MOTOR_CLAIM_CHALLENGE_FV.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72dc1b87",
   "metadata": {},
   "source": [
    "# Motor Claim Challenge\n",
    "## Proposed Methodology\n",
    "<img src='methodology.png' width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bbebff",
   "metadata": {},
   "source": [
    "### Dataset Preparation\n",
    "\n",
    "This step consisted of observing the data structure and preparation in order to find null values, find outliers, and format the type of data present in each field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5640ca11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
    "\n",
    "import torch\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.impute import KNNImputer, SimpleImputer\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "from utils import setup_clf, setup_reg, train_tab_clf, train_tab_reg\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "becd88cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10302 entries, 0 to 10301\n",
      "Data columns (total 25 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   REFERENCE       10302 non-null  int64  \n",
      " 1   CHILD_DRIVE     10302 non-null  int64  \n",
      " 2   BIRTHDAY        10302 non-null  object \n",
      " 3   AGE             10295 non-null  float64\n",
      " 4   HOME_CHILDREN   10302 non-null  int64  \n",
      " 5   JOB_TENURE      9754 non-null   float64\n",
      " 6   INCOME          9732 non-null   float64\n",
      " 7   HOME_VAL        10302 non-null  float64\n",
      " 8   MARITAL_STATUS  10302 non-null  object \n",
      " 9   GENDER          10302 non-null  object \n",
      " 10  EDUCATION       10302 non-null  object \n",
      " 11  OCCUPATION      9637 non-null   object \n",
      " 12  TRAVTIME        10302 non-null  int64  \n",
      " 13  CAR_USE         10302 non-null  object \n",
      " 14  CAR_COST        10302 non-null  int64  \n",
      " 15  PTIF            10302 non-null  int64  \n",
      " 16  CAR_TYPE        10302 non-null  object \n",
      " 17  OLDCLAIM        10302 non-null  int64  \n",
      " 18  CLM_FREQ        10302 non-null  int64  \n",
      " 19  REVOKED         10302 non-null  object \n",
      " 20  MVR_PTS         10302 non-null  int64  \n",
      " 21  CLM_AMT         10302 non-null  int64  \n",
      " 22  CAR_AGE         9663 non-null   float64\n",
      " 23  CLAIM_FLAG      10302 non-null  int64  \n",
      " 24  CITY_RURAL      10302 non-null  object \n",
      "dtypes: float64(5), int64(11), object(9)\n",
      "memory usage: 2.0+ MB\n",
      "None\n",
      "8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "encoding = \"cp1252\"\n",
    "encoding = \"ISO-8859-1\"\n",
    "\n",
    "data = pd.read_csv(\"data.csv\" ,encoding= encoding)\n",
    "\n",
    "# Cleaning and transforming the data to float\n",
    "data['INCOME'] = data['INCOME'].str.replace(\",\",\"\")\n",
    "data['INCOME'] = data['INCOME'].str.replace(\"£\",\"\").astype(\"float64\")\n",
    "\n",
    "data['HOME_VAL'] = data['HOME_VAL'].str.replace(\",\",\"\")\n",
    "data['HOME_VAL'] = data['HOME_VAL'].str.replace(\"£\",\"\").astype(\"float64\")\n",
    "\n",
    "#Inputing missing income data values by getting the median based on gender and education\n",
    "#data['INCOME'] = data.groupby(['GENDER','EDUCATION'], sort= False)['INCOME'].apply(lambda x: x.ffill().bfill())\n",
    "\n",
    "#Analysing null values and data type information\n",
    "print(data.info())\n",
    "\n",
    "#Replacing infinity numbers\n",
    "data.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "#Dropping nan values\n",
    "#data = data.dropna()\n",
    "#data = data.reset_index()\n",
    "all_data = data.drop(columns=['REFERENCE','BIRTHDAY'])\n",
    "\n",
    "#getting categorical and numerical columns\n",
    "\n",
    "categorical_cols = [col for col in all_data.columns if (all_data[col].nunique() < 10) and \n",
    "                    (all_data[col].dtype == \"object\") and\n",
    "                    col not in ['CLM_AMT','CLAIM_FLAG']]\n",
    "\n",
    "numerical_cols = [col for col in all_data.columns if (all_data[col].dtype in ['int64', 'float64','float32']) and\n",
    "                  col not in ['CLM_AMT','CLAIM_FLAG']]\n",
    "print(len(categorical_cols))\n",
    "len(numerical_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b53568",
   "metadata": {},
   "source": [
    "#### Dealing with missing values\n",
    "In order to make full use of the data, an input of missing data based on the KNN model was performed for numeric values and a default value was placed on the categorical values.\n",
    "\n",
    "##### Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ed0cfb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10302 entries, 0 to 10301\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   CHILD_DRIVE    10302 non-null  float64\n",
      " 1   AGE            10302 non-null  float64\n",
      " 2   HOME_CHILDREN  10302 non-null  float64\n",
      " 3   JOB_TENURE     10302 non-null  float64\n",
      " 4   INCOME         10302 non-null  float64\n",
      " 5   HOME_VAL       10302 non-null  float64\n",
      " 6   TRAVTIME       10302 non-null  float64\n",
      " 7   CAR_COST       10302 non-null  float64\n",
      " 8   PTIF           10302 non-null  float64\n",
      " 9   OLDCLAIM       10302 non-null  float64\n",
      " 10  CLM_FREQ       10302 non-null  float64\n",
      " 11  MVR_PTS        10302 non-null  float64\n",
      " 12  CAR_AGE        10302 non-null  float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "knn_imputer = KNNImputer(n_neighbors=2)\n",
    "\n",
    "df = all_data[numerical_cols]\n",
    "df_imputed_num = pd.DataFrame(knn_imputer.fit_transform(df))\n",
    "df_imputed_num.columns = df.columns\n",
    "df_imputed_num.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4f1c0e",
   "metadata": {},
   "source": [
    "##### Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed0b804f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10302 entries, 0 to 10301\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   MARITAL_STATUS  10302 non-null  object\n",
      " 1   GENDER          10302 non-null  object\n",
      " 2   EDUCATION       10302 non-null  object\n",
      " 3   OCCUPATION      10302 non-null  object\n",
      " 4   CAR_USE         10302 non-null  object\n",
      " 5   CAR_TYPE        10302 non-null  object\n",
      " 6   REVOKED         10302 non-null  object\n",
