XGBoost Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import numpy as np\n",
    "import re \n",
    "from datetime import datetime as dt\n",
    "from datetime import timedelta \n",
    "import pickle\n",
    "from random import *\n",
    "from pysolar.solar import *\n",
    "\n",
    "#plotting\n",
    "from IPython.display import HTML, display\n",
    "import datashader as ds\n",
    "from datashader import transfer_functions as tf\n",
    "from datashader.colors import colormap_select, Greys9, Hot, viridis, inferno\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from matplotlib.ticker import NullFormatter\n",
    "%matplotlib inline\n",
    "\n",
    "#modeling\n",
    "import xgboost as xgb\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, KFold,StratifiedShuffleSplit, cross_val_score\n",
    "from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score,roc_auc_score, roc_curve, average_precision_score,precision_recall_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/df_all_processed.csv')\n",
    "ndf = pd.read_csv('./data/ndf.csv')\n",
    "ndf_tmc=pd.read_csv('./data/ndf_TMC.csv')\n",
    "ramp = pd.read_csv('./data/ramp_exit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',300)\n",
    "pd.set_option('display.max_rows',300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, ramp, how = 'left', left_on = 'TMC', right_on = 'tmc')\n",
    "ndf = pd.merge(ndf, ramp, how = 'left', left_on = 'TMC', right_on = 'tmc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vertical Profile for ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = pd.read_csv('./data/NJ3_grade.csv')\n",
    "g.index = pd.IntervalIndex.from_arrays(g['MP ST'],g['MP END'],closed='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf['grade'] = ndf['MP'].apply(lambda x: g.iloc[g.index.get_loc(x)]['Grade EB'])\n",
    "ndf['grade'] = np.where(ndf['direction']=='W', ndf['grade']*-1, ndf['grade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf = ndf.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Density-Volume-Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Density - Flow - Speed\n",
    "df['density'] = (df['VOL']+df['TRK'])/df['spd']\n",
    "ndf['density'] = (ndf['VOL']+ndf['TRK'])/ndf['spd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = 1\n",
    "ndf['target'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Feature Engineering\n",
    "\n",
    "* `speed (spd)`\n",
    "* `solar_altitude`\n",
    "* `solar_azimuth`\n",
    "* `speed difference with downstream link 1 (dn1_spd_diff)`\n",
    "* `speed difference with upstream link 1 (up1_spd_diff)`\n",
    "* `mean speed for the previous 15-min (avg_spd_15min)`\n",
    "* `mean speed for the previous 10-min (avg_spd_10min)`\n",
    "* `mean speed for the previous 5-min (avg_spd_5min)`\n",
    "* `speed standard deviation (SD) for the previous 15-min (std_15min)`\n",
    "* `speed standard deviation (SD) for the previous 10-min (std_10min)`\n",
    "* `speed standard deviation (SD) for the previous 5-min (std_5min)`\n",
    "* `coefficient of variation (CV) for the previous 15-min (cv_15min)`\n",
    "* `coefficient of variation (CV) for the previous 10-min (cv_10min)`\n",
    "* `coefficient of variation (CV) for the previous 5-min (cv_5min)`\n",
    "* `measn speed difference between upstream and downstream link for the previous 5-min (avg_spd_diff_up1dn1_5min)`\n",
    "* `High Temperature (High)`\n",
    "* `Low Temperature (Low)`\n",
    "* `Precipitation amount (Precip.)`\n",
    "* `Snow`\n",
    "* `Snow depth (Snow d.)`\n",
    "* `Link Capacity (CAPLINK)`\n",
    "* `Hourly volume (VOL)`\n",
    "* `Hourly truck volume (TRK)` \n",
    "* `Population Density (Pop_Dense)`\n",
    "* `Vertical Profile (grade)`\n",
    "* `Density (Density)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "float_feature_names = [\n",
    "    'spd',\n",
    "    'solar_altitude',\n",
    "    'solar_azimuth',\n",
    "    'dn1_spd_diff',\n",
    "    'up1_spd_diff',\n",
    "    'avg_spd_15min',\n",
    "    'avg_spd_10min',\n",
    "    'avg_spd_5min',\n",
    "    'std_15min',\n",
    "    'std_10min',\n",
    "    'std_5min',\n",
    "    'cv_15min',\n",
    "    'cv_10min',\n",
    "    'cv_5min',\n",
    "    'avg_spd_diff_up1dn1_5min',\n",
    "    'High',\n",
    "    'Low',\n",
    "    'Precip.',\n",
    "    'Snow d.',\n",
    "    'CAPLINK',\n",
    "    'VOL',\n",
    "    'TRK',\n",
    "    'Pop_Dense',\n",
    "    'density',\n",
    "    'grade'\n",
    "   \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_feature_names = ['direction_','RAMP']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "temporal_feature_names = ['hour', 'month', 'weekday']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reponse Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_feature_names = ['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.xs(float_feature_names+binary_feature_names+temporal_feature_names+res_feature_names, axis=1)\n",
    "ndf1 = ndf.xs(float_feature_names+binary_feature_names+temporal_feature_names+res_feature_names, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = pd.concat([df1, ndf1])\n",
    "df_ = df_.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df_.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spd                         0\n",
       "solar_altitude              0\n",
       "solar_azimuth               0\n",
       "dn1_spd_diff                0\n",
       "up1_spd_diff                0\n",
       "avg_spd_15min               0\n",
       "avg_spd_10min               0\n",
       "avg_spd_5min                0\n",
       "std_15min                   0\n",
       "std_10min                   0\n",
       "std_5min                    0\n",
       "cv_15min                    0\n",
       "cv_10min                    0\n",
       "cv_5min                     0\n",
       "avg_spd_diff_up1dn1_5min    0\n",
       "High                        0\n",
       "Low                         0\n",
       "Precip.                     0\n",
       "Snow d.                     0\n",
       "CAPLINK                     0\n",
       "VOL                         0\n",
       "TRK                         0\n",
       "Pop_Dense                   0\n",
       "density                     0\n",
       "grade                       0\n",
