str):\n",
    "        plt.savefig(save_path, dpi=600)\n",
    "    print(\"Classification Report for Pytorch Classifier: \\n\",\n",
    "          classification_report(cupy2numpy(test_targets, not use_cpu), cupy2numpy(pred_labels, not use_cpu)))\n",
    "    return test_targets, pred_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Pick ONE of NVTabular (recommended w/ GPU) OR Pytorch Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for explainability later.\n",
    "y_pos = y_test.loc[y_test==1].sample(n=BATCH_SIZE, random_state=8675309)\n",
    "y_neg = y_test.loc[y_test==0].sample(n=BATCH_SIZE, random_state=8675309)\n",
    "X_test_pos = X_test.loc[y_pos.index]\n",
    "X_test_neg = X_test.loc[y_neg.index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyt_data_prep_time = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Pytorch Dataloader and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:21:58.704930Z",
     "start_time": "2021-07-14T20:21:58.702373Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:23:22.802704Z",
     "start_time": "2021-07-14T20:23:22.764268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10109124, 20)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:32:39.295879Z",
     "start_time": "2021-07-14T20:32:39.226206Z"
    }
   },
   "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>OrInterestRate</th>\n",
       "      <th>OrUnpaidPrinc</th>\n",
       "      <th>OrLoanTerm</th>\n",
       "      <th>OrCLTV</th>\n",
       "      <th>NumBorrow</th>\n",
       "      <th>DTIRat</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>NumUnits</th>\n",
       "      <th>CoCreditScore</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550205</th>\n",
       "      <td>1.117743</td>\n",
       "      <td>-0.996140</td>\n",
       "      <td>-1.531366</td>\n",
       "      <td>-2.909132</td>\n",
       "      <td>-1.164328</td>\n",
       "      <td>-0.904860</td>\n",
       "      <td>0.985665</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137124</th>\n",
       "      <td>1.616121</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>0.637021</td>\n",
       "      <td>-0.650480</td>\n",
       "      <td>-1.164328</td>\n",
       "      <td>2.717126</td>\n",
       "      <td>-2.276593</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541402</th>\n",
       "      <td>1.616121</td>\n",
       "      <td>-1.060655</td>\n",
       "      <td>0.637021</td>\n",
       "      <td>-2.561647</td>\n",
       "      <td>0.816602</td>\n",
       "      <td>0.155234</td>\n",
       "      <td>0.630162</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>1.013563</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5374781</th>\n",
       "      <td>0.394607</td>\n",
       "      <td>-1.270329</td>\n",
       "      <td>-1.531366</td>\n",
       "      <td>0.276147</td>\n",
       "      <td>-1.164328</td>\n",
       "      <td>0.420257</td>\n",
       "      <td>-1.711971</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269471</th>\n",
       "      <td>1.371818</td>\n",
       "      <td>1.205442</td>\n",
       "      <td>0.637021</td>\n",
       "      <td>0.565717</td>\n",
       "      <td>-1.164328</td>\n",
       "      <td>1.303668</td>\n",
       "      <td>0.253748</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.000504</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9214207</th>\n",
       "      <td>-0.826907</td>\n",
       "      <td>1.584468</td>\n",
       "      <td>0.637021</td>\n",
       "      <td>-0.997965</td>\n",
       "      <td>0.816602</td>\n",
       "      <td>-1.964953</td>\n",
       "      <td>0.567426</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.605517</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10613369</th>\n",
       "      <td>-1.193361</td>\n",
       "      <td>-0.673564</td>\n",
       "      <td>0.637021</td>\n",
       "      <td>0.623631</td>\n",
       "      <td>-1.164328</td>\n",
       "      <td>-0.639836</td>\n",
       "      <td>0.671986</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4343224</th>\n",
       "      <td>0.150304</td>\n",
       "      <td>-0.213893</td>\n",
       "      <td>-1.531366</td>\n",
       "      <td>-0.824222</td>\n",
       "      <td>0.816602</td>\n",
       "      <td>-0.904860</td>\n",
       "      <td>-0.018107</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.856622</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759119</th>\n",
       "      <td>-1.559815</td>\n",
       "      <td>0.519968</td>\n",
       "      <td>-1.531366</td>\n",
       "      <td>0.565717</td>\n",
       "      <td>-1.164328</td>\n",
       "      <td>-0.816519</td>\n",
       "      <td>1.194784</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998321</th>\n",
       "      <td>0.028153</td>\n",
       "      <td>1.479631</td>\n",
       "      <td>0.637021</td>\n",
       "      <td>-0.534652</td>\n",
       "      <td>0.816602</td>\n",
       "      <td>-0.904860</td>\n",
       "      <td>0.797457</td>\n",
       "      <td>-0.132996</td>\n",
       "      <td>1.139116</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10109124 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          OrInterestRate  OrUnpaidPrinc  OrLoanTerm    OrCLTV  NumBorrow    DTIRat  CreditScore  NumUnits  CoCreditScore     Extra\n",
       "1550205         1.117743      -0.996140   -1.531366 -2.909132  -1.164328 -0.904860     0.985665 -0.132996       0.005000 -0.000504\n",
       "1137124         1.616121      -0.084862    0.637021 -0.650480  -1.164328  2.717126    -2.276593 -0.132996       0.005000 -0.000504\n",
       "541402          1.616121      -1.060655    0.637021 -2.561647   0.816602  0.155234     0.630162 -0.132996       1.013563 -0.000504\n",
       "5374781         0.394607      -1.270329   -1.531366  0.276147  -1.164328  0.420257    -1.711971 -0.132996       0.005000 -0.000504\n",
       "2269471         1.371818       1.205442    0.637021  0.565717  -1.164328  1.303668     0.253748 -0.132996       0.005000 -0.000504\n",
       "...                  ...            ...         ...       ...        ...       ...          ...       ...            ...       ...\n",
       "9214207        -0.826907       1.584468    0.637021 -0.997965   0.816602 -1.964953     0.567426 -0.132996       0.605517 -0.000504\n",