      " 7   CITY_RURAL      10302 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 644.0+ KB\n"
     ]
    }
   ],
   "source": [
    "cat_imputer = SimpleImputer(strategy='constant', fill_value='MISSING')\n",
    "\n",
    "missing_cat_samples = all_data[all_data.isna().any(axis=1)].index.to_list()\n",
    "#df_inputed_cat = imputer_with_tester(all_data, categorical_cols, cat_imputer, missing_cat_samples)\n",
    "\n",
    "df = all_data[categorical_cols]\n",
    "df_imputed_cat = pd.DataFrame(cat_imputer.fit_transform(df))\n",
    "df_imputed_cat.columns = df.columns\n",
    "df_imputed_cat.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a692048f",
   "metadata": {},
   "source": [
    "### Data merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4035f4ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_processed = pd.concat([df_imputed_cat, df_imputed_num, all_data[['CLAIM_FLAG','CLM_AMT']]], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa4c2a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLAIM_FLAG    2746\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(all_data_processed[['CLAIM_FLAG']].sum())\n",
    "all_data_processed.head()\n",
    "all_data_processed.to_csv(\"data_proc.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a25abb",
   "metadata": {},
   "source": [
    "#### Target Visualization\n",
    "##### Claim Flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae20f57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "ax = all_data_processed[['CLAIM_FLAG']].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d29bfe5",
   "metadata": {},
   "source": [
    "#### Claim Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f457a47d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "#ax = all_data[all_data['CLM_AMT']<=20000]['CLM_AMT'].plot.hist()\n",
    "ax = all_data_processed['CLM_AMT'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0a5281",
   "metadata": {},
   "source": [
    "### Correlation Graph\n",
    "#### Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4268f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19b33a0c970>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "plt.figure(figsize=(16,14))\n",
    "df_imputed_concat_num = pd.concat([df_imputed_num,all_data[['CLAIM_FLAG','CLM_AMT']]],axis=1)\n",
    "sns.heatmap(df_imputed_concat_num.astype('float32').corr(), linewidths=0.1, vmax=1.0, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6e0cd68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19b0da2fd90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummies_cat = pd.DataFrame()\n",
    "for column in df_imputed_cat.columns:\n",
    "    dummies_cat[column] =  le.fit_transform(df_imputed_cat[column])\n",
    "df_imputed_concat_cat = pd.concat([dummies_cat,all_data[['CLAIM_FLAG','CLM_AMT']]],axis=1)\n",
    "plt.figure(figsize=(16,14))\n",
    "sns.heatmap(df_imputed_concat_cat.astype('float32').corr(), linewidths=0.1, vmax=1.0, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae8bd1f",
   "metadata": {},
   "source": [
    "### Model Selection\n",
    "\n",
    "For this problem, two models were chosen, the first one is a deep leaning model based on self-attention for tabular data, called TabNet, besides this model, a decision tree based model, XgBoost, a widely used solution to solve tabular data problems, was also chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95d46af",
   "metadata": {},
   "source": [
    "#### TabNet\n",
    "\n",
    "Developed by Google, the TabNet model is based on the idea of self-attachment introduced by transformer models for problem solving in NLP. According to the tests performed, this architecture was able to outperform the metrics obtained by other approaches in several benchmarks. \n",
    "\n",
    "The main advantage of using transformer-based depleaning models, besides its generalization power, is the interpretative capacity of the results, something that is currently gaining more and more importance, since understanding how intelligence algorithms make decisions can help us to clearly understand user behavior, for example. Thus, TabNet was selected for being able to give us the power of non-linear problem solving coming from deep neural network models, with the explainability that more classical models such as decision trees can give us.\n",
    "\n",
    "The use was made through the implementation below. In the utils.py file are written the functions that will be used below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e323b7",
   "metadata": {},
   "source": [
    "##### Task 1 - Predict Claim\n",
    "\n",
    "In this task, the main goal is to identify whether or not a customer will open a claim notice. Within the database used, this action is indicated by the attribute \"CLAIM_FLAG\". In order not to bias the data, the attribute \"CLM_AMT\", which indicates the amount requested by the insured, was also removed, since it is completely related to whether the customer has filed a claim or not. Finally, before training, the values were normalized between 0 and 1, to facilitate optimization of the weights of the neural network used.\n",
    "\n",
    "After normalizing the data, the TabNet model was trained for 200 epochs, with a patience of 20 epochs. The optimizer used was Adam, which uses an adaptive learning coefficient, thus avoiding local minima and accelerating model convergence.\n",
    "\n",
    "To evaluate the model's performance, the AUC (Area Under the Curve) metric was chosen, which indicates the model's ability to distinguish between two classes, being more reliable than other metrics such as accuracy or precision only. It tells you what is the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance. A value of AUC < 0.5 means that the model is performing no better than a random guess, so the closer to 1, the better the model's ability to distinguish. Besides the AUC metric, we also analyzed F1-Score, which is a harmonic mean between precision and recall (the ability of the model to identify the class of interest), which can help us get a better picture of how the models are doing, since it can be applied very well to unbalanced databases, such as ours.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10dc8d1",
   "metadata": {},
   "source": [
    "##### Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61fe825e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\boaro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pytorch_tabnet\\abstract_model.py:75: UserWarning: Device used : cpu\n",