       "direction_                  0\n",
       "RAMP                        0\n",
       "hour                        0\n",
       "month                       0\n",
       "weekday                     0\n",
       "target                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "float_features = df_.xs(float_feature_names,axis=1).values\n",
    "# Use scikit-learn's StandardScaler\n",
    "scaler = StandardScaler()\n",
    "float_scaled = scaler.fit_transform(float_features)\n",
    "\n",
    "df_[float_feature_names] = float_scaled\n",
    "\n",
    "with open('scalers.pkl','wb') as fp:\n",
    "    pickle.dump(scaler,fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feature = ['direction_','RAMP', 'hour', 'month', 'weekday']\n",
    "df_ = pd.get_dummies(df_,columns=cat_feature)\n",
    "\n",
    "# Get the one-hot variable names\n",
    "#cat_feature_names = pd.get_dummies(df_[cat_feature],columns=cat_feature).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_.drop('target', axis=1).values\n",
    "y = df_['target'].values\n",
    "feature_names = df_.drop('target',axis =1).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19267, 72) (19267,)\n",
      "(10376, 72) (10376,)\n",
      "Test Accuracy: 0.8600616808018504\n",
      "Test F1: 0.49016853932584276\n",
      "Test Precision: 0.7481243301178992\n",
      "Test Recall: 0.36449086161879896\n",
      "Test AUC: 0.8346506147234292\n",
      "Fraction Predicted: 1.0\n",
      "Fraction False Alarm 4.418276762402089\n",
      "Train Accuracy: 0.8699330461410703\n",
      "Train F1: 0.5408574569439355\n",
      "Train Precision: 0.8132231404958677\n",
      "Train Recall: 0.4051605819379632\n",
      "Train AUC: 0.8737289837112299\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35, shuffle = True, random_state=10)\n",
    "\n",
    "print (X_train.shape,y_train.shape)\n",
    "print (X_test.shape,y_test.shape)\n",
    "X_train = xgb.DMatrix(X_train,label=y_train,feature_names=feature_names)\n",
    "X_test =  xgb.DMatrix(X_test,label=y_test,feature_names=feature_names)\n",
    "# params = {\n",
    "#     'n_estimators': 2000,\n",
    "#     'max_depth':6,\n",
    "#     'reg_lambda': 0.0,\n",
    "#     'reg_alpha':1.0\n",
    "# }\n",
    "\n",
    "params = {\n",
    "    'max_depth':6,\n",
    "    'min_child_weight': 5.0,\n",
    "    'reg_lambda': 1.0,\n",
    "    'reg_alpha':0.0,\n",
    "    'scale_pos_weight':1.0,\n",
    "    'eval_metric':'auc',\n",
    "    'objective':'binary:logistic',\n",
    "    'eta':0.5\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "booster = xgb.train(params,X_train)\n",
    "\n",
    "y_pred_test = np.round(booster.predict(X_test))\n",
    "print ('Test Accuracy:',accuracy_score(y_test,y_pred_test))\n",
    "print ('Test F1:',f1_score(y_test,y_pred_test))\n",
    "print ('Test Precision:',precision_score(y_test,y_pred_test))\n",
    "print ('Test Recall:',recall_score(y_test,y_pred_test))\n",
    "y_pred_test = booster.predict(X_test)\n",
    "print ('Test AUC:',roc_auc_score(y_test,y_pred_test))\n",
    "\n",
    "num_accidents_predicted_correctly = np.sum((y_test > 0) * (y_pred_test > 0))\n",
    "num_accidents = np.sum(y_test > 0)\n",
    "\n",
    "print ('Fraction Predicted:',num_accidents_predicted_correctly/float(num_accidents))\n",
    "print ('Fraction False Alarm',(np.sum(y_pred_test>0) - num_accidents_predicted_correctly)/float(num_accidents))\n",
    "y_pred_train = np.round(booster.predict(X_train))\n",
    "print ('Train Accuracy:',accuracy_score(y_train,y_pred_train))\n",
    "print ('Train F1:',f1_score(y_train,y_pred_train))\n",
    "print ('Train Precision:',precision_score(y_train,y_pred_train))\n",
    "print ('Train Recall:',recall_score(y_train,y_pred_train))\n",
    "y_pred_train = booster.predict(X_train)\n",
    "print ('Train AUC:',roc_auc_score(y_train,y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\teval-auc:0.741136\n",
      "Will train until eval-auc hasn't improved in 25 rounds.\n",
      "[1]\teval-auc:0.755665\n",
      "[2]\teval-auc:0.772049\n",
      "[3]\teval-auc:0.79866\n",
      "[4]\teval-auc:0.807203\n",
      "[5]\teval-auc:0.8165\n",
      "[6]\teval-auc:0.819263\n",
      "[7]\teval-auc:0.821891\n",
      "[8]\teval-auc:0.82287\n",
      "[9]\teval-auc:0.834651\n",
      "[10]\teval-auc:0.840654\n",
      "[11]\teval-auc:0.840613\n",
      "[12]\teval-auc:0.841672\n",
      "[13]\teval-auc:0.840891\n",
      "[14]\teval-auc:0.843679\n",
      "[15]\teval-auc:0.847908\n",
      "[16]\teval-auc:0.848066\n",
      "[17]\teval-auc:0.848007\n",
      "[18]\teval-auc:0.849227\n",
      "[19]\teval-auc:0.847621\n",
      "[20]\teval-auc:0.846263\n",
      "[21]\teval-auc:0.846737\n",
      "[22]\teval-auc:0.84682\n",
      "[23]\teval-auc:0.848095\n",
      "[24]\teval-auc:0.847476\n",
      "[25]\teval-auc:0.847345\n",
      "[26]\teval-auc:0.847062\n",
      "[27]\teval-auc:0.845402\n",
      "[28]\teval-auc:0.845371\n",
      "[29]\teval-auc:0.84561\n",
      "[30]\teval-auc:0.845374\n",
      "[31]\teval-auc:0.84585\n",
      "[32]\teval-auc:0.846356\n",
      "[33]\teval-auc:0.846262\n",
      "[34]\teval-auc:0.845587\n",
      "[35]\teval-auc:0.845349\n",
      "[36]\teval-auc:0.845318\n",
      "[37]\teval-auc:0.844817\n",
      "[38]\teval-auc:0.844772\n",
      "[39]\teval-auc:0.844896\n",
      "[40]\teval-auc:0.84405\n",
      "[41]\teval-auc:0.843952\n",
      "[42]\teval-auc:0.843189\n",
      "[43]\teval-auc:0.842304\n",
      "Stopping. Best iteration:\n",
      "[18]\teval-auc:0.849227\n",
      "\n"
     ]
    }
   ],
   "source": [
    "booster = xgb.train(params,X_train,\n",
    "    evals = [(X_test, 'eval')],\n",
    "    num_boost_round=3000,\n",
    "    early_stopping_rounds=25\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Features')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "xgb.plot_importance(booster,ax=plt.gca(),importance_type='weight', max_num_features=20)\n",
    "plt.xlabel('F Score', fontsize=12)\n",
    "plt.ylabel('Features', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Features')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "xgb.plot_importance(booster,ax=plt.gca(),importance_type='weight')\n",
    "plt.xlabel('F Score', fontsize=12)\n",
    "plt.ylabel('Features', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target\n",