       "10613369       -1.193361      -0.673564    0.637021  0.623631  -1.164328 -0.639836     0.671986 -0.132996       0.005000 -0.000504\n",
       "4343224         0.150304      -0.213893   -1.531366 -0.824222   0.816602 -0.904860    -0.018107 -0.132996       0.856622 -0.000504\n",
       "8759119        -1.559815       0.519968   -1.531366  0.565717  -1.164328 -0.816519     1.194784 -0.132996       0.005000 -0.000504\n",
       "2998321         0.028153       1.479631    0.637021 -0.534652   0.816602 -0.904860     0.797457 -0.132996       1.139116 -0.000504\n",
       "\n",
       "[10109124 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[CONTINUOUS_COLUMNS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Channel',\n",
       " 'FTHomeBuyer',\n",
       " 'LoanPurpose',\n",
       " 'MortInsType',\n",
       " 'OccStatus',\n",
       " 'ProductType',\n",
       " 'PropertyState',\n",
       " 'PropertyType',\n",
       " 'SellerName',\n",
       " 'Zip']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CATEGORICAL_COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T21:20:46.332924Z",
     "start_time": "2021-07-14T21:20:46.275848Z"
    }
   },
   "outputs": [],
   "source": [
    "## train data\n",
    "class PytData(Dataset):\n",
    "\n",
    "    def __init__(self, X_data, y_data, cont_cols, cat_cols, device):\n",
    "        if device.type != 'cpu':\n",
    "            self.X_data_cont = torch.tensor(X_data[cont_cols].as_gpu_matrix(), dtype=torch.float32, device=device)\n",
    "            self.X_data_cat = torch.tensor(X_data[cat_cols].as_gpu_matrix(), dtype=torch.int64, device=device)\n",
    "        else:\n",
    "            self.X_data_cont = torch.tensor(X_data[cont_cols].values, dtype=torch.float32, device=device)\n",
    "            self.X_data_cat = torch.tensor(X_data[cat_cols].values, dtype=torch.int64, device=device)\n",
    "        \n",
    "        assert len(cont_cols) == self.X_data_cont.shape[1]\n",
    "        assert len(cat_cols) == self.X_data_cat.shape[1]\n",
    "        \n",
    "        self.y_data = torch.tensor(y_data.values, dtype=torch.float32, device=device)\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        return self.X_data_cat[index], self.X_data_cont[index], self.y_data[index]\n",
    "        \n",
    "    def __len__ (self):\n",
    "        return len(self.y_data)\n",
    "\n",
    "\n",
    "train_data = PytData(X_train, y_train, CONTINUOUS_COLUMNS, CATEGORICAL_COLUMNS, device)\n",
    "test_data = PytData(X_test, y_test, CONTINUOUS_COLUMNS, CATEGORICAL_COLUMNS, device)\n",
    "\n",
    "train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = DataLoader(dataset=test_data, batch_size=BATCH_SIZE)\n",
    "\n",
    "#for explainability later\n",
    "pos_data = PytData(X_test_pos, y_pos, CONTINUOUS_COLUMNS, CATEGORICAL_COLUMNS, device)\n",
    "neg_data = PytData(X_test_neg, y_neg, CONTINUOUS_COLUMNS, CATEGORICAL_COLUMNS, device)\n",
    "\n",
    "pos_loader = DataLoader(dataset=pos_data, batch_size=BATCH_SIZE)\n",
    "neg_loader = DataLoader(dataset=neg_data, batch_size=BATCH_SIZE)\n",
    "\n",
    "with open(f'{clean_data_path}/pos_loader.pkl', 'wb') as f:\n",
    "    pickle.dump(pos_loader, f)\n",
    "    \n",
    "with open(f'{clean_data_path}/neg_loader.pkl', 'wb') as f:\n",
    "    pickle.dump(neg_loader, f)\n",
    "\n",
    "with open(f'{clean_data_path}/test_loader.pkl', 'wb') as f:\n",
    "    pickle.dump(test_loader, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true",
    "tags": []
   },
   "source": [
    "### Model Training - Pytorch Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:52:35.695727Z",
     "start_time": "2021-07-15T00:52:33.332346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binaryClassification(\n",
      "  (layer_1): Linear(in_features=20, out_features=512, bias=True)\n",
      "  (layer_2): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_3): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_4): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_5): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_out): Linear(in_features=512, out_features=1, bias=True)\n",
      "  (prelu1): PReLU(num_parameters=1)\n",
      "  (prelu2): PReLU(num_parameters=1)\n",
      "  (prelu3): PReLU(num_parameters=1)\n",
      "  (prelu4): PReLU(num_parameters=1)\n",
      "  (prelu5): PReLU(num_parameters=1)\n",
      "  (dropout1): Dropout(p=0.1, inplace=False)\n",
      "  (dropout2): Dropout(p=0.1, inplace=False)\n",
      "  (dropout3): Dropout(p=0.1, inplace=False)\n",
      "  (dropout4): Dropout(p=0.1, inplace=False)\n",
      "  (batchnorm1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm5): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ") cuda:0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = binaryClassification(cat_cards=cards, numer_dims=len(CONTINUOUS_COLUMNS)).to(device)\n",
    "print(model, device)\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9dfcb5d87d2d4f23bc5946975796247d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c76cc58927564b8fb59971f39f2318a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/549 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3751 AUC:  0.9130 Pr:  0.3079 Rec:  0.4798  Acc:  0.9538 Thresh:  0.1903 Pred Def: 50577 Actual: 32453 \n",
      "Confusion Matrix: \n",
      ": [[1055776   35006]\n",
      " [  16882   15571]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd310681aa394fed870a19f5d63fedbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "257b308a19654cffbf8f6dd6de840ad9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/549 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3859 AUC:  0.9157 Pr:  0.3256 Rec:  0.4737  Acc:  0.9564 Thresh:  0.1909 Pred Def: 47211 Actual: 32453 \n",
      "Confusion Matrix: \n",
      ": [[1058944   31838]\n",
      " [  17080   15373]]\n",
      "Total time: 260.4687271118164\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def binary_acc(y_pred, y_test):\n",
    "    y_pred_tag = torch.round(torch.sigmoid(y_pred))\n",
    "    correct_results_sum = (y_pred_tag == y_test).sum().float()\n",
    "    acc = correct_results_sum/y_test.shape[0]\n",
    "    return acc\n",
    "\n",
    "\n",
    "model_name = 'fnma_model_mlp_pyt'\n",
    "\n",