      "  warnings.warn(f\"Device used : {self.device}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1031, 21)\n",
      "epoch 0  | loss: 0.86702 | train_auc: 0.52331 | valid_auc: 0.49769 |  0:00:01s\n",
      "epoch 1  | loss: 0.68303 | train_auc: 0.52814 | valid_auc: 0.53091 |  0:00:02s\n",
      "epoch 2  | loss: 0.66619 | train_auc: 0.55285 | valid_auc: 0.58308 |  0:00:03s\n",
      "epoch 3  | loss: 0.66157 | train_auc: 0.61312 | valid_auc: 0.62019 |  0:00:04s\n",
      "epoch 4  | loss: 0.65403 | train_auc: 0.64917 | valid_auc: 0.64568 |  0:00:05s\n",
      "epoch 5  | loss: 0.64048 | train_auc: 0.672   | valid_auc: 0.688   |  0:00:07s\n",
      "epoch 6  | loss: 0.63473 | train_auc: 0.67765 | valid_auc: 0.66238 |  0:00:08s\n",
      "epoch 7  | loss: 0.63111 | train_auc: 0.66673 | valid_auc: 0.66028 |  0:00:09s\n",
      "epoch 8  | loss: 0.63068 | train_auc: 0.66433 | valid_auc: 0.66863 |  0:00:10s\n",
      "epoch 9  | loss: 0.62576 | train_auc: 0.66593 | valid_auc: 0.6758  |  0:00:11s\n",
      "epoch 10 | loss: 0.61866 | train_auc: 0.68186 | valid_auc: 0.68161 |  0:00:12s\n",
      "epoch 11 | loss: 0.61115 | train_auc: 0.68452 | valid_auc: 0.68422 |  0:00:14s\n",
      "epoch 12 | loss: 0.61553 | train_auc: 0.68925 | valid_auc: 0.69197 |  0:00:15s\n",
      "epoch 13 | loss: 0.62086 | train_auc: 0.70626 | valid_auc: 0.70516 |  0:00:16s\n",
      "epoch 14 | loss: 0.61811 | train_auc: 0.69894 | valid_auc: 0.70141 |  0:00:17s\n",
      "epoch 15 | loss: 0.60861 | train_auc: 0.70888 | valid_auc: 0.70016 |  0:00:18s\n",
      "epoch 16 | loss: 0.6174  | train_auc: 0.74009 | valid_auc: 0.71142 |  0:00:19s\n",
      "epoch 17 | loss: 0.60069 | train_auc: 0.74647 | valid_auc: 0.71937 |  0:00:20s\n",
      "epoch 18 | loss: 0.608   | train_auc: 0.74911 | valid_auc: 0.73747 |  0:00:21s\n",
      "epoch 19 | loss: 0.606   | train_auc: 0.75403 | valid_auc: 0.75386 |  0:00:22s\n",
      "epoch 20 | loss: 0.60152 | train_auc: 0.75496 | valid_auc: 0.74413 |  0:00:24s\n",
      "epoch 21 | loss: 0.60553 | train_auc: 0.7602  | valid_auc: 0.76331 |  0:00:25s\n",
      "epoch 22 | loss: 0.5986  | train_auc: 0.76332 | valid_auc: 0.76296 |  0:00:26s\n",
      "epoch 23 | loss: 0.60557 | train_auc: 0.76621 | valid_auc: 0.77517 |  0:00:27s\n",
      "epoch 24 | loss: 0.59713 | train_auc: 0.77137 | valid_auc: 0.77424 |  0:00:28s\n",
      "epoch 25 | loss: 0.60935 | train_auc: 0.76932 | valid_auc: 0.7678  |  0:00:29s\n",
      "epoch 26 | loss: 0.60091 | train_auc: 0.77206 | valid_auc: 0.77302 |  0:00:30s\n",
      "epoch 27 | loss: 0.59603 | train_auc: 0.77566 | valid_auc: 0.77989 |  0:00:31s\n",
      "epoch 28 | loss: 0.59605 | train_auc: 0.77533 | valid_auc: 0.77311 |  0:00:32s\n",
      "epoch 29 | loss: 0.5952  | train_auc: 0.7759  | valid_auc: 0.77266 |  0:00:33s\n",
      "epoch 30 | loss: 0.59683 | train_auc: 0.77873 | valid_auc: 0.78172 |  0:00:34s\n",
      "epoch 31 | loss: 0.59423 | train_auc: 0.78297 | valid_auc: 0.78345 |  0:00:36s\n",
      "epoch 32 | loss: 0.58332 | train_auc: 0.78191 | valid_auc: 0.77385 |  0:00:37s\n",
      "epoch 33 | loss: 0.58996 | train_auc: 0.78751 | valid_auc: 0.78396 |  0:00:38s\n",
      "epoch 34 | loss: 0.57968 | train_auc: 0.79032 | valid_auc: 0.79173 |  0:00:39s\n",
      "epoch 35 | loss: 0.58227 | train_auc: 0.79029 | valid_auc: 0.79062 |  0:00:40s\n",
      "epoch 36 | loss: 0.58062 | train_auc: 0.79288 | valid_auc: 0.79046 |  0:00:41s\n",
      "epoch 37 | loss: 0.57396 | train_auc: 0.79125 | valid_auc: 0.78569 |  0:00:42s\n",
      "epoch 38 | loss: 0.57382 | train_auc: 0.79296 | valid_auc: 0.79086 |  0:00:43s\n",
      "epoch 39 | loss: 0.58255 | train_auc: 0.79537 | valid_auc: 0.79358 |  0:00:44s\n",
      "epoch 40 | loss: 0.58337 | train_auc: 0.79676 | valid_auc: 0.79392 |  0:00:45s\n",
      "epoch 41 | loss: 0.57877 | train_auc: 0.7966  | valid_auc: 0.79923 |  0:00:46s\n",
      "epoch 42 | loss: 0.57239 | train_auc: 0.79681 | valid_auc: 0.79333 |  0:00:48s\n",
      "epoch 43 | loss: 0.57129 | train_auc: 0.79494 | valid_auc: 0.79743 |  0:00:49s\n",
      "epoch 44 | loss: 0.57396 | train_auc: 0.79458 | valid_auc: 0.79147 |  0:00:50s\n",
      "epoch 45 | loss: 0.57281 | train_auc: 0.79182 | valid_auc: 0.7835  |  0:00:51s\n",
      "epoch 46 | loss: 0.56009 | train_auc: 0.79597 | valid_auc: 0.78634 |  0:00:52s\n",
      "epoch 47 | loss: 0.56724 | train_auc: 0.79782 | valid_auc: 0.78143 |  0:00:53s\n",
      "epoch 48 | loss: 0.57104 | train_auc: 0.79747 | valid_auc: 0.78612 |  0:00:55s\n",
      "epoch 49 | loss: 0.56792 | train_auc: 0.7952  | valid_auc: 0.78208 |  0:00:56s\n",
      "epoch 50 | loss: 0.57202 | train_auc: 0.79335 | valid_auc: 0.784   |  0:00:57s\n",
      "epoch 51 | loss: 0.577   | train_auc: 0.7956  | valid_auc: 0.7798  |  0:00:58s\n",
      "epoch 52 | loss: 0.56582 | train_auc: 0.79905 | valid_auc: 0.77944 |  0:00:59s\n",
      "epoch 53 | loss: 0.57695 | train_auc: 0.80259 | valid_auc: 0.77902 |  0:01:00s\n",
      "epoch 54 | loss: 0.57177 | train_auc: 0.80185 | valid_auc: 0.78161 |  0:01:02s\n",
      "epoch 55 | loss: 0.57347 | train_auc: 0.80387 | valid_auc: 0.78452 |  0:01:03s\n",
      "epoch 56 | loss: 0.57882 | train_auc: 0.8072  | valid_auc: 0.78513 |  0:01:04s\n",
      "epoch 57 | loss: 0.56292 | train_auc: 0.80741 | valid_auc: 0.78614 |  0:01:05s\n",
      "epoch 58 | loss: 0.56631 | train_auc: 0.80967 | valid_auc: 0.78407 |  0:01:06s\n",
      "epoch 59 | loss: 0.56591 | train_auc: 0.80735 | valid_auc: 0.78488 |  0:01:07s\n",
      "epoch 60 | loss: 0.56251 | train_auc: 0.80679 | valid_auc: 0.78688 |  0:01:08s\n",
      "epoch 61 | loss: 0.57027 | train_auc: 0.81044 | valid_auc: 0.79357 |  0:01:09s\n",
      "\n",
      "Early stopping occurred at epoch 61 with best_epoch = 41 and best_valid_auc = 0.79923\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\boaro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pytorch_tabnet\\callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
      "  warnings.warn(wrn_msg)\n"
     ]
    }
   ],
   "source": [
    "# Deal with categorical data, and return a train, test, valid split, along with the TabNet model and feature list names\n",
    "#X_train, y_train, X_valid,y_valid, X_test, y_test, clf, features = setup_clf(all_data_processed.drop(columns=['CLM_AMT']),'CLAIM_FLAG')\n",
    "X_train, y_train, X_valid,y_valid, X_test, y_test, clf, features = setup_clf(all_data_processed.drop(columns=['CLM_AMT']),'CLAIM_FLAG')\n",
    "\n",
    "\n",
    "print(X_test.shape)\n",
    "scaler = MinMaxScaler()\n",