      "0    24085\n",
      "1     5558\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_.groupby('target').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imbalanced Data - SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled dataset shape Counter({0: 24085, 1: 10000})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE \n",
    "\n",
    "x1 = df_.drop('target', axis=1)\n",
    "y1 = df_['target']\n",
    "\n",
    "'''Increase the crash samples from 5558 to 10000'''\n",
    "\n",
    "sm = SMOTE(random_state=42,sampling_strategy={1:10000})\n",
    "X_res, y_res = sm.fit_sample(x1, y1)\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain shape\n",
      "(23859, 72)\n",
      "xtest shape\n",
      "(10226, 72)\n"
     ]
    }
   ],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(X_res, y_res, test_size=0.30, random_state=0)\n",
    "print('xtrain shape')\n",
    "print(xtrain.shape)\n",
    "print('xtest shape')\n",
    "print(xtest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bynode': 1, 'colsample_bytree': 1, 'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 3, 'min_child_weight': 1, 'missing': None, 'n_estimators': 100, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'subsample': 1, 'verbosity': 1}\n"
     ]
    }
   ],
   "source": [
    "# Extreme Gradient Boost Algorithm on reduced dimensions data\n",
    "\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "\n",
    "# Create the XGB classifier, xgb_model.\n",
    "xgb_model = XGBClassifier()\n",
    "\n",
    "# List the default parameters.\n",
    "print(xgb_model.get_xgb_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.246448\tvalidation_1-error:0.250636\n",
      "[1]\tvalidation_0-error:0.238568\tvalidation_1-error:0.239879\n",
      "[2]\tvalidation_0-error:0.238401\tvalidation_1-error:0.24027\n",
      "[3]\tvalidation_0-error:0.237856\tvalidation_1-error:0.23939\n",
      "[4]\tvalidation_0-error:0.236808\tvalidation_1-error:0.238412\n",
      "[5]\tvalidation_0-error:0.235802\tvalidation_1-error:0.238216\n",
      "[6]\tvalidation_0-error:0.234293\tvalidation_1-error:0.236652\n",
      "[7]\tvalidation_0-error:0.233371\tvalidation_1-error:0.235869\n",
      "[8]\tvalidation_0-error:0.23379\tvalidation_1-error:0.236163\n",
      "[9]\tvalidation_0-error:0.233497\tvalidation_1-error:0.236456\n",
      "[10]\tvalidation_0-error:0.232742\tvalidation_1-error:0.235478\n",
      "[11]\tvalidation_0-error:0.232742\tvalidation_1-error:0.235283\n",
      "[12]\tvalidation_0-error:0.23182\tvalidation_1-error:0.235283\n",
      "[13]\tvalidation_0-error:0.231108\tvalidation_1-error:0.235185\n",
      "[14]\tvalidation_0-error:0.230689\tvalidation_1-error:0.234794\n",
      "[15]\tvalidation_0-error:0.230018\tvalidation_1-error:0.233425\n",
      "[16]\tvalidation_0-error:0.228509\tvalidation_1-error:0.232544\n",
      "[17]\tvalidation_0-error:0.22545\tvalidation_1-error:0.230491\n",
      "[18]\tvalidation_0-error:0.224318\tvalidation_1-error:0.229122\n",
      "[19]\tvalidation_0-error:0.223522\tvalidation_1-error:0.228144\n",
      "[20]\tvalidation_0-error:0.222516\tvalidation_1-error:0.226873\n",
      "[21]\tvalidation_0-error:0.221594\tvalidation_1-error:0.227264\n",
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      "[25]\tvalidation_0-error:0.214343\tvalidation_1-error:0.221396\n",
      "[26]\tvalidation_0-error:0.213672\tvalidation_1-error:0.219832\n",
      "[27]\tvalidation_0-error:0.213001\tvalidation_1-error:0.219245\n",
      "[28]\tvalidation_0-error:0.212079\tvalidation_1-error:0.218756\n",
      "[29]\tvalidation_0-error:0.210696\tvalidation_1-error:0.217485\n",
      "[30]\tvalidation_0-error:0.208391\tvalidation_1-error:0.215822\n",
      "[31]\tvalidation_0-error:0.207343\tvalidation_1-error:0.215333\n",
      "[32]\tvalidation_0-error:0.207301\tvalidation_1-error:0.215333\n",
      "[33]\tvalidation_0-error:0.206421\tvalidation_1-error:0.213671\n",
      "[34]\tvalidation_0-error:0.206128\tvalidation_1-error:0.213573\n",
      "[35]\tvalidation_0-error:0.205499\tvalidation_1-error:0.211715\n",
      "[36]\tvalidation_0-error:0.204577\tvalidation_1-error:0.211715\n",
      "[37]\tvalidation_0-error:0.203236\tvalidation_1-error:0.209662\n",
      "[38]\tvalidation_0-error:0.202984\tvalidation_1-error:0.209857\n",
      "[39]\tvalidation_0-error:0.202062\tvalidation_1-error:0.20927\n",
      "[40]\tvalidation_0-error:0.201936\tvalidation_1-error:0.207608\n",
      "[41]\tvalidation_0-error:0.201308\tvalidation_1-error:0.206826\n",
      "[42]\tvalidation_0-error:0.201056\tvalidation_1-error:0.207217\n",
      "[43]\tvalidation_0-error:0.200386\tvalidation_1-error:0.206141\n",
      "[44]\tvalidation_0-error:0.199715\tvalidation_1-error:0.205652\n",
      "[45]\tvalidation_0-error:0.198835\tvalidation_1-error:0.203208\n",
      "[46]\tvalidation_0-error:0.198332\tvalidation_1-error:0.203403\n",
      "[47]\tvalidation_0-error:0.197368\tvalidation_1-error:0.202523\n",
      "[48]\tvalidation_0-error:0.196278\tvalidation_1-error:0.202034\n",
      "[49]\tvalidation_0-error:0.19435\tvalidation_1-error:0.199785\n",
      "[50]\tvalidation_0-error:0.194266\tvalidation_1-error:0.199687\n",
      "[51]\tvalidation_0-error:0.193428\tvalidation_1-error:0.199003\n",
      "[52]\tvalidation_0-error:0.192171\tvalidation_1-error:0.198416\n",
      "[53]\tvalidation_0-error:0.191626\tvalidation_1-error:0.196656\n",
      "[54]\tvalidation_0-error:0.191458\tvalidation_1-error:0.196851\n",
      "[55]\tvalidation_0-error:0.190704\tvalidation_1-error:0.196264\n",
      "[56]\tvalidation_0-error:0.190578\tvalidation_1-error:0.196656\n",
      "[57]\tvalidation_0-error:0.190452\tvalidation_1-error:0.196656\n",
      "[58]\tvalidation_0-error:0.19041\tvalidation_1-error:0.196949\n",
      "[59]\tvalidation_0-error:0.189404\tvalidation_1-error:0.194993\n",
      "[60]\tvalidation_0-error:0.189363\tvalidation_1-error:0.194798\n",
      "[61]\tvalidation_0-error:0.188734\tvalidation_1-error:0.194211\n",
      "[62]\tvalidation_0-error:0.187979\tvalidation_1-error:0.194309\n",
      "[63]\tvalidation_0-error:0.187183\tvalidation_1-error:0.193429\n",
      "[64]\tvalidation_0-error:0.186429\tvalidation_1-error:0.192842\n",
      "[65]\tvalidation_0-error:0.185381\tvalidation_1-error:0.192255\n",