    "start = time.time()\n",
    "max_f1 = 0\n",
    "for e in range(1, EPOCHS+1):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    train_loss, train_logits, train_targets = train_epoch(model, train_loader, optimizer, criterion, metric=binary_acc, device=device,\n",
    "                             epoch=e)\n",
    "    epoch_f1_score, threshold = calc_metrics(model, train_targets, train_logits, test_loader, criterion, device, use_cpu=use_cpu)\n",
    "    if epoch_f1_score > max_f1:\n",
    "        max_f1 = epoch_f1_score\n",
    "        # save the model\n",
    "        save_checkpoint(model, optimizer, e, epoch_f1_score, model_name, threshold=threshold, save_path=clean_data_path)\n",
    "    \n",
    "print(f'Total time: {time.time() - start}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, threshold = load_checkpoint(model_name, model, device, save_path=clean_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18c8a15a15a04407b5a9947149e90792",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thresh:  0.1909 Prec:  0.3269 Rec:  0.5120 Pred Def: 626182 Actual: 399783 AUC:  0.9098 Acc:  0.9390 F1:  0.3990 AUPRC:  0.3402\n",
      "Confusion Matrix: \n",
      ": [[9287852  421489]\n",
      " [ 195090  204693]]\n",
      "Classification Report for Pytorch Classifier: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97   9709341\n",
      "           1       0.33      0.51      0.40    399783\n",
      "\n",
      "    accuracy                           0.94  10109124\n",
      "   macro avg       0.65      0.73      0.68  10109124\n",
      "weighted avg       0.95      0.94      0.95  10109124\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = test_em(model, train_loader, criterion, threshold, device, use_cpu=use_cpu,\n",
    "        save_path=f'{figures_path}/in_sample_mlp_pyt_pr_roc.png' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8e058edc472457aa132102c7d04b4ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/549 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thresh:  0.1909 Prec:  0.3256 Rec:  0.4737 Pred Def: 47211 Actual: 32453 AUC:  0.9157 Acc:  0.9564 F1:  0.3859 AUPRC:  0.3190\n",
      "Confusion Matrix: \n",
      ": [[1058944   31838]\n",
      " [  17080   15373]]\n",
      "Classification Report for Pytorch Classifier: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98   1090782\n",
      "           1       0.33      0.47      0.39     32453\n",
      "\n",
      "    accuracy                           0.96   1123235\n",
      "   macro avg       0.65      0.72      0.68   1123235\n",
      "weighted avg       0.97      0.96      0.96   1123235\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_labels, pred_labels = test_em(model, test_loader, criterion, threshold, device, use_cpu=use_cpu, \n",
    "                                   save_path=f'{figures_path}/out_of_sample_mlp_pyt_pr_roc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = confusion_matrix(true_labels, pred_labels)\n",
    "results = cupy2numpy(results, True)\n",
    "plot_confusion_matrix(results, ['Normal','Anomaly'], title='Confusion Matrix', save_path=f'{figures_path}/pyt_mlp_confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cudf\n",
    "import pyarrow.parquet as pq\n",
    "#!pip install librosa --user\n",
    "#!pip install llvmlite --ignore-installed\n",
    "#!pip install nvtabular==0.5.3\n",
    "pyt_dl_time = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true",
    "tags": []
   },
   "source": [
    "### (GPU only) NVTabular Dataloader and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:52:52.370925Z",
     "start_time": "2021-07-15T00:52:52.068786Z"
    }
   },
   "outputs": [],
   "source": [
    "import nvtabular as nvt\n",
    "from nvtabular.loader.torch import TorchAsyncItr, DLDataLoader\n",
    "\n",
    "if use_cpu:\n",
    "    raise Exception('This section is for GPU only.')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.5.3'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvt.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T21:39:27.664378Z",
     "start_time": "2021-07-14T21:39:27.605802Z"
    }
   },
   "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>Channel</th>\n",
       "      <th>CoCreditScore</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>DTIRat</th>\n",
       "      <th>Extra</th>\n",
       "      <th>FTHomeBuyer</th>\n",
       "      <th>LoanPurpose</th>\n",
       "      <th>MortInsType</th>\n",
       "      <th>NumBorrow</th>\n",
       "      <th>NumUnits</th>\n",
       "      <th>OccStatus</th>\n",
       "      <th>OrCLTV</th>\n",
       "      <th>OrInterestRate</th>\n",
       "      <th>OrLoanTerm</th>\n",
       "      <th>OrUnpaidPrinc</th>\n",
       "      <th>ProductType</th>\n",
       "      <th>PropertyState</th>\n",
       "      <th>PropertyType</th>\n",
       "      <th>SellerName</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550205</th>\n",
       "      <td>2</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>0.985658</td>\n",
       "      <td>-0.904846</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.909084</td>\n",
       "      <td>1.117746</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>-0.996131</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137124</th>\n",
       "      <td>1</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-2.276635</td>\n",
       "      <td>2.717158</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.650462</td>\n",
       "      <td>1.616116</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541402</th>\n",
       "      <td>2</td>\n",
       "      <td>1.013539</td>\n",
       "      <td>0.630152</td>\n",
       "      <td>0.155253</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.561604</td>\n",
       "      <td>1.616116</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-1.060646</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>437</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5374781</th>\n",
       "      <td>2</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>-1.712007</td>\n",
       "      <td>0.420278</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.276153</td>\n",
       "      <td>0.394622</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>-1.270318</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269471</th>\n",