    "  \n",
    "#X_train = scaler.fit_transform(X_train) \n",
    "#X_valid = scaler.fit_transform(X_valid) \n",
    "#X_test = scaler.fit_transform(X_test) \n",
    "\n",
    "# Training\n",
    "save_history = train_tab_clf(X_train, y_train, X_valid,y_valid, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1835f31",
   "metadata": {},
   "source": [
    "##### Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d78b658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19b0da3e7c0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clf.history['loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbead1b2",
   "metadata": {},
   "source": [
    "##### AUC visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5665bfe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19b0da2b3d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clf.history['train_auc'])\n",
    "plt.plot(clf.history['valid_auc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3643301",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c30088be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST VALID SCORE FOR Motor_Ensurace : 0.7992340639959687\n",
      "FINAL TEST SCORE FOR Motor_Ensurace : 0.7829998080429983\n"
     ]
    }
   ],
   "source": [
    "dataset_name = 'Motor_Ensurace'\n",
    "preds = clf.predict_proba(X_test)\n",
    "\n",
    "test_auc = roc_auc_score(y_score=preds[:,1], y_true=y_test)\n",
    "\n",
    "\n",
    "\n",
    "preds_valid = clf.predict_proba(X_valid)\n",
    "valid_auc = roc_auc_score(y_score=preds_valid[:,1], y_true=y_valid)\n",
    "\n",
    "print(f\"BEST VALID SCORE FOR {dataset_name} : {clf.best_cost}\")\n",
    "print(f\"FINAL TEST SCORE FOR {dataset_name} : {test_auc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71fa1665",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.74      0.80       755\n",
      "           1       0.50      0.70      0.58       276\n",
      "\n",
      "    accuracy                           0.73      1031\n",
      "   macro avg       0.69      0.72      0.69      1031\n",
      "weighted avg       0.77      0.73      0.74      1031\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "preds_binary = clf.predict(X_test)\n",
    "print(classification_report(y_test,preds_binary,))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db51875f",
   "metadata": {},
   "source": [
    "##### Plotting AUC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5704de80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "\n",
    "preds = preds[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a1d5b7",
   "metadata": {},
   "source": [
    "##### Feature importance\n",
    "\n",
    "After training and evaluating the model, we can then see the features that mattered most for the model's decision making"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca7f92ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19b0e31aac0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pd.Series(clf.feature_importances_, index=features).nlargest(10).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977495e5",
   "metadata": {},
   "source": [
    "##### Mask Visualization\n",
    "\n",
    "As said before, one of the advantages of using a transformer-based model is its interpretability. That said, below we can visualize the attention masks (activations) for 50 examples. Each row means an example and each column an attribute, the color meaning the importance of the attribute in the classification by the model. Each of the maps shows the attributes that activated the most in each learning stage of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4cc8747",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8266, 21)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1584 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "xplain_matrix, masks_clf = clf.explain(X_train)\n",
    "fig, axs = plt.subplots(1, 3, figsize=(22,22))\n",
    "print(masks_clf[0].shape)\n",
    "for i in range(3):\n",
    "    axs[i].imshow(masks_clf[i][:50])\n",
    "    axs[i].set_title(f\"mask {i}\")\n",
    "    axs[i].set_xticks([i for i,_ in enumerate(features)])\n",
    "    axs[i].set_xticklabels(features,rotation='vertical')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c52b7d",
   "metadata": {},
   "source": [
    "### XGBoost\n",
    "\n",
    "Running a XGBoost model for comparision os results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "22d3a9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\boaro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.65534\n",
      "[10]\tvalidation_0-logloss:0.48846\n",
      "[20]\tvalidation_0-logloss:0.44631\n",
      "[30]\tvalidation_0-logloss:0.43172\n",
      "[40]\tvalidation_0-logloss:0.42862\n",
      "[50]\tvalidation_0-logloss:0.42631\n",
      "[60]\tvalidation_0-logloss:0.42916\n",
      "[70]\tvalidation_0-logloss:0.43297\n",
      "[80]\tvalidation_0-logloss:0.43414\n",
      "[86]\tvalidation_0-logloss:0.43525\n",
      "Valid_auc:  0.8395817586293777\n",
      "Test_auc:  0.8102505038871292\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.91      0.85       755\n",
      "           1       0.62      0.39      0.48       276\n",
      "\n",
      "    accuracy                           0.77      1031\n",
      "   macro avg       0.71      0.65      0.67      1031\n",
      "weighted avg       0.75      0.77      0.75      1031\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "clf_xgb = XGBClassifier(max_depth=8,\n",
    "    learning_rate=0.1,\n",
    "    n_estimators=1000,\n",
    "    verbosity=0,\n",
    "    silent=None,\n",
    "    objective='binary:logistic',\n",
    "    booster='gbtree',\n",
    "    n_jobs=-1,\n",
    "    nthread=None,\n",
    "    gamma=0,\n",
    "    min_child_weight=1,\n",
    "    max_delta_step=0,\n",
    "    subsample=0.7,\n",
    "    colsample_bytree=1,\n",
    "    colsample_bylevel=1,\n",
    "    colsample_bynode=1,\n",
    "    reg_alpha=0,\n",
    "    reg_lambda=1,\n",
    "    scale_pos_weight=1,\n",
    "    base_score=0.5,\n",
    "    random_state=0,\n",
    "    seed=None,)\n",
    "\n",
    "clf_xgb.fit(X_train, y_train,\n",
    "        eval_set=[(X_valid, y_valid)],\n",
    "        early_stopping_rounds=40,\n",
    "        verbose=10)\n",
    "\n",
    "preds = np.array(clf_xgb.predict_proba(X_valid))\n",
    "valid_auc = roc_auc_score(y_score=preds[:,1], y_true=y_valid)\n",
    "print(\"Valid_auc: \",valid_auc)\n",
    "\n",
    "preds = np.array(clf_xgb.predict_proba(X_test))\n",