      "[66]\tvalidation_0-error:0.184794\tvalidation_1-error:0.191962\n",
      "[67]\tvalidation_0-error:0.184668\tvalidation_1-error:0.191473\n",
      "[68]\tvalidation_0-error:0.184333\tvalidation_1-error:0.190886\n",
      "[69]\tvalidation_0-error:0.183998\tvalidation_1-error:0.190984\n",
      "[70]\tvalidation_0-error:0.183579\tvalidation_1-error:0.190788\n",
      "[71]\tvalidation_0-error:0.18274\tvalidation_1-error:0.190201\n",
      "[72]\tvalidation_0-error:0.182573\tvalidation_1-error:0.189712\n",
      "[73]\tvalidation_0-error:0.182656\tvalidation_1-error:0.18981\n",
      "[74]\tvalidation_0-error:0.181399\tvalidation_1-error:0.189126\n",
      "[75]\tvalidation_0-error:0.181189\tvalidation_1-error:0.188735\n",
      "[76]\tvalidation_0-error:0.180854\tvalidation_1-error:0.188246\n",
      "[77]\tvalidation_0-error:0.180309\tvalidation_1-error:0.187659\n",
      "[78]\tvalidation_0-error:0.180393\tvalidation_1-error:0.188343\n",
      "[79]\tvalidation_0-error:0.179681\tvalidation_1-error:0.186974\n",
      "[80]\tvalidation_0-error:0.179471\tvalidation_1-error:0.187072\n",
      "[81]\tvalidation_0-error:0.179178\tvalidation_1-error:0.186974\n",
      "[82]\tvalidation_0-error:0.178507\tvalidation_1-error:0.186485\n",
      "[83]\tvalidation_0-error:0.178549\tvalidation_1-error:0.18629\n",
      "[84]\tvalidation_0-error:0.177669\tvalidation_1-error:0.185801\n",
      "[85]\tvalidation_0-error:0.177543\tvalidation_1-error:0.185508\n",
      "[86]\tvalidation_0-error:0.176956\tvalidation_1-error:0.184627\n",
      "[87]\tvalidation_0-error:0.177082\tvalidation_1-error:0.184921\n",
      "[88]\tvalidation_0-error:0.176118\tvalidation_1-error:0.183747\n",
      "[89]\tvalidation_0-error:0.17507\tvalidation_1-error:0.183161\n",
      "[90]\tvalidation_0-error:0.174442\tvalidation_1-error:0.183063\n",
      "[91]\tvalidation_0-error:0.174274\tvalidation_1-error:0.182867\n",
      "[92]\tvalidation_0-error:0.173603\tvalidation_1-error:0.182378\n",
      "[93]\tvalidation_0-error:0.173603\tvalidation_1-error:0.181987\n",
      "[94]\tvalidation_0-error:0.1731\tvalidation_1-error:0.181987\n",
      "[95]\tvalidation_0-error:0.172514\tvalidation_1-error:0.181205\n",
      "[96]\tvalidation_0-error:0.171256\tvalidation_1-error:0.180129\n",
      "[97]\tvalidation_0-error:0.169035\tvalidation_1-error:0.177782\n",
      "[98]\tvalidation_0-error:0.168783\tvalidation_1-error:0.176022\n",
      "[99]\tvalidation_0-error:0.167819\tvalidation_1-error:0.175142\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train and evaluate.\n",
    "xgb_model.fit(xtrain, ytrain, eval_metric=['error'], eval_set=[((xtrain, ytrain)),(xtest, ytest)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[6856  332]\n",
      " [1459 1579]]\n",
      "classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.95      0.88      7188\n",
      "           1       0.83      0.52      0.64      3038\n",
      "\n",
      "    accuracy                           0.82     10226\n",
      "   macro avg       0.83      0.74      0.76     10226\n",
      "weighted avg       0.83      0.82      0.81     10226\n",
      "\n",
      "-----------------------------------------------------------------------------------------\n",
      "Accuracy is :\n",
      "0.8248582045765696\n",
      "Area under the curve : 0.736781\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "y_pred = xgb_model.predict(xtest)  #predicts\n",
    "print('confusion matrix')\n",
    "print(metrics.confusion_matrix(ytest, y_pred))\n",
    "print('classification report')\n",
    "print(metrics.classification_report(ytest, y_pred))\n",
    "print(\"-----------------------------------------------------------------------------------------\")\n",
    "print(\"Accuracy is :\")\n",
    "print(metrics.accuracy_score(ytest, y_pred))\n",
    "print('Area under the curve : %f' % (metrics.roc_auc_score(ytest, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.8248582045765696\n",
      "Test F1: 0.6381087088300667\n",
      "Test Precision: 0.826268969126112\n",
      "Test Recall: 0.5197498354180382\n"
     ]
    }
   ],
   "source": [
    "print ('Test Accuracy:',accuracy_score(ytest,y_pred))\n",
    "print ('Test F1:',f1_score(ytest,y_pred))\n",
    "print ('Test Precision:',precision_score(ytest,y_pred))\n",
    "print ('Test Recall:',recall_score(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'max_depth':6,\n",
    "    'min_child_weight': 5.0,\n",
    "    'reg_lambda': 1.0,\n",
    "    'reg_alpha':0.0,\n",
    "    'scale_pos_weight':1.0,\n",
    "    'eval_metric':'auc',\n",
    "    'objective':'binary:logistic',\n",
    "    'eta':0.5\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model1 = XGBClassifier().set_params(max_depth = 6, min_child_weight = 5, reg_lambda = 1, reg_alpha =0,\n",
    "                                       scale_pos_weight = 1, objective = 'binary:logistic', eta = 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.226288\tvalidation_1-error:0.23098\n",
      "[1]\tvalidation_0-error:0.220504\tvalidation_1-error:0.226286\n",
      "[2]\tvalidation_0-error:0.214804\tvalidation_1-error:0.220027\n",
      "[3]\tvalidation_0-error:0.214133\tvalidation_1-error:0.219343\n",
      "[4]\tvalidation_0-error:0.213211\tvalidation_1-error:0.220321\n",
      "[5]\tvalidation_0-error:0.207175\tvalidation_1-error:0.214062\n",
      "[6]\tvalidation_0-error:0.20508\tvalidation_1-error:0.21152\n",
      "[7]\tvalidation_0-error:0.200805\tvalidation_1-error:0.207999\n",
      "[8]\tvalidation_0-error:0.200847\tvalidation_1-error:0.208097\n",
      "[9]\tvalidation_0-error:0.199715\tvalidation_1-error:0.208195\n",
      "[10]\tvalidation_0-error:0.198248\tvalidation_1-error:0.207021\n",
      "[11]\tvalidation_0-error:0.194308\tvalidation_1-error:0.203696\n",
      "[12]\tvalidation_0-error:0.192338\tvalidation_1-error:0.201252\n",
      "[13]\tvalidation_0-error:0.190662\tvalidation_1-error:0.198807\n",
      "[14]\tvalidation_0-error:0.188357\tvalidation_1-error:0.19646\n",
      "[15]\tvalidation_0-error:0.186345\tvalidation_1-error:0.195482\n",
      "[16]\tvalidation_0-error:0.184501\tvalidation_1-error:0.193429\n",
      "[17]\tvalidation_0-error:0.181022\tvalidation_1-error:0.190593\n",
      "[18]\tvalidation_0-error:0.180519\tvalidation_1-error:0.189517\n",