       "      <td>1</td>\n",
       "      <td>0.004986</td>\n",
       "      <td>0.253734</td>\n",
       "      <td>1.303693</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.164326</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>0.565720</td>\n",
       "      <td>1.371817</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>1.205431</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Channel  CoCreditScore  CreditScore    DTIRat     Extra  FTHomeBuyer  LoanPurpose  MortInsType  NumBorrow  NumUnits  OccStatus    OrCLTV  OrInterestRate  OrLoanTerm  OrUnpaidPrinc  ProductType  PropertyState  PropertyType  SellerName  Zip  Default\n",
       "1550205        2       0.004986     0.985658 -0.904846 -0.000487            0            2            0  -1.164326 -0.133009          1 -2.909084        1.117746   -1.531372      -0.996131            0              4             4          31  902        0\n",
       "1137124        1       0.004986    -2.276635  2.717158 -0.000487            0            0            0  -1.164326 -0.133009          1 -0.650462        1.616116    0.637020      -0.084862            0             21             3          48  120        1\n",
       "541402         2       1.013539     0.630152  0.155253 -0.000487            0            0            0   0.816595 -0.133009          1 -2.561604        1.616116    0.637020      -1.060646            0             23             4          31  437        0\n",
       "5374781        2       0.004986    -1.712007  0.420278 -0.000487            0            2            0  -1.164326 -0.133009          0  0.276153        0.394622   -1.531372      -1.270318            0             28             3           3  195        0\n",
       "2269471        1       0.004986     0.253734  1.303693 -0.000487            0            2            0  -1.164326 -0.133009          1  0.565720        1.371817    0.637020       1.205431            0             51             4           3  475        0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = concat([X_train[sorted(X_train.columns)],  y_train], axis=1)\n",
    "train[CATEGORICAL_COLUMNS] = train[CATEGORICAL_COLUMNS].astype(int16)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T21:39:28.169798Z",
     "start_time": "2021-07-14T21:39:28.122762Z"
    }
   },
   "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>Channel</th>\n",
       "      <th>CoCreditScore</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>DTIRat</th>\n",
       "      <th>Extra</th>\n",
       "      <th>FTHomeBuyer</th>\n",
       "      <th>LoanPurpose</th>\n",
       "      <th>MortInsType</th>\n",
       "      <th>NumBorrow</th>\n",
       "      <th>NumUnits</th>\n",
       "      <th>OccStatus</th>\n",
       "      <th>OrCLTV</th>\n",
       "      <th>OrInterestRate</th>\n",
       "      <th>OrLoanTerm</th>\n",
       "      <th>OrUnpaidPrinc</th>\n",
       "      <th>ProductType</th>\n",
       "      <th>PropertyState</th>\n",
       "      <th>PropertyType</th>\n",
       "      <th>SellerName</th>\n",
       "      <th>Zip</th>\n",
       "      <th>Default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9793327</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.461690</td>\n",
       "      <td>-0.352718</td>\n",
       "      <td>-0.728163</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>1.086941</td>\n",
       "      <td>-0.949021</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-0.939681</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>273</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115999</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.277774</td>\n",
       "      <td>-1.063731</td>\n",
       "      <td>0.155253</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.476721</td>\n",
       "      <td>-0.460423</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>-0.592915</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8502057</th>\n",
       "      <td>2</td>\n",
       "      <td>-2.313573</td>\n",
       "      <td>-1.586534</td>\n",
       "      <td>0.950327</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.882115</td>\n",
       "      <td>-1.510908</td>\n",
       "      <td>-1.531372</td>\n",
       "      <td>-1.036453</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6870604</th>\n",
       "      <td>1</td>\n",
       "      <td>1.201866</td>\n",
       "      <td>0.922922</td>\n",
       "      <td>-0.816504</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>0.160326</td>\n",
       "      <td>-0.460423</td>\n",
       "      <td>-0.808575</td>\n",
       "      <td>-0.181634</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>396</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6856426</th>\n",
       "      <td>1</td>\n",
       "      <td>1.013539</td>\n",
       "      <td>0.860186</td>\n",
       "      <td>0.066911</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.816595</td>\n",
       "      <td>-0.133009</td>\n",
       "      <td>1</td>\n",
       "      <td>0.623633</td>\n",
       "      <td>-0.338274</td>\n",
       "      <td>0.637020</td>\n",
       "      <td>-0.826780</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Channel  CoCreditScore  CreditScore    DTIRat     Extra  FTHomeBuyer  LoanPurpose  MortInsType  NumBorrow  NumUnits  OccStatus    OrCLTV  OrInterestRate  OrLoanTerm  OrUnpaidPrinc  ProductType  PropertyState  PropertyType  SellerName  Zip  Default\n",
       "9793327        1      -0.461690    -0.352718 -0.728163 -0.000487            0            2            0   0.816595 -0.133009          1  1.086941       -0.949021    0.637020      -0.939681            0              9             4          18  273        0\n",
       "4115999        0      -1.277774    -1.063731  0.155253 -0.000487            0            2            0   0.816595 -0.133009          1 -0.476721       -0.460423   -1.531372      -0.592915            0             45             3          18  734        0\n",
       "8502057        2      -2.313573    -1.586534  0.950327 -0.000487            0            0            0   0.816595 -0.133009          1 -0.882115       -1.510908   -1.531372      -1.036453            0             51             4          31  470        0\n",