    "test_auc = roc_auc_score(y_score=preds[:,1], y_true=y_test)\n",
    "print(\"Test_auc: \",test_auc)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "preds_binary = clf_xgb.predict(X_test)\n",
    "print(classification_report(y_test,preds_binary))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f8ce0c",
   "metadata": {},
   "source": [
    "As we can see, the values obtained by TabNet are very close to the values obtained by XgBoost, with a variation of 1% in f1-score metric and 3% in AUC. The TabNet training proved to be more stable since the difference between the AUC values obtained in training and validation was smaller than those obtained by XGBoost."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ed128c",
   "metadata": {},
   "source": [
    "##### Task 2 - Predict Claim Amount\n",
    "\n",
    "This task consisted of giving a customer who has filed a claim notice, what amount he will claim. In the database used this value is indicated by the field \"CLM_AMT\". For this step, the data was also normalized between 0 and 1.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b732d12",
   "metadata": {},
   "source": [
    "##### Model Training\n",
    "Training was performed for 100 epochs and 20 patience epochs. The model was evaluated using the metrics mse and rmse, which measure the mean squared error and the square root of the mean squared error respectively.\n",
    "\n",
    "For training, only samples with amounts below 20,000 were selected, given the lack of sufficient data from amounts above this value (outliers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "18ce3f4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MARITAL_STATUS 2\n",
      "GENDER 2\n",
      "EDUCATION 5\n",
      "OCCUPATION 9\n",
      "CAR_USE 2\n",
      "CAR_TYPE 6\n",
      "REVOKED 2\n",
      "CITY_RURAL 2\n",
      "Set 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\boaro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pytorch_tabnet\\abstract_model.py:75: UserWarning: Device used : cpu\n",
      "  warnings.warn(f\"Device used : {self.device}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0  | loss: 27409616.30245| train_mae: 4431.48478| valid_mae: 4144.94881|  0:00:01s\n",
      "epoch 1  | loss: 27849056.19299| train_mae: 4430.93836| valid_mae: 4144.43336|  0:00:02s\n",
      "epoch 2  | loss: 27581300.41287| train_mae: 4430.08748| valid_mae: 4143.6549|  0:00:03s\n",
      "epoch 3  | loss: 27464253.85214| train_mae: 4429.20973| valid_mae: 4142.82729|  0:00:05s\n",
      "epoch 4  | loss: 26915097.15602| train_mae: 4427.8457| valid_mae: 4141.44464|  0:00:06s\n",
      "epoch 5  | loss: 27450686.4023| train_mae: 4425.96535| valid_mae: 4139.65075|  0:00:08s\n",
      "epoch 6  | loss: 27581586.82669| train_mae: 4423.668| valid_mae: 4137.27761|  0:00:09s\n",
      "epoch 7  | loss: 26919593.71195| train_mae: 4420.52669| valid_mae: 4133.89647|  0:00:10s\n",
      "epoch 8  | loss: 27430391.90398| train_mae: 4416.81099| valid_mae: 4130.63777|  0:00:12s\n",
      "epoch 9  | loss: 27564283.02544| train_mae: 4412.69103| valid_mae: 4126.47517|  0:00:13s\n",
      "epoch 10 | loss: 27441509.45367| train_mae: 4408.62939| valid_mae: 4122.27047|  0:00:14s\n",
      "epoch 11 | loss: 27157445.68123| train_mae: 4403.52339| valid_mae: 4116.59189|  0:00:16s\n",
      "epoch 12 | loss: 27204299.46135| train_mae: 4397.93109| valid_mae: 4111.28618|  0:00:17s\n",
      "epoch 13 | loss: 27237600.21027| train_mae: 4392.12191| valid_mae: 4105.75833|  0:00:18s\n",
      "epoch 14 | loss: 26979646.62698| train_mae: 4386.02527| valid_mae: 4099.22063|  0:00:20s\n",
      "epoch 15 | loss: 27209978.51464| train_mae: 4379.25213| valid_mae: 4093.05624|  0:00:21s\n",
      "epoch 16 | loss: 27162479.5084| train_mae: 4371.11889| valid_mae: 4085.98599|  0:00:22s\n",
      "epoch 17 | loss: 26877474.35238| train_mae: 4362.55173| valid_mae: 4076.52625|  0:00:24s\n",
      "epoch 18 | loss: 27208428.55209| train_mae: 4350.29552| valid_mae: 4062.25895|  0:00:25s\n",
      "epoch 19 | loss: 27037597.99232| train_mae: 4342.21166| valid_mae: 4054.70211|  0:00:27s\n",
      "epoch 20 | loss: 27037507.79549| train_mae: 4335.58134| valid_mae: 4047.51307|  0:00:28s\n",
      "epoch 21 | loss: 26856341.54777| train_mae: 4327.73968| valid_mae: 4039.64964|  0:00:29s\n",
      "epoch 22 | loss: 26569332.95247| train_mae: 4322.08308| valid_mae: 4033.15329|  0:00:31s\n",
      "epoch 23 | loss: 26183497.13586| train_mae: 4313.09602| valid_mae: 4024.3224|  0:00:32s\n",
      "epoch 24 | loss: 26581580.80365| train_mae: 4301.4662| valid_mae: 4013.19028|  0:00:33s\n",
      "epoch 25 | loss: 26609821.90494| train_mae: 4289.49099| valid_mae: 4001.73538|  0:00:35s\n",
      "epoch 26 | loss: 26393082.58185| train_mae: 4276.88463| valid_mae: 3991.08456|  0:00:36s\n",
      "epoch 27 | loss: 26427621.03217| train_mae: 4265.86361| valid_mae: 3980.76426|  0:00:37s\n",
      "epoch 28 | loss: 26323864.98608| train_mae: 4255.68854| valid_mae: 3972.09131|  0:00:39s\n",
      "epoch 29 | loss: 26323323.73884| train_mae: 4245.01634| valid_mae: 3962.45028|  0:00:40s\n",
      "epoch 30 | loss: 26173614.9928| train_mae: 4231.68574| valid_mae: 3948.58639|  0:00:41s\n",
      "epoch 31 | loss: 25719598.95919| train_mae: 4219.38299| valid_mae: 3936.04325|  0:00:43s\n",
      "epoch 32 | loss: 25501252.43399| train_mae: 4207.53704| valid_mae: 3924.8182|  0:00:44s\n",
      "epoch 33 | loss: 25214490.16995| train_mae: 4198.59992| valid_mae: 3914.61217|  0:00:45s\n",
      "epoch 34 | loss: 25654003.11954| train_mae: 4187.81728| valid_mae: 3902.95562|  0:00:47s\n",
      "epoch 35 | loss: 25560499.00528| train_mae: 4170.53214| valid_mae: 3886.7544|  0:00:48s\n",
      "epoch 36 | loss: 24846284.35526| train_mae: 4149.03921| valid_mae: 3866.67231|  0:00:49s\n",
      "epoch 37 | loss: 25398690.60202| train_mae: 4125.61717| valid_mae: 3844.48433|  0:00:51s\n",
      "epoch 38 | loss: 25216991.94719| train_mae: 4101.37545| valid_mae: 3821.38493|  0:00:52s\n",
      "epoch 39 | loss: 25036647.63514| train_mae: 4072.45798| valid_mae: 3790.49629|  0:00:54s\n",
      "epoch 40 | loss: 24654023.10034| train_mae: 4047.3667| valid_mae: 3763.94687|  0:00:55s\n",
      "epoch 41 | loss: 24714578.04801| train_mae: 4034.03618| valid_mae: 3749.03061|  0:00:56s\n",
      "epoch 42 | loss: 24298862.41671| train_mae: 4018.44892| valid_mae: 3733.74357|  0:00:58s\n",
      "epoch 43 | loss: 24359011.33845| train_mae: 3997.28122| valid_mae: 3713.06345|  0:00:59s\n",