      "[19]\tvalidation_0-error:0.17704\tvalidation_1-error:0.185996\n",
      "[20]\tvalidation_0-error:0.174903\tvalidation_1-error:0.184041\n",
      "[21]\tvalidation_0-error:0.17398\tvalidation_1-error:0.184236\n",
      "[22]\tvalidation_0-error:0.172933\tvalidation_1-error:0.183552\n",
      "[23]\tvalidation_0-error:0.171759\tvalidation_1-error:0.183454\n",
      "[24]\tvalidation_0-error:0.170166\tvalidation_1-error:0.181694\n",
      "[25]\tvalidation_0-error:0.169077\tvalidation_1-error:0.181009\n",
      "[26]\tvalidation_0-error:0.166017\tvalidation_1-error:0.177684\n",
      "[27]\tvalidation_0-error:0.163335\tvalidation_1-error:0.175337\n",
      "[28]\tvalidation_0-error:0.163083\tvalidation_1-error:0.173773\n",
      "[29]\tvalidation_0-error:0.160694\tvalidation_1-error:0.174164\n",
      "[30]\tvalidation_0-error:0.161071\tvalidation_1-error:0.173479\n",
      "[31]\tvalidation_0-error:0.158598\tvalidation_1-error:0.17211\n",
      "[32]\tvalidation_0-error:0.157173\tvalidation_1-error:0.169763\n",
      "[33]\tvalidation_0-error:0.156838\tvalidation_1-error:0.169372\n",
      "[34]\tvalidation_0-error:0.154491\tvalidation_1-error:0.166341\n",
      "[35]\tvalidation_0-error:0.153108\tvalidation_1-error:0.165754\n",
      "[36]\tvalidation_0-error:0.151348\tvalidation_1-error:0.165167\n",
      "[37]\tvalidation_0-error:0.151348\tvalidation_1-error:0.163994\n",
      "[38]\tvalidation_0-error:0.150342\tvalidation_1-error:0.162527\n",
      "[39]\tvalidation_0-error:0.149503\tvalidation_1-error:0.163016\n",
      "[40]\tvalidation_0-error:0.148456\tvalidation_1-error:0.161256\n",
      "[41]\tvalidation_0-error:0.14833\tvalidation_1-error:0.161451\n",
      "[42]\tvalidation_0-error:0.147156\tvalidation_1-error:0.159887\n",
      "[43]\tvalidation_0-error:0.146611\tvalidation_1-error:0.159789\n",
      "[44]\tvalidation_0-error:0.145899\tvalidation_1-error:0.159104\n",
      "[45]\tvalidation_0-error:0.143552\tvalidation_1-error:0.157442\n",
      "[46]\tvalidation_0-error:0.142923\tvalidation_1-error:0.156757\n",
      "[47]\tvalidation_0-error:0.14133\tvalidation_1-error:0.156073\n",
      "[48]\tvalidation_0-error:0.140911\tvalidation_1-error:0.154801\n",
      "[49]\tvalidation_0-error:0.139738\tvalidation_1-error:0.153921\n",
      "[50]\tvalidation_0-error:0.138648\tvalidation_1-error:0.153335\n",
      "[51]\tvalidation_0-error:0.138187\tvalidation_1-error:0.15353\n",
      "[52]\tvalidation_0-error:0.137474\tvalidation_1-error:0.153139\n",
      "[53]\tvalidation_0-error:0.137055\tvalidation_1-error:0.152455\n",
      "[54]\tvalidation_0-error:0.136468\tvalidation_1-error:0.15177\n",
      "[55]\tvalidation_0-error:0.133367\tvalidation_1-error:0.150205\n",
      "[56]\tvalidation_0-error:0.132948\tvalidation_1-error:0.148934\n",
      "[57]\tvalidation_0-error:0.132235\tvalidation_1-error:0.14825\n",
      "[58]\tvalidation_0-error:0.131229\tvalidation_1-error:0.147663\n",
      "[59]\tvalidation_0-error:0.130223\tvalidation_1-error:0.147858\n",
      "[60]\tvalidation_0-error:0.129846\tvalidation_1-error:0.146783\n",
      "[61]\tvalidation_0-error:0.129176\tvalidation_1-error:0.146685\n",
      "[62]\tvalidation_0-error:0.128505\tvalidation_1-error:0.145903\n",
      "[63]\tvalidation_0-error:0.128212\tvalidation_1-error:0.146098\n",
      "[64]\tvalidation_0-error:0.128212\tvalidation_1-error:0.146\n",
      "[65]\tvalidation_0-error:0.127751\tvalidation_1-error:0.145316\n",
      "[66]\tvalidation_0-error:0.126619\tvalidation_1-error:0.143653\n",
      "[67]\tvalidation_0-error:0.126535\tvalidation_1-error:0.143458\n",
      "[68]\tvalidation_0-error:0.125655\tvalidation_1-error:0.142676\n",
      "[69]\tvalidation_0-error:0.124523\tvalidation_1-error:0.141404\n",
      "[70]\tvalidation_0-error:0.124272\tvalidation_1-error:0.141209\n",
      "[71]\tvalidation_0-error:0.122889\tvalidation_1-error:0.140329\n",
      "[72]\tvalidation_0-error:0.122595\tvalidation_1-error:0.13984\n",
      "[73]\tvalidation_0-error:0.122428\tvalidation_1-error:0.139742\n",
      "[74]\tvalidation_0-error:0.121044\tvalidation_1-error:0.139448\n",
      "[75]\tvalidation_0-error:0.120961\tvalidation_1-error:0.140035\n",
      "[76]\tvalidation_0-error:0.120583\tvalidation_1-error:0.139253\n",
      "[77]\tvalidation_0-error:0.12029\tvalidation_1-error:0.138764\n",
      "[78]\tvalidation_0-error:0.119829\tvalidation_1-error:0.13896\n",
      "[79]\tvalidation_0-error:0.119075\tvalidation_1-error:0.138275\n",
      "[80]\tvalidation_0-error:0.119494\tvalidation_1-error:0.138373\n",
      "[81]\tvalidation_0-error:0.118697\tvalidation_1-error:0.136417\n",
      "[82]\tvalidation_0-error:0.117817\tvalidation_1-error:0.136124\n",
      "[83]\tvalidation_0-error:0.117608\tvalidation_1-error:0.136221\n",
      "[84]\tvalidation_0-error:0.117188\tvalidation_1-error:0.136319\n",
      "[85]\tvalidation_0-error:0.117147\tvalidation_1-error:0.136221\n",
      "[86]\tvalidation_0-error:0.116853\tvalidation_1-error:0.135537\n",
      "[87]\tvalidation_0-error:0.115805\tvalidation_1-error:0.13583\n",
      "[88]\tvalidation_0-error:0.115722\tvalidation_1-error:0.136026\n",
      "[89]\tvalidation_0-error:0.114967\tvalidation_1-error:0.135341\n",
      "[90]\tvalidation_0-error:0.114674\tvalidation_1-error:0.135048\n",
      "[91]\tvalidation_0-error:0.114296\tvalidation_1-error:0.134755\n",
      "[92]\tvalidation_0-error:0.113626\tvalidation_1-error:0.134657\n",
      "[93]\tvalidation_0-error:0.11283\tvalidation_1-error:0.133874\n",
      "[94]\tvalidation_0-error:0.112285\tvalidation_1-error:0.133679\n",
      "[95]\tvalidation_0-error:0.11174\tvalidation_1-error:0.13319\n",
      "[96]\tvalidation_0-error:0.11153\tvalidation_1-error:0.132212\n",
      "[97]\tvalidation_0-error:0.110985\tvalidation_1-error:0.132016\n",
      "[98]\tvalidation_0-error:0.110524\tvalidation_1-error:0.131723\n",
      "[99]\tvalidation_0-error:0.110315\tvalidation_1-error:0.132016\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(eta=0.4, max_depth=6, min_child_weight=5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model1.fit(xtrain, ytrain, eval_metric=['error'], eval_set=[((xtrain, ytrain)),(xtest, ytest)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[6948  240]\n",
      " [1110 1928]]\n",
      "classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91      7188\n",