       "6870604        1       1.201866     0.922922 -0.816504 -0.000487            0            2            0   0.816595 -0.133009          1  0.160326       -0.460423   -0.808575      -0.181634            0             16             3          26  396        0\n",
       "6856426        1       1.013539     0.860186  0.066911 -0.000487            0            1            0   0.816595 -0.133009          1  0.623633       -0.338274    0.637020      -0.826780            0             34             3          26  847        0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = concat([X_test[sorted(X_test.columns)],  y_test], axis=1)\n",
    "test[CATEGORICAL_COLUMNS] = test[CATEGORICAL_COLUMNS].astype(cp.int16)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for explainability later.\n",
    "y_pos = y_test.loc[y_test==1].sample(n=BATCH_SIZE, random_state=8675309)\n",
    "y_neg = y_test.loc[y_test==0].sample(n=BATCH_SIZE, random_state=8675309)\n",
    "\n",
    "X_test_pos = X_test.loc[y_pos.index]\n",
    "X_test_neg = X_test.loc[y_neg.index]\n",
    "\n",
    "pos_data = concat([X_test_pos[sorted(X_test_pos.columns)],  y_pos], axis=1)\n",
    "pos_data[CATEGORICAL_COLUMNS] = pos_data[CATEGORICAL_COLUMNS].astype(int16)\n",
    "\n",
    "neg_data = concat([X_test_neg[sorted(X_test_neg.columns)],  y_neg], axis=1)\n",
    "neg_data[CATEGORICAL_COLUMNS] = neg_data[CATEGORICAL_COLUMNS].astype(int16)\n",
    "\n",
    "pos_path = f'{clean_data_path}/fnma_pos_test.parquet'\n",
    "neg_path = f'{clean_data_path}/fnma_neg_test.parquet'\n",
    "\n",
    "pos_data.to_parquet(pos_path)\n",
    "neg_data.to_parquet(neg_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:52:56.078717Z",
     "start_time": "2021-07-15T00:52:56.076015Z"
    }
   },
   "outputs": [],
   "source": [
    "# save train and test (not necessary but saves time from re-processing data if you restart kernel)\n",
    "\n",
    "# train_path = 'fnma_train_nvt_ros.parquet'\n",
    "# test_path = 'fnma_test_nvt_ros.parquet'\n",
    "\n",
    "train_path = f'{clean_data_path}/fnma_train_nvt.parquet'\n",
    "test_path = f'{clean_data_path}/fnma_test_nvt.parquet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T21:39:40.490022Z",
     "start_time": "2021-07-14T21:39:40.427344Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.to_parquet(train_path)\n",
    "test.to_parquet(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:52:59.115849Z",
     "start_time": "2021-07-15T00:52:57.666504Z"
    }
   },
   "outputs": [],
   "source": [
    "train_dataset = TorchAsyncItr(\n",
    "    nvt.Dataset(train_path),\n",
    "    cats=CATEGORICAL_COLUMNS,\n",
    "    conts=CONTINUOUS_COLUMNS,\n",
    "    labels=LABEL_COLUMNS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "train_loader = DLDataLoader(\n",
    "   train_dataset,\n",
    "   batch_size=None,\n",
    "   pin_memory=False,\n",
    "   num_workers=0\n",
    ")\n",
    "\n",
    "test_dataset = TorchAsyncItr(\n",
    "   nvt.Dataset(test_path),\n",
    "   cats=CATEGORICAL_COLUMNS,\n",
    "   conts=CONTINUOUS_COLUMNS,\n",
    "   labels=LABEL_COLUMNS,\n",
    "   batch_size=BATCH_SIZE\n",
    ")\n",
    "\n",
    "test_loader = DLDataLoader(\n",
    "   test_dataset,\n",
    "   batch_size=None,\n",
    "   pin_memory=False,\n",
    "   num_workers=0\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true",
    "tags": []
   },
   "source": [
    "### Model Training - NVTabular Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:53:08.400498Z",
     "start_time": "2021-07-15T00:53:08.382295Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binaryClassification(\n",
      "  (layer_1): Linear(in_features=20, out_features=512, bias=True)\n",
      "  (layer_2): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_3): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_4): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_5): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (layer_out): Linear(in_features=512, out_features=1, bias=True)\n",
      "  (prelu1): PReLU(num_parameters=1)\n",
      "  (prelu2): PReLU(num_parameters=1)\n",
      "  (prelu3): PReLU(num_parameters=1)\n",
      "  (prelu4): PReLU(num_parameters=1)\n",
      "  (prelu5): PReLU(num_parameters=1)\n",
      "  (dropout1): Dropout(p=0.1, inplace=False)\n",
      "  (dropout2): Dropout(p=0.1, inplace=False)\n",
      "  (dropout3): Dropout(p=0.1, inplace=False)\n",
      "  (dropout4): Dropout(p=0.1, inplace=False)\n",
      "  (batchnorm1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (batchnorm5): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = binaryClassification(cat_cards=cards, numer_dims=len(CONTINUOUS_COLUMNS)).to(device)\n",
    "\n",
    "print(model)\n",
    "criterion = nn.BCEWithLogitsLoss() # pos_weight=torch.tensor(25, device=device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T00:53:17.976178Z",
     "start_time": "2021-07-15T00:53:16.989705Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12dd2fe5e86440e7858f6427ce9feb9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9149e6d97b0e4f59b0bc7fb1c6fae83e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3836 AUC:  0.9155 Pr:  0.3367 Rec:  0.4457  Acc:  0.9586 Thresh:  0.1956 Pred Def: 42941 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1062314   28482]\n",
      " [  17980   14459]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2439a967729f4cae97875b7bd9bfed92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6bed2e1b9f64dbea05367a926091759",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3835 AUC:  0.9157 Pr:  0.3419 Rec:  0.4367  Acc:  0.9595 Thresh:  0.1930 Pred Def: 41435 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1063528   27268]\n",
      " [  18272   14167]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df737611459541f495d9903742df96cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "631e1fae6f7d4489bc4ffe04dc09289d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3899 AUC:  0.9175 Pr:  0.3443 Rec:  0.4492  Acc:  0.9594 Thresh:  0.1937 Pred Def: 42322 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1063047   27749]\n",