      "epoch 44 | loss: 24281910.04465| train_mae: 3999.6778| valid_mae: 3716.33974|  0:01:00s\n",
      "epoch 45 | loss: 24283474.76524| train_mae: 3971.47152| valid_mae: 3689.37714|  0:01:02s\n",
      "epoch 46 | loss: 23712228.38022| train_mae: 3941.92083| valid_mae: 3659.74919|  0:01:03s\n",
      "epoch 47 | loss: 23998833.91743| train_mae: 3941.11276| valid_mae: 3656.56157|  0:01:04s\n",
      "epoch 48 | loss: 23390126.72636| train_mae: 3931.98078| valid_mae: 3646.92875|  0:01:06s\n",
      "epoch 49 | loss: 23478044.46375| train_mae: 3907.74599| valid_mae: 3623.06194|  0:01:07s\n",
      "epoch 50 | loss: 23187526.8987| train_mae: 3885.64757| valid_mae: 3604.67242|  0:01:08s\n",
      "epoch 51 | loss: 22728829.81805| train_mae: 3841.45421| valid_mae: 3556.15682|  0:01:10s\n",
      "epoch 52 | loss: 22732533.53| train_mae: 3798.57868| valid_mae: 3507.64574|  0:01:11s\n",
      "epoch 53 | loss: 22707796.80365| train_mae: 3769.85728| valid_mae: 3481.20253|  0:01:12s\n",
      "epoch 54 | loss: 22541156.36102| train_mae: 3745.44034| valid_mae: 3460.72774|  0:01:14s\n",
      "epoch 55 | loss: 22056093.4844| train_mae: 3712.54092| valid_mae: 3434.17575|  0:01:15s\n",
      "epoch 56 | loss: 22162115.06385| train_mae: 3682.41508| valid_mae: 3406.07642|  0:01:16s\n",
      "epoch 57 | loss: 22020144.62218| train_mae: 3674.47949| valid_mae: 3400.58894|  0:01:18s\n",
      "epoch 58 | loss: 21964867.20499| train_mae: 3669.82345| valid_mae: 3394.14609|  0:01:19s\n",
      "epoch 59 | loss: 21623337.15506| train_mae: 3666.46627| valid_mae: 3387.04596|  0:01:21s\n",
      "epoch 60 | loss: 21384694.0941| train_mae: 3662.4833| valid_mae: 3380.28867|  0:01:22s\n",
      "epoch 61 | loss: 21248965.40182| train_mae: 3651.69484| valid_mae: 3374.78003|  0:01:23s\n",
      "epoch 62 | loss: 20829039.14258| train_mae: 3628.22289| valid_mae: 3355.58345|  0:01:25s\n",
      "epoch 63 | loss: 20968635.29045| train_mae: 3571.59038| valid_mae: 3295.74216|  0:01:26s\n",
      "epoch 64 | loss: 20736928.88622| train_mae: 3521.40262| valid_mae: 3243.20622|  0:01:27s\n",
      "epoch 65 | loss: 20321167.03217| train_mae: 3476.80579| valid_mae: 3205.01469|  0:01:29s\n",
      "epoch 66 | loss: 20237880.47528| train_mae: 3455.88513| valid_mae: 3190.52222|  0:01:30s\n",
      "epoch 67 | loss: 20040580.17427| train_mae: 3433.55113| valid_mae: 3167.81286|  0:01:32s\n",
      "epoch 68 | loss: 20240310.01824| train_mae: 3413.53521| valid_mae: 3148.25732|  0:01:33s\n",
      "epoch 69 | loss: 19754651.2674| train_mae: 3404.9722| valid_mae: 3132.0243|  0:01:34s\n",
      "epoch 70 | loss: 19444399.72252| train_mae: 3394.7526| valid_mae: 3126.29072|  0:01:36s\n",
      "epoch 71 | loss: 19300335.65194| train_mae: 3382.82 | valid_mae: 3108.81628|  0:01:37s\n",
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      "epoch 73 | loss: 18848567.44887| train_mae: 3340.03829| valid_mae: 3070.47185|  0:01:39s\n",
      "epoch 74 | loss: 18512452.6481| train_mae: 3322.79565| valid_mae: 3052.70081|  0:01:41s\n",
      "epoch 75 | loss: 18427368.2746| train_mae: 3275.07877| valid_mae: 3020.61634|  0:01:42s\n",
      "epoch 76 | loss: 17984008.97216| train_mae: 3233.61297| valid_mae: 2981.75741|  0:01:43s\n",
      "epoch 77 | loss: 18151998.12962| train_mae: 3180.56878| valid_mae: 2939.92728|  0:01:45s\n",
      "epoch 78 | loss: 17678109.06961| train_mae: 3149.71907| valid_mae: 2916.28482|  0:01:46s\n",
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      "epoch 80 | loss: 17395205.40759| train_mae: 3111.25186| valid_mae: 2867.67789|  0:01:49s\n",
      "epoch 81 | loss: 17328379.71483| train_mae: 3075.08053| valid_mae: 2829.07944|  0:01:50s\n",
      "epoch 82 | loss: 16872738.10754| train_mae: 3047.26045| valid_mae: 2801.09373|  0:01:51s\n",
      "epoch 83 | loss: 16932660.52328| train_mae: 3034.46866| valid_mae: 2799.74271|  0:01:52s\n",
      "epoch 84 | loss: 16526091.66587| train_mae: 3078.59405| valid_mae: 2838.14338|  0:01:54s\n",
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      "epoch 87 | loss: 15915692.98608| train_mae: 3038.53612| valid_mae: 2780.85809|  0:01:57s\n",
      "epoch 88 | loss: 15760875.75132| train_mae: 2970.76313| valid_mae: 2721.22496|  0:01:59s\n",
      "epoch 89 | loss: 16027498.0048| train_mae: 2900.21546| valid_mae: 2671.48045|  0:02:00s\n",
      "epoch 90 | loss: 15535776.25156| train_mae: 2840.76924| valid_mae: 2607.81472|  0:02:01s\n",
      "epoch 91 | loss: 15309659.59674| train_mae: 2792.7882| valid_mae: 2568.97295|  0:02:03s\n",
      "epoch 92 | loss: 15039226.05497| train_mae: 2770.36264| valid_mae: 2550.20405|  0:02:04s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 93 | loss: 15108312.80557| train_mae: 2760.73749| valid_mae: 2535.8457|  0:02:05s\n",
      "epoch 94 | loss: 14728404.41239| train_mae: 2736.52467| valid_mae: 2510.46203|  0:02:07s\n",
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      "epoch 96 | loss: 14073426.81517| train_mae: 2636.78762| valid_mae: 2456.95118|  0:02:09s\n",
      "epoch 97 | loss: 14339189.49976| train_mae: 2613.93915| valid_mae: 2434.0762|  0:02:10s\n",
      "epoch 98 | loss: 14091298.52616| train_mae: 2607.11871| valid_mae: 2422.84277|  0:02:12s\n",
      "epoch 99 | loss: 13880171.2386| train_mae: 2578.91202| valid_mae: 2400.61236|  0:02:13s\n",
      "epoch 100| loss: 13828324.6505| train_mae: 2558.50657| valid_mae: 2387.49366|  0:02:14s\n",
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      "epoch 109| loss: 11803878.1301| train_mae: 2450.69326| valid_mae: 2279.10611|  0:02:26s\n",
      "epoch 110| loss: 11853941.66875| train_mae: 2391.95612| valid_mae: 2239.31276|  0:02:27s\n",
      "epoch 111| loss: 11740983.36918| train_mae: 2345.53725| valid_mae: 2202.66746|  0:02:28s\n",
      "epoch 112| loss: 11563852.66251| train_mae: 2320.84164| valid_mae: 2177.88257|  0:02:30s\n",
      "epoch 113| loss: 11448539.48656| train_mae: 2317.1665| valid_mae: 2185.99383|  0:02:31s\n",
      "epoch 114| loss: 11172986.5132| train_mae: 2313.48938| valid_mae: 2183.91478|  0:02:32s\n",
      "epoch 115| loss: 11192136.6529| train_mae: 2321.73148| valid_mae: 2194.34795|  0:02:33s\n",
      "epoch 116| loss: 11047458.70571| train_mae: 2312.58493| valid_mae: 2194.03055|  0:02:35s\n",
      "epoch 117| loss: 10832031.80413| train_mae: 2307.49659| valid_mae: 2175.03343|  0:02:36s\n",
      "epoch 118| loss: 10937290.78253| train_mae: 2310.45056| valid_mae: 2188.81641|  0:02:37s\n",
      "epoch 119| loss: 10737788.1301| train_mae: 2282.25871| valid_mae: 2149.53392|  0:02:39s\n",