      "           1       0.89      0.63      0.74      3038\n",
      "\n",
      "    accuracy                           0.87     10226\n",
      "   macro avg       0.88      0.80      0.83     10226\n",
      "weighted avg       0.87      0.87      0.86     10226\n",
      "\n",
      "-----------------------------------------------------------------------------------------\n",
      "Accuracy is :\n",
      "0.8679835712888715\n",
      "Area under the curve : 0.800620\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "y_pred1 = xgb_model1.predict(xtest)  #predicts\n",
    "print('confusion matrix')\n",
    "print(metrics.confusion_matrix(ytest, y_pred1))\n",
    "print('classification report')\n",
    "print(metrics.classification_report(ytest, y_pred1))\n",
    "print(\"-----------------------------------------------------------------------------------------\")\n",
    "print(\"Accuracy is :\")\n",
    "print(metrics.accuracy_score(ytest, y_pred1))\n",
    "print('Area under the curve : %f' % (metrics.roc_auc_score(ytest, y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[6948  240]\n",
      " [1110 1928]]\n",
      "classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.97      0.91      7188\n",
      "           1       0.89      0.63      0.74      3038\n",
      "\n",
      "    accuracy                           0.87     10226\n",
      "   macro avg       0.88      0.80      0.83     10226\n",
      "weighted avg       0.87      0.87      0.86     10226\n",
      "\n",
      "-----------------------------------------------------------------------------------------\n",
      "Accuracy is :\n",
      "0.8679835712888715\n",
      "Area under the curve : 0.800620\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "y_pred1 = xgb_model1.predict(xtest)  #predicts\n",
    "print('confusion matrix')\n",
    "print(metrics.confusion_matrix(ytest, y_pred1))\n",
    "print('classification report')\n",
    "print(metrics.classification_report(ytest, y_pred1))\n",
    "print(\"-----------------------------------------------------------------------------------------\")\n",
    "print(\"Accuracy is :\")\n",
    "print(metrics.accuracy_score(ytest, y_pred1))\n",
    "print('Area under the curve : %f' % (metrics.roc_auc_score(ytest, y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.8679835712888715\n",
      "Test F1: 0.7406838263542067\n",
      "Test Precision: 0.8892988929889298\n",
      "Test Recall: 0.6346280447662936\n"
     ]
    }
   ],
   "source": [
    "print ('Test Accuracy:',accuracy_score(ytest,y_pred1))\n",
    "print ('Test F1:',f1_score(ytest,y_pred1))\n",
    "print ('Test Precision:',precision_score(ytest,y_pred1))\n",
    "print ('Test Recall:',recall_score(ytest,y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE Senario 2\n",
    "12500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled dataset shape Counter({0: 24085, 1: 12500})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE \n",
    "\n",
    "x2 = df_.drop('target', axis=1)\n",
    "y2 = df_['target']\n",
    "\n",
    "'''Increase the crash samples from 5558 to 10000'''\n",
    "\n",
    "sm = SMOTE(random_state=42,sampling_strategy={1:12500})\n",
    "X_res1, y_res1 = sm.fit_sample(x2, y2)\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain shape\n",
      "(25609, 72)\n",
      "xtest shape\n",
      "(10976, 72)\n"
     ]
    }
   ],
   "source": [
    "xtrain1, xtest1, ytrain1, ytest1 = train_test_split(X_res1, y_res1, test_size=0.30, random_state=10)\n",
    "print('xtrain shape')\n",
    "print(xtrain1.shape)\n",
    "print('xtest shape')\n",
    "print(xtest1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.242337\tvalidation_1-error:0.249909\n",
      "[1]\tvalidation_0-error:0.23031\tvalidation_1-error:0.237427\n",
      "[2]\tvalidation_0-error:0.229607\tvalidation_1-error:0.238156\n",
      "[3]\tvalidation_0-error:0.224882\tvalidation_1-error:0.233236\n",
      "[4]\tvalidation_0-error:0.214963\tvalidation_1-error:0.225674\n",
      "[5]\tvalidation_0-error:0.20813\tvalidation_1-error:0.218203\n",
      "[6]\tvalidation_0-error:0.202976\tvalidation_1-error:0.212372\n",
      "[7]\tvalidation_0-error:0.201179\tvalidation_1-error:0.211097\n",
      "[8]\tvalidation_0-error:0.19747\tvalidation_1-error:0.208637\n",
      "[9]\tvalidation_0-error:0.193994\tvalidation_1-error:0.204537\n",
      "[10]\tvalidation_0-error:0.193174\tvalidation_1-error:0.202442\n",
      "[11]\tvalidation_0-error:0.19048\tvalidation_1-error:0.199617\n",
      "[12]\tvalidation_0-error:0.189777\tvalidation_1-error:0.199435\n",
      "[13]\tvalidation_0-error:0.18841\tvalidation_1-error:0.19816\n",
      "[14]\tvalidation_0-error:0.187786\tvalidation_1-error:0.197157\n",
      "[15]\tvalidation_0-error:0.183256\tvalidation_1-error:0.193604\n",
      "[16]\tvalidation_0-error:0.181069\tvalidation_1-error:0.19242\n",
      "[17]\tvalidation_0-error:0.178726\tvalidation_1-error:0.188776\n",
      "[18]\tvalidation_0-error:0.177594\tvalidation_1-error:0.187773\n",
      "[19]\tvalidation_0-error:0.174626\tvalidation_1-error:0.183582\n",
      "[20]\tvalidation_0-error:0.171776\tvalidation_1-error:0.181942\n",
      "[21]\tvalidation_0-error:0.169472\tvalidation_1-error:0.180758\n",
      "[22]\tvalidation_0-error:0.168027\tvalidation_1-error:0.1793\n",
      "[23]\tvalidation_0-error:0.165567\tvalidation_1-error:0.17684\n",
      "[24]\tvalidation_0-error:0.163732\tvalidation_1-error:0.175838\n",
      "[25]\tvalidation_0-error:0.16092\tvalidation_1-error:0.174107\n",
      "[26]\tvalidation_0-error:0.157952\tvalidation_1-error:0.170645\n",
      "[27]\tvalidation_0-error:0.155219\tvalidation_1-error:0.169643\n",
      "[28]\tvalidation_0-error:0.154282\tvalidation_1-error:0.169734\n",
      "[29]\tvalidation_0-error:0.153149\tvalidation_1-error:0.16691\n",
      "[30]\tvalidation_0-error:0.151665\tvalidation_1-error:0.165725\n",
      "[31]\tvalidation_0-error:0.150924\tvalidation_1-error:0.165179\n",
      "[32]\tvalidation_0-error:0.15026\tvalidation_1-error:0.164814\n",
      "[33]\tvalidation_0-error:0.148542\tvalidation_1-error:0.162536\n",
      "[34]\tvalidation_0-error:0.147019\tvalidation_1-error:0.16117\n",
      "[35]\tvalidation_0-error:0.145457\tvalidation_1-error:0.160441\n",
      "[36]\tvalidation_0-error:0.144285\tvalidation_1-error:0.158892\n",