      " [  17866   14573]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfb6f1b8c65d40ea9397fda3b2e945c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "416b4e93e4c745e197d048a859ee5960",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3932 AUC:  0.9187 Pr:  0.3259 Rec:  0.4955  Acc:  0.9558 Thresh:  0.1998 Pred Def: 49325 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1057546   33250]\n",
      " [  16364   16075]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ce6369c1f81490db8fbaa7392ea31e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51fe14cae8a3499e8f21d86cf2ac4b36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.0001 AUC:  0.9199 Pr:  1.0000 Rec:  0.0000  Acc:  0.9711 Thresh:  0.9517 Pred Def: 0 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1090796       0]\n",
      " [  32439       0]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bedf844858ec483c89edc9a8238dc783",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c10f37bb47a142a0978999e8c9bbba09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3946 AUC:  0.9199 Pr:  0.3265 Rec:  0.4987  Acc:  0.9558 Thresh:  0.1999 Pred Def: 49550 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1057424   33372]\n",
      " [  16261   16178]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "509bfdc740ea4160a393f1a3eccfbab8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5705bd2e11874efa841defc48617ce3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3969 AUC:  0.9202 Pr:  0.3433 Rec:  0.4702  Acc:  0.9587 Thresh:  0.1992 Pred Def: 44427 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1061622   29174]\n",
      " [  17186   15253]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5810936080da4f1eb286c19dd770e35c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e51e2ab5ca644b983a472b89ebde8a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3980 AUC:  0.9207 Pr:  0.3324 Rec:  0.4959  Acc:  0.9567 Thresh:  0.1959 Pred Def: 48384 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1058497   32299]\n",
      " [  16354   16085]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd2fcf56ec164d06bfcf58d14ad57a36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20223417dbdf45f8bcef273b5fd3ec56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3981 AUC:  0.9206 Pr:  0.3494 Rec:  0.4626  Acc:  0.9596 Thresh:  0.2029 Pred Def: 42948 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1062855   27941]\n",
      " [  17432   15007]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d432c43c7ed46a8863740201c06f191",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c226081d2cf54076a67d58401615efa6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.0001 AUC:  0.9209 Pr:  1.0000 Rec:  0.0000  Acc:  0.9711 Thresh:  0.9437 Pred Def: 0 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1090796       0]\n",
      " [  32439       0]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53546fc54904488f9d4796d972932942",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c39d2cc418541d6bfaa8a26b1a6dcfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3999 AUC:  0.9212 Pr:  0.3472 Rec:  0.4715  Acc:  0.9591 Thresh:  0.2005 Pred Def: 44049 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1062042   28754]\n",
      " [  17144   15295]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99e7f4c9bed24aebbe1b0c586fb740ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6207722577c6473691d843495cf96d4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3971 AUC:  0.9212 Pr:  0.3315 Rec:  0.4953  Acc:  0.9566 Thresh:  0.1993 Pred Def: 48473 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1058390   32406]\n",
      " [  16372   16067]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78d329b67d234c2e99088a06a4b2f6cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5db3b543cdea40f7b0eebf353dc097f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.0001 AUC:  0.9215 Pr:  1.0000 Rec:  0.0000  Acc:  0.9711 Thresh:  0.9547 Pred Def: 0 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1090796       0]\n",
      " [  32439       0]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "399738e8f67a4f50a6919fcaa716e479",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f369adc47ea4079adb3d2a9a7b2b9a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3991 AUC:  0.9216 Pr:  0.3737 Rec:  0.4281  Acc:  0.9628 Thresh:  0.2061 Pred Def: 37166 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1067518   23278]\n",
      " [  18551   13888]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7835481d65834c1db73c3907c7e6ff96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6b1407a8eb4403987423889d82e49a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3976 AUC:  0.9207 Pr:  0.3349 Rec:  0.4893  Acc:  0.9572 Thresh:  0.2086 Pred Def: 47396 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1059271   31525]\n",
      " [  16568   15871]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "897a54829ee54f819a68513dba7018e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa57e399b9404c498fa48404bbd593ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3987 AUC:  0.9217 Pr:  0.3337 Rec:  0.4952  Acc:  0.9569 Thresh:  0.2029 Pred Def: 48136 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1058723   32073]\n",
      " [  16376   16063]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14fcbe780bcf47f2a03e7c8a2f5801b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "969effc8e9064ffc9325a51d28725946",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.4010 AUC:  0.9221 Pr:  0.3529 Rec:  0.4642  Acc:  0.9599 Thresh:  0.2048 Pred Def: 42666 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1063188   27608]\n",
      " [  17381   15058]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b89504eed96d409fa56407095e44aef5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20283f3978774e6c92b38bf137864777",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.4002 AUC:  0.9217 Pr:  0.3634 Rec:  0.4454  Acc:  0.9614 Thresh:  0.2049 Pred Def: 39762 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1065482   25314]\n",
      " [  17991   14448]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbc7bcf5d0454a3e9a8ec5f1830fdf56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dadc87429cbe48ca94d6a47aa2e1e268",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.4010 AUC:  0.9224 Pr:  0.3402 Rec:  0.4884  Acc:  0.9579 Thresh:  0.2031 Pred Def: 46576 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1060064   30732]\n",