      "epoch 120| loss: 10232013.04513| train_mae: 2227.87462| valid_mae: 2125.19731|  0:02:40s\n",
      "epoch 121| loss: 10695719.49592| train_mae: 2193.59446| valid_mae: 2104.38277|  0:02:41s\n",
      "epoch 122| loss: 10112501.6493| train_mae: 2167.30395| valid_mae: 2086.6674|  0:02:43s\n",
      "epoch 123| loss: 10282264.90518| train_mae: 2138.40739| valid_mae: 2062.31416|  0:02:44s\n",
      "epoch 124| loss: 10041766.97864| train_mae: 2098.82675| valid_mae: 2025.95531|  0:02:45s\n",
      "epoch 125| loss: 10053795.08569| train_mae: 2071.95419| valid_mae: 1997.18192|  0:02:47s\n",
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      "epoch 127| loss: 9869227.99808| train_mae: 2053.82692| valid_mae: 1980.9031|  0:02:49s\n",
      "epoch 128| loss: 9664328.08161| train_mae: 2059.69186| valid_mae: 2001.22364|  0:02:51s\n",
      "epoch 129| loss: 9408086.57753| train_mae: 2071.08985| valid_mae: 2039.88478|  0:02:52s\n",
      "epoch 130| loss: 9536092.96111| train_mae: 2080.35688| valid_mae: 2024.04575|  0:02:54s\n",
      "epoch 131| loss: 9387991.11762| train_mae: 2092.69939| valid_mae: 2017.44474|  0:02:55s\n",
      "epoch 132| loss: 9105110.64762| train_mae: 2091.75135| valid_mae: 2033.10521|  0:02:57s\n",
      "epoch 133| loss: 9337300.16491| train_mae: 2098.9239| valid_mae: 2048.56174|  0:02:58s\n",
      "epoch 134| loss: 9143696.61666| train_mae: 2077.0355| valid_mae: 2041.84405|  0:02:59s\n",
      "epoch 135| loss: 9160002.95007| train_mae: 2036.6194| valid_mae: 2008.68682|  0:03:01s\n",
      "epoch 136| loss: 9120255.82477| train_mae: 2006.12254| valid_mae: 1969.55568|  0:03:02s\n",
      "epoch 137| loss: 8351205.3927| train_mae: 1989.20029| valid_mae: 1960.59001|  0:03:03s\n",
      "epoch 138| loss: 8701279.03325| train_mae: 1980.84838| valid_mae: 1956.436|  0:03:05s\n",
      "epoch 139| loss: 8752740.36054| train_mae: 1979.31011| valid_mae: 1952.09567|  0:03:06s\n",
      "epoch 140| loss: 8766024.9976| train_mae: 1976.56878| valid_mae: 1952.4029|  0:03:07s\n",
      "epoch 141| loss: 8794027.60742| train_mae: 1973.41883| valid_mae: 1954.01493|  0:03:08s\n",
      "epoch 142| loss: 8599990.57609| train_mae: 1985.36599| valid_mae: 1965.61426|  0:03:10s\n",
      "epoch 143| loss: 8603923.47768| train_mae: 1996.80715| valid_mae: 1967.54837|  0:03:11s\n",
      "epoch 144| loss: 8401282.95487| train_mae: 1993.8155| valid_mae: 1971.42209|  0:03:12s\n",
      "epoch 145| loss: 8299991.69659| train_mae: 1981.20147| valid_mae: 1969.69102|  0:03:14s\n",
      "epoch 146| loss: 8135562.83941| train_mae: 1965.86012| valid_mae: 1958.11482|  0:03:15s\n",
      "epoch 147| loss: 8505280.60682| train_mae: 1953.90623| valid_mae: 1956.83359|  0:03:16s\n",
      "epoch 148| loss: 8119768.22792| train_mae: 1952.26756| valid_mae: 1956.22202|  0:03:17s\n",
      "epoch 149| loss: 8309121.03024| train_mae: 1948.80646| valid_mae: 1954.03528|  0:03:19s\n",
      "epoch 150| loss: 8239170.86366| train_mae: 1946.74436| valid_mae: 1954.55582|  0:03:20s\n",
      "epoch 151| loss: 8345425.0144| train_mae: 1945.9278| valid_mae: 1955.67785|  0:03:21s\n",
      "epoch 152| loss: 8211832.26884| train_mae: 1944.6612| valid_mae: 1954.39502|  0:03:23s\n",
      "epoch 153| loss: 8069650.22228| train_mae: 1945.90794| valid_mae: 1958.95485|  0:03:24s\n",
      "epoch 154| loss: 7959632.98176| train_mae: 1950.59885| valid_mae: 1974.97122|  0:03:26s\n",
      "epoch 155| loss: 8180405.02688| train_mae: 1953.29223| valid_mae: 1988.17032|  0:03:27s\n",
      "epoch 156| loss: 8086950.74724| train_mae: 1943.19287| valid_mae: 1965.3572|  0:03:28s\n",
      "epoch 157| loss: 8232858.73452| train_mae: 1941.65791| valid_mae: 1963.61893|  0:03:30s\n",
      "epoch 158| loss: 7971463.74844| train_mae: 1940.46122| valid_mae: 1964.47353|  0:03:31s\n",
      "epoch 159| loss: 8037094.0965| train_mae: 1943.14388| valid_mae: 1958.31202|  0:03:32s\n",
      "\n",
      "Early stopping occurred at epoch 159 with best_epoch = 139 and best_valid_mae = 1952.09567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\boaro\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pytorch_tabnet\\callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
      "  warnings.warn(wrn_msg)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_proc = all_data_processed[(all_data_processed['CLM_AMT']<=20000) & all_data_processed['CLAIM_FLAG']==1].reset_index()\n",
    "X_train, y_train, X_valid,y_valid, X_test, y_test, clf, features = setup_reg(data_proc.drop(columns=['index']),'CLM_AMT',task='regression')\n",
    "\n",
    " \n",
    "save_history = train_tab_reg(X_train, y_train, X_valid,y_valid, clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee7d8da",
   "metadata": {},
   "source": [
    "##### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e44912d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST VALID SCORE FOR REGRESSION : 1952.0956661072878\n",
      "FINAL TEST SCORE FOR REGRESSION : 1958.45831450873\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "preds = clf.predict(X_test)\n",
    "\n",
    "y_true = y_test\n",
    "\n",
    "test_score = mean_absolute_error(y_pred=preds, y_true=y_true)\n",
    "\n",
    "print(f\"BEST VALID SCORE FOR REGRESSION : {clf.best_cost}\")\n",
    "print(f\"FINAL TEST SCORE FOR REGRESSION : {test_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa911974",
   "metadata": {},
   "source": [
    "##### Feature importance\n",
    "\n",
    "Let's see how important the attributes are for the prediction of the claimed value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9e4ba5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19b0bf434f0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pd.Series(clf.feature_importances_, index=features).nlargest(10).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af308dad",
   "metadata": {},
   "source": [
    "As we can see, the attribute that had the most importance for the prediction model was car use, which indicates whether the car is for private or commercial use, followed by the driver's occupation. Besides these, gender and relationship status also had a notable importance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1265140",
   "metadata": {},
   "source": [
    "##### Mask Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b7fd2c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1584 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xplain_matrix, masks = clf.explain(X_train)\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(22,22))\n",