      "[37]\tvalidation_0-error:0.144012\tvalidation_1-error:0.158072\n",
      "[38]\tvalidation_0-error:0.143778\tvalidation_1-error:0.15789\n",
      "[39]\tvalidation_0-error:0.141357\tvalidation_1-error:0.155886\n",
      "[40]\tvalidation_0-error:0.14038\tvalidation_1-error:0.155794\n",
      "[41]\tvalidation_0-error:0.139209\tvalidation_1-error:0.154974\n",
      "[42]\tvalidation_0-error:0.138076\tvalidation_1-error:0.153608\n",
      "[43]\tvalidation_0-error:0.137764\tvalidation_1-error:0.152332\n",
      "[44]\tvalidation_0-error:0.136671\tvalidation_1-error:0.152059\n",
      "[45]\tvalidation_0-error:0.13628\tvalidation_1-error:0.150966\n",
      "[46]\tvalidation_0-error:0.135499\tvalidation_1-error:0.151239\n",
      "[47]\tvalidation_0-error:0.134328\tvalidation_1-error:0.15051\n",
      "[48]\tvalidation_0-error:0.13218\tvalidation_1-error:0.147777\n",
      "[49]\tvalidation_0-error:0.132414\tvalidation_1-error:0.146866\n",
      "[50]\tvalidation_0-error:0.130696\tvalidation_1-error:0.146228\n",
      "[51]\tvalidation_0-error:0.130189\tvalidation_1-error:0.145226\n",
      "[52]\tvalidation_0-error:0.129993\tvalidation_1-error:0.14395\n",
      "[53]\tvalidation_0-error:0.12929\tvalidation_1-error:0.144406\n",
      "[54]\tvalidation_0-error:0.128314\tvalidation_1-error:0.144315\n",
      "[55]\tvalidation_0-error:0.127416\tvalidation_1-error:0.143495\n",
      "[56]\tvalidation_0-error:0.127065\tvalidation_1-error:0.143039\n",
      "[57]\tvalidation_0-error:0.126674\tvalidation_1-error:0.142675\n",
      "[58]\tvalidation_0-error:0.126245\tvalidation_1-error:0.141582\n",
      "[59]\tvalidation_0-error:0.125854\tvalidation_1-error:0.140579\n",
      "[60]\tvalidation_0-error:0.125268\tvalidation_1-error:0.140853\n",
      "[61]\tvalidation_0-error:0.123902\tvalidation_1-error:0.139668\n",
      "[62]\tvalidation_0-error:0.123316\tvalidation_1-error:0.139213\n",
      "[63]\tvalidation_0-error:0.122769\tvalidation_1-error:0.138666\n",
      "[64]\tvalidation_0-error:0.121871\tvalidation_1-error:0.138939\n",
      "[65]\tvalidation_0-error:0.121637\tvalidation_1-error:0.138393\n",
      "[66]\tvalidation_0-error:0.121715\tvalidation_1-error:0.138666\n",
      "[67]\tvalidation_0-error:0.121676\tvalidation_1-error:0.138028\n",
      "[68]\tvalidation_0-error:0.12109\tvalidation_1-error:0.137573\n",
      "[69]\tvalidation_0-error:0.120739\tvalidation_1-error:0.137391\n",
      "[70]\tvalidation_0-error:0.120544\tvalidation_1-error:0.136753\n",
      "[71]\tvalidation_0-error:0.120778\tvalidation_1-error:0.13648\n",
      "[72]\tvalidation_0-error:0.120114\tvalidation_1-error:0.136844\n",
      "[73]\tvalidation_0-error:0.120075\tvalidation_1-error:0.137208\n",
      "[74]\tvalidation_0-error:0.119919\tvalidation_1-error:0.136844\n",
      "[75]\tvalidation_0-error:0.119567\tvalidation_1-error:0.137026\n",
      "[76]\tvalidation_0-error:0.119021\tvalidation_1-error:0.136571\n",
      "[77]\tvalidation_0-error:0.11863\tvalidation_1-error:0.136024\n",
      "[78]\tvalidation_0-error:0.118083\tvalidation_1-error:0.13566\n",
      "[79]\tvalidation_0-error:0.11742\tvalidation_1-error:0.135569\n",
      "[80]\tvalidation_0-error:0.117146\tvalidation_1-error:0.13484\n",
      "[81]\tvalidation_0-error:0.1166\tvalidation_1-error:0.134931\n",
      "[82]\tvalidation_0-error:0.116131\tvalidation_1-error:0.134657\n",
      "[83]\tvalidation_0-error:0.115662\tvalidation_1-error:0.134657\n",
      "[84]\tvalidation_0-error:0.114881\tvalidation_1-error:0.134293\n",
      "[85]\tvalidation_0-error:0.11414\tvalidation_1-error:0.133564\n",
      "[86]\tvalidation_0-error:0.1125\tvalidation_1-error:0.132289\n",
      "[87]\tvalidation_0-error:0.112695\tvalidation_1-error:0.13156\n",
      "[88]\tvalidation_0-error:0.111875\tvalidation_1-error:0.131195\n",
      "[89]\tvalidation_0-error:0.111601\tvalidation_1-error:0.131104\n",
      "[90]\tvalidation_0-error:0.111133\tvalidation_1-error:0.129555\n",
      "[91]\tvalidation_0-error:0.110547\tvalidation_1-error:0.129829\n",
      "[92]\tvalidation_0-error:0.109805\tvalidation_1-error:0.129829\n",
      "[93]\tvalidation_0-error:0.10961\tvalidation_1-error:0.1291\n",
      "[94]\tvalidation_0-error:0.108946\tvalidation_1-error:0.128644\n",
      "[95]\tvalidation_0-error:0.108712\tvalidation_1-error:0.128553\n",
      "[96]\tvalidation_0-error:0.108282\tvalidation_1-error:0.128098\n",
      "[97]\tvalidation_0-error:0.10754\tvalidation_1-error:0.127551\n",
      "[98]\tvalidation_0-error:0.107579\tvalidation_1-error:0.12746\n",
      "[99]\tvalidation_0-error:0.106681\tvalidation_1-error:0.126184\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(eta=0.4, max_depth=6, min_child_weight=5)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model1.fit(xtrain1, ytrain1, eval_metric=['error'], eval_set=[((xtrain1, ytrain1)),(xtest1, ytest1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[6877  306]\n",
      " [1079 2714]]\n",
      "classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.96      0.91      7183\n",
      "           1       0.90      0.72      0.80      3793\n",
      "\n",
      "    accuracy                           0.87     10976\n",
      "   macro avg       0.88      0.84      0.85     10976\n",
      "weighted avg       0.88      0.87      0.87     10976\n",
      "\n",
      "-----------------------------------------------------------------------------------------\n",
      "Accuracy is :\n",
      "0.8738155976676385\n",
      "Area under the curve : 0.836464\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "y_pred1 = xgb_model1.predict(xtest1)  #predicts\n",
    "print('confusion matrix')\n",
    "print(metrics.confusion_matrix(ytest1, y_pred1))\n",
    "print('classification report')\n",
    "print(metrics.classification_report(ytest1, y_pred1))\n",
    "print(\"-----------------------------------------------------------------------------------------\")\n",
    "print(\"Accuracy is :\")\n",
    "print(metrics.accuracy_score(ytest1, y_pred1))\n",
    "print('Area under the curve : %f' % (metrics.roc_auc_score(ytest1, y_pred1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE Senario 3\n",
    "15000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resampled dataset shape Counter({0: 24085, 1: 15000})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE \n",