      " [  16595   15844]]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2856bd0d9e0140a49000b8393fca8c5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4937 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25cbfc8b072f4191b1c144eb854331e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1:  0.3996 AUC:  0.9222 Pr:  0.3594 Rec:  0.4500  Acc:  0.9609 Thresh:  0.2071 Pred Def: 40623 Actual: 32439 \n",
      "Confusion Matrix: \n",
      ": [[1064772   26024]\n",
      " [  17840   14599]]\n",
      "Total time: 382.2052869796753\n"
     ]
    }
   ],
   "source": [
    "def binary_acc(y_pred, y_test):\n",
    "    with torch.no_grad():\n",
    "        y_pred_tag = torch.round(torch.sigmoid(y_pred))\n",
    "        correct_results_sum = (y_pred_tag == y_test).sum().float()\n",
    "        acc = correct_results_sum/y_test.shape[0]\n",
    "    return acc\n",
    "\n",
    "model_name = 'fnma_model_mlp_nvt'\n",
    "\n",
    "start = time.time()\n",
    "max_f1 = 0\n",
    "for e in range(1, EPOCHS+1):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    train_loss, train_logits, train_targets = train_epoch(model, train_loader, optimizer, criterion, metric=binary_acc, device=device,\n",
    "                             epoch=e)\n",
    "    epoch_f1_score, threshold = calc_metrics(model, train_targets, train_logits, test_loader, criterion, device, use_cpu)\n",
    "    if epoch_f1_score > max_f1:\n",
    "        max_f1 = epoch_f1_score\n",
    "        # save the model\n",
    "        save_checkpoint(model, optimizer, e, epoch_f1_score, model_name, threshold=threshold, save_path=clean_data_path)\n",
    "    \n",
    "print(f'Total time: {time.time() - start}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T21:54:07.702568Z",
     "start_time": "2021-07-14T21:54:07.677309Z"
    }
   },
   "outputs": [],
   "source": [
    "model, threshold = load_checkpoint(model_name, model, device, save_path=clean_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8beef426c448473fa4cd713cc272faeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thresh:  0.1900 Prec:  0.3335 Rec:  0.4758 Pred Def: 570338 Actual: 399797 AUC:  0.9070 Acc:  0.9417 F1:  0.3922 AUPRC:  0.3326\n",
      "Confusion Matrix: \n",
      ": [[9329222  380105]\n",
      " [ 209564  190233]]\n",
      "Classification Report for Pytorch Classifier: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97   9709327\n",
      "           1       0.33      0.48      0.39    399797\n",
      "\n",
      "    accuracy                           0.94  10109124\n",
      "   macro avg       0.66      0.72      0.68  10109124\n",
      "weighted avg       0.95      0.94      0.95  10109124\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in sample performance.\n",
    "_ = test_em(model, train_loader, criterion, threshold, device, use_cpu=use_cpu,\n",
    "            save_path=f'{figures_path}/in_sample_nvt_mlp_pyt_pr_roc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T21:55:24.017092Z",
     "start_time": "2021-07-14T21:55:21.499715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f462d9f328d04147836b33810ae142a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thresh:  0.1900 Prec:  0.3310 Rec:  0.4388 Pred Def: 42994 Actual: 32439 AUC:  0.9135 Acc:  0.9582 F1:  0.3774 AUPRC:  0.3104\n",
      "Confusion Matrix: \n",
      ": [[1062035   28761]\n",
      " [  18206   14233]]\n",
      "Classification Report for Pytorch Classifier: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.98   1090796\n",
      "           1       0.33      0.44      0.38     32439\n",
      "\n",
      "    accuracy                           0.96   1123235\n",
      "   macro avg       0.66      0.71      0.68   1123235\n",
      "weighted avg       0.96      0.96      0.96   1123235\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# out of sample performance.\n",
    "true_labels, pred_labels = test_em(model, test_loader, criterion, threshold, device, use_cpu=use_cpu,\n",
    "                                   save_path=f'{figures_path}/out_of_sample_nvt_mlp_pyt_pr_roc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = confusion_matrix(true_labels, pred_labels)\n",
    "results = cupy2numpy(results, True)\n",
    "plot_confusion_matrix(results, ['Normal','Anomaly'], title='Confusion Matrix', save_path=f'{figures_path}/nvt_mlp_confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": "true",
    "tags": []
   },
   "source": [
    "# Timings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "nvt_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "figures_path = '/rapids/notebooks/figures'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-14T20:20:11.944557Z",
     "start_time": "2021-07-14T20:20:11.784209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'latency in seconds')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Orig from Aug 2 2021:\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "#These timings are current as of Sep 2, 2020\n",
    "data = [[614.2, 19.5, 125.9, .176, 1821.1, 9370.6, 27.7, 0.0, 504.0],\n",
    "         [16.4, .137, 54.6, .007, 102.1, 3712.4, 4.3, 631.0, 254.9]]\n",
    "\n",
    "steps = ['read_csv', 'merge', 'xgb train', 'xgb inference',\n",
    "         'xgb shap', 'pyt train', 'pyt inference', 'nvtabular train\\n(GPU only)',\n",
    "         'captum\\ngradientshap']\n",
    "steps.reverse()\n",
    "data[0].reverse()\n",
    "data[1].reverse()\n",
    "#data[2].reverse()\n",
    "cpu = data[0]\n",
    "a100 = data[1]\n",
    "#a100 = data[2]\n",
    "DF = pd.DataFrame({#'NVIDIA A100 GPU': a100,\n",
    "                   'NVIDIA A100 GPU': a100,\n",
    "                   'Intel Xeon E5-2698 CPU': cpu}, index=steps)\n",
    "dict = {#\"NVIDIA A100 GPU\": \"darkgreen\",\n",
    "    \"NVIDIA A100 GPU\": \"#76B900\",\"CPU\": \"turquoise\"}\n",
    "_ = DF.plot.barh(color=dict.values(), ax=ax)\n",
    "plt.suptitle(\"RAPIDS GPU Acceleration of read_csv, merge, ML training ML inference, and Shapley ML Explanation\")\n",
    "ax.set_title('Run times on CPU and GPU - Lower is better')\n",
    "ax.set_xlabel('latency in seconds')\n",
    "\n",
    "# print(\"               RAPIDS GPU Acceleration of read_csv, merge, ML training,\")\n",