    "\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].imshow(masks[i][:50])\n",
    "    axs[i].set_title(f\"mask {i}\")\n",
    "    axs[i].set_xticks([i for i,_ in enumerate(features)])\n",
    "    axs[i].set_xticklabels(features,rotation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b81f914",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57022541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:4610.03711\n",
      "[10]\tvalidation_0-rmse:2994.99414\n",
      "[20]\tvalidation_0-rmse:2817.80054\n",
      "[30]\tvalidation_0-rmse:2828.62256\n",
      "[40]\tvalidation_0-rmse:2871.73999\n",
      "[50]\tvalidation_0-rmse:2924.38403\n",
      "[60]\tvalidation_0-rmse:2956.15820\n",
      "[66]\tvalidation_0-rmse:2968.78980\n",
      "BEST VALID SCORE FOR REGRESSION : 2008.2069105017486\n",
      "FINAL TEST SCORE FOR REGRESSION : 1979.930397332351\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#data_proc = ll_data_processed[(ll_data_processed['CLM_AMT']<=20000) & all_data_processed['CLAIM_FLAG']==1].reset_index()\n",
    "#X_train, y_train, X_valid,y_valid, X_test, y_test, clf = setup_reg(data_proc,'CLM_AMT',task='regression')\n",
    "\n",
    "#X_train, y_train, X_valid,y_valid, X_test, y_test, clf = setup_reg(all_data,'CLM_AMT')\n",
    "\n",
    "#scaler = MinMaxScaler()\n",
    "  \n",
    "#X_train, y_train = scaler.fit_transform(X_train) ,scaler.fit_transform(y_train)\n",
    "#X_valid, y_valid = scaler.fit_transform(X_valid) ,scaler.fit_transform(y_valid)\n",
    "#X_test, y_test = scaler.fit_transform(X_test) ,scaler.fit_transform(y_test)\n",
    "\n",
    "clf_xgb = XGBRegressor(max_depth=8,\n",
    "    learning_rate=0.1,\n",
    "    n_estimators=1000,\n",
    "    verbosity=0,\n",
    "    silent=None,\n",
    "    objective='reg:linear',\n",
    "    booster='gbtree',\n",
    "    n_jobs=-1,\n",
    "    nthread=None,\n",
    "    gamma=0,\n",
    "    min_child_weight=1,\n",
    "    max_delta_step=0,\n",
    "    subsample=0.7,\n",
    "    colsample_bytree=1,\n",
    "    colsample_bylevel=1,\n",
    "    colsample_bynode=1,\n",
    "    reg_alpha=0,\n",
    "    reg_lambda=1,\n",
    "    scale_pos_weight=1,\n",
    "    base_score=0.5,\n",
    "    random_state=0,\n",
    "    seed=None,)\n",
    "\n",
    "clf_xgb.fit(X_train, y_train,\n",
    "        eval_set=[(X_valid, y_valid)],\n",
    "        early_stopping_rounds=40,\n",
    "        verbose=10)\n",
    "\n",
    "preds = np.array(clf_xgb.predict(X_valid))\n",
    "valid_auc = mean_absolute_error(y_pred=preds, y_true=y_valid)\n",
    "print(f\"BEST VALID SCORE FOR REGRESSION : {valid_auc}\")\n",
    "\n",
    "preds = np.array(clf_xgb.predict(X_test))\n",
    "test_auc = mean_absolute_error(y_pred=preds, y_true=y_test)\n",
    "\n",
    "print(f\"FINAL TEST SCORE FOR REGRESSION : {test_auc}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540cf68a",
   "metadata": {},
   "source": [
    "As we noted, TabNet performed slightly better on test set than XGBoost at predicting insurance claim values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a377093",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "##### What drives claims ?\n",
    "\n",
    "According to the study conducted and presented in this notebook, it is not just one customer attribute that tells if he will submit a claim or not, but several attributes, the main ones being his salary, his history of previous claims, his location, and the purpose for which he uses his car. Thus, the proposed solution can explain what leads a driver to submit a claim in a general way, but we can also visualize in a specific way, which characteristics most influenced the model to classify that user (attention mask visualizations).\n",
    "\n",
    "##### Can we predict the value of a claim based on driver's profile?\n",
    "\n",
    "As shown, we are also able to assess the value on which the customer will request based on his profile. According to the model, the requested value is related to attributes such as gender, education level, marital status, car usage, and whether the customer has had his or her driver's license renewed or not.\n",
    "\n",
    "##### What are the advatanges of the proposed aproach ?\n",
    "\n",
    "Competitive results in comparision with similiar works and good explainability, plus as demonstrated by the authors of the TabNet architecture, this approach is able to address the actual importance of the attributes relative to the target, algorithms such as XGBoost, have been shown to be more likely to better balance the importance of the attributes.\n",
    "\n",
    "##### What are the disadvantages of the proposed aproach ?\n",
    "\n",
    "Neural nets, such as TabNet, tend to have a higher algorithmic complexity, which leads to a higher computational cost compared to training other approaches, such as decision trees. This problem can be alleviated through the use of GPU's, parallelizing some processing steps in order to decrease training time.\n"
   ]
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    "### Next Steps\n",
    "\n",
    "#### Unsupervised Learning\n",
    "\n",
    "With this it is hoped that the model can find the non-linear relationships between the data without the need for labels. Studies show that initializing the TabNet weights in an unsupervised manner decreases the model optimization time to reach an optimal result in a more efficient way, besides becoming more robust to unseen data.\n",
    "\n",
    "#### HyperParameter optimization\n",
    "\n",
    "Perform an optimization step in order to find the hyperparameters that best fit the proposed problem, thus maximizing the model results\n",
    "\n",
    "#### Go Deep\n",
    "\n",
    "Test other deep learning models for tabular data to find more insights about the data and customers.\n",
    "\n",
    "#### Hybrid Model\n",
    "\n",
    "Build a hybrid model (ensemble) that can take advantage of the main positive points of each approach and mitigate the negative points.\n"
   ]
  },
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   "source": [
    "#### REFERENCES\n",
    "\n",
    "https://github.com/xzhangfox/Prediction-of-Car-Insurance-Claims/blob/master/Final-Group-Project-Report/FinalReport.pdf\n",
    "https://www.kaggle.com/code/dllim1/end-to-end-ml-on-motor-insurance-with-xgboost/comments\n",
    "https://github.com/dreamquark-ai/tabnet\n",
    "https://arxiv.org/pdf/1908.07442.pdf"
   ]
  }
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