    "# print(\"                       ML inference, and Shapley ML Explanation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: '/rapids/notebooks/figures/perf_plot_preprocess.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1272610/2633115805.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# print(\"                  RAPIDS GPU Acceleration of read_csv, merge, DL inference\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{figures_path}/perf_plot_preprocess.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    967\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3013\u001b[0m                 \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3015\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3017\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2253\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2254\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2255\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2256\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2257\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[1;32m    507\u001b[0m         \"\"\"\n\u001b[1;32m    508\u001b[0m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m         mpl.image.imsave(\n\u001b[0m\u001b[1;32m    510\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m             dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)\n",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1614\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"format\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1615\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dpi\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1616\u001b[0;31m         \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpil_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/rapids-21.10/lib/python3.8/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2235\u001b[0m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: '/rapids/notebooks/figures/perf_plot_preprocess.png'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "#These timings are current as of Aug 2, 2021\n",
    "data = [[614.2, 19.5, 27.7],\n",
    "         [16.4, .137, 4.3]]\n",
    "\n",
    "steps = ['read_csv','merge', 'pyt inference']\n",
    "\n",
    "steps.reverse()\n",
    "data[0].reverse()\n",
    "data[1].reverse()\n",
    "cpu = data[0]\n",
    "a100 = data[1]\n",
    "DF = pd.DataFrame({'NVIDIA Ampere A100 GPU': a100,\n",
    "                   'Intel Xeon E5-2698 CPU': cpu}, index=steps)\n",
    "dct = {\"NVIDIA Ampere A100 GPU\": \"#76B900\",\"CPU\": \"turquoise\"}\n",
    "_ = DF.plot.barh(color=dct.values(), ax=ax)\n",
    "ax.set_title('Run times on CPU and GPU')\n",
    "ax.set_xlabel('latency in seconds')\n",
    "plt.suptitle(\"                  RAPIDS GPU Acceleration of read_csv, merge, DL inference\")\n",
    "ax.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "# print(\"                  RAPIDS GPU Acceleration of read_csv, merge, DL inference\")\n",
    "plt.savefig(f'{figures_path}/perf_plot_preprocess.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "#These timings are current as of Aug 2, 2021\n",
    "data = [[125.9, 1821.1, 9370.6, 0.0, 504.0],\n",
    "         [54.6, 102.1, 3712.4, 631.0, 254.9]]\n",
    "\n",
    "steps = ['xgb train', 'xgb shap', 'pyt train', 'nvtabular train\\n(GPU only)',\n",
    "         'captum\\ngradientshap']\n",
    "steps.reverse()\n",
    "data[0].reverse()\n",
    "data[1].reverse()\n",
    "cpu = data[0]\n",
    "a100 = data[1]\n",
    "DF = pd.DataFrame({'NVIDIA A100 GPU': a100,\n",
    "                   'Intel Xeon E5-2698 CPU': cpu}, index=steps)\n",
    "dct = {\"NVIDIA Ampere A100 GPU\": \"#76B900\",\"CPU\": \"turquoise\"}\n",
    "_ = DF.plot.barh(color=dict.values(), ax=ax)\n",
    "ax.set_title('Run times on CPU and GPU')\n",
    "ax.set_xlabel('latency in seconds')\n",
    "plt.suptitle(\"                  RAPIDS GPU Acceleration of ML and DL train, NVTabular train, and Shapley ML and DL Explanation\")\n",
    "ax.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{figures_path}/perf_plot_train.png', dpi=800)\n",
    "# print(\"    RAPIDS GPU Acceleration of ML and DL train, NVTabular train, and Shapley ML and DL Explanation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "#These timings are current as of Aug 2, 2021\n",
    "data = [[382.2], [2567.3]]\n",
    "\n",
    "# steps = ['xgb train', 'xgb shap', 'pyt train', 'nvtabular train\\n(GPU only)',\n",
    "#          'captum\\ngradientshap']\n",
    "# steps.reverse()\n",
    "# data[0].reverse()\n",
    "# data[1].reverse()\n",
    "nvt_a100 = data[0]\n",
    "pyt_a100 = data[1]\n",
    "DF = pd.DataFrame({'PyTorch via NVTabular API on GPU': nvt_a100, 'PyTorch on GPU': pyt_a100},\n",
    "                  index=['training\\nepoch\\nloop'],\n",
    "                 )\n",
    "color_dict = {\"PyTorch via NVTabular API on GPU\":  \"#76B900\", \"PyTorch on GPU\": \"pink\"}\n",
    "_ = DF.plot.barh(color=color_dict.values(), ax=ax)\n",
    "ax.set_title('Model train times with and without NVTabular on GPU')\n",
    "ax.set_xlabel('latency in seconds')\n",
    "# ax.set_ylabel('training\\nepoch\\nloop', rotation=0)\n",
    "# plt.suptitle(\"                  Train times with and without NVTabular on GPU\")\n",
    "ax.legend(loc='upper right')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles[::-1], labels[::-1])\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{figures_path}/perf_plot_nvt_train.png', dpi=600)\n",
    "\n",
    "# fig.subplots_adjust()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyt_a100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.barh(y=[0,1], width=[nvt_a100[0], pyt_a100[0]], color=['pink', 'lightgreen'])\n",
    "_=ax.set_yticklabels([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total run time\n",
    "print(nvt_time - total_start )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_time = [i - total_start for i in [data_prep_time, shap_time, pyt_time, pyt_data_prep_time, pyt_dl_time, nvt_time]]\n",
    "print(cum_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Tested on n a A100 32 GB GPU CUDA 11.2 Driver 460.32.03 on July 16, 2021\n",
    "# gpu_cum_time = [5.847876310348511, 77.14555883407593, 345.10619354248047, 347.9490294456482, 2602.528173685074, 2990.594742298126]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}