    "\n",
    "x3 = df_.drop('target', axis=1)\n",
    "y3 = df_['target']\n",
    "\n",
    "'''Increase the crash samples from 5558 to 10000'''\n",
    "\n",
    "sm = SMOTE(random_state=42,sampling_strategy={1:15000})\n",
    "X_res2, y_res2 = sm.fit_sample(x3, y3)\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain shape\n",
      "(27359, 72)\n",
      "xtest shape\n",
      "(11726, 72)\n"
     ]
    }
   ],
   "source": [
    "xtrain2, xtest2, ytrain2, ytest2 = train_test_split(X_res2, y_res2, test_size=0.30, random_state=20)\n",
    "print('xtrain shape')\n",
    "print(xtrain2.shape)\n",
    "print('xtest shape')\n",
    "print(xtest2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.267773\tvalidation_1-error:0.27341\n",
      "[1]\tvalidation_0-error:0.252787\tvalidation_1-error:0.258741\n",
      "[2]\tvalidation_0-error:0.251142\tvalidation_1-error:0.259423\n",
      "[3]\tvalidation_0-error:0.246025\tvalidation_1-error:0.254221\n",
      "[4]\tvalidation_0-error:0.2359\tvalidation_1-error:0.242026\n",
      "[5]\tvalidation_0-error:0.232794\tvalidation_1-error:0.237336\n",
      "[6]\tvalidation_0-error:0.224241\tvalidation_1-error:0.227443\n",
      "[7]\tvalidation_0-error:0.219087\tvalidation_1-error:0.224714\n",
      "[8]\tvalidation_0-error:0.215944\tvalidation_1-error:0.222241\n",
      "[9]\tvalidation_0-error:0.206513\tvalidation_1-error:0.211922\n",
      "[10]\tvalidation_0-error:0.200885\tvalidation_1-error:0.205526\n",
      "[11]\tvalidation_0-error:0.196791\tvalidation_1-error:0.202968\n",
      "[12]\tvalidation_0-error:0.194817\tvalidation_1-error:0.20075\n",
      "[13]\tvalidation_0-error:0.191491\tvalidation_1-error:0.19606\n",
      "[14]\tvalidation_0-error:0.187141\tvalidation_1-error:0.19009\n",
      "[15]\tvalidation_0-error:0.184766\tvalidation_1-error:0.189152\n",
      "[16]\tvalidation_0-error:0.184473\tvalidation_1-error:0.187958\n",
      "[17]\tvalidation_0-error:0.179758\tvalidation_1-error:0.183694\n",
      "[18]\tvalidation_0-error:0.177638\tvalidation_1-error:0.181562\n",
      "[19]\tvalidation_0-error:0.175701\tvalidation_1-error:0.180624\n",
      "[20]\tvalidation_0-error:0.171278\tvalidation_1-error:0.176872\n",
      "[21]\tvalidation_0-error:0.169341\tvalidation_1-error:0.17491\n",
      "[22]\tvalidation_0-error:0.166453\tvalidation_1-error:0.172523\n",
      "[23]\tvalidation_0-error:0.162396\tvalidation_1-error:0.167065\n",
      "[24]\tvalidation_0-error:0.159034\tvalidation_1-error:0.165018\n",
      "[25]\tvalidation_0-error:0.156767\tvalidation_1-error:0.162459\n",
      "[26]\tvalidation_0-error:0.154501\tvalidation_1-error:0.160327\n",
      "[27]\tvalidation_0-error:0.151979\tvalidation_1-error:0.159645\n",
      "[28]\tvalidation_0-error:0.148178\tvalidation_1-error:0.154358\n",
      "[29]\tvalidation_0-error:0.147776\tvalidation_1-error:0.154017\n",
      "[30]\tvalidation_0-error:0.145327\tvalidation_1-error:0.152738\n",
      "[31]\tvalidation_0-error:0.143353\tvalidation_1-error:0.151714\n",
      "[32]\tvalidation_0-error:0.142878\tvalidation_1-error:0.15035\n",
      "[33]\tvalidation_0-error:0.139844\tvalidation_1-error:0.147109\n",
      "[34]\tvalidation_0-error:0.13904\tvalidation_1-error:0.146341\n",
      "[35]\tvalidation_0-error:0.138492\tvalidation_1-error:0.144465\n",
      "[36]\tvalidation_0-error:0.137724\tvalidation_1-error:0.144636\n",
      "[37]\tvalidation_0-error:0.136737\tvalidation_1-error:0.143442\n",
      "[38]\tvalidation_0-error:0.135202\tvalidation_1-error:0.143016\n",
      "[39]\tvalidation_0-error:0.13385\tvalidation_1-error:0.142248\n",
      "[40]\tvalidation_0-error:0.133448\tvalidation_1-error:0.141395\n",
      "[41]\tvalidation_0-error:0.133411\tvalidation_1-error:0.14131\n",
      "[42]\tvalidation_0-error:0.131474\tvalidation_1-error:0.139263\n",
      "[43]\tvalidation_0-error:0.130487\tvalidation_1-error:0.138581\n",
      "[44]\tvalidation_0-error:0.12961\tvalidation_1-error:0.137984\n",
      "[45]\tvalidation_0-error:0.128806\tvalidation_1-error:0.137046\n",
      "[46]\tvalidation_0-error:0.127234\tvalidation_1-error:0.136364\n",
      "[47]\tvalidation_0-error:0.126832\tvalidation_1-error:0.135767\n",
      "[48]\tvalidation_0-error:0.126284\tvalidation_1-error:0.13534\n",
      "[49]\tvalidation_0-error:0.125297\tvalidation_1-error:0.134914\n",
      "[50]\tvalidation_0-error:0.124968\tvalidation_1-error:0.134232\n",
      "[51]\tvalidation_0-error:0.124822\tvalidation_1-error:0.13389\n",
      "[52]\tvalidation_0-error:0.124237\tvalidation_1-error:0.133123\n",
      "[53]\tvalidation_0-error:0.122885\tvalidation_1-error:0.132697\n",
      "[54]\tvalidation_0-error:0.122738\tvalidation_1-error:0.133038\n",
      "[55]\tvalidation_0-error:0.121788\tvalidation_1-error:0.131673\n",
      "[56]\tvalidation_0-error:0.120947\tvalidation_1-error:0.131588\n",
      "[57]\tvalidation_0-error:0.120143\tvalidation_1-error:0.13082\n",
      "[58]\tvalidation_0-error:0.120034\tvalidation_1-error:0.131417\n",
      "[59]\tvalidation_0-error:0.119595\tvalidation_1-error:0.130565\n",
      "[60]\tvalidation_0-error:0.119083\tvalidation_1-error:0.131162\n",
      "[61]\tvalidation_0-error:0.118754\tvalidation_1-error:0.130906\n",
      "[62]\tvalidation_0-error:0.118681\tvalidation_1-error:0.129882\n",
      "[63]\tvalidation_0-error:0.117292\tvalidation_1-error:0.128347\n",
      "[64]\tvalidation_0-error:0.115282\tvalidation_1-error:0.127153\n",
      "[65]\tvalidation_0-error:0.114478\tvalidation_1-error:0.126471\n",
      "[66]\tvalidation_0-error:0.11382\tvalidation_1-error:0.125874\n",
      "[67]\tvalidation_0-error:0.113418\tvalidation_1-error:0.125277\n",
      "[68]\tvalidation_0-error:0.112687\tvalidation_1-error:0.124765\n",
      "[69]\tvalidation_0-error:0.112614\tvalidation_1-error:0.124169\n",
      "[70]\tvalidation_0-error:0.111663\tvalidation_1-error:0.123913\n",
      "[71]\tvalidation_0-error:0.110457\tvalidation_1-error:0.122889\n",
      "[72]\tvalidation_0-error:0.109288\tvalidation_1-error:0.121354\n",
      "[73]\tvalidation_0-error:0.109397\tvalidation_1-error:0.121781\n",
      "[74]\tvalidation_0-error:0.108301\tvalidation_1-error:0.121098\n",
      "[75]\tvalidation_0-error:0.107972\tvalidation_1-error:0.121098\n",
      "[76]\tvalidation_0-error:0.10735\tvalidation_1-error:0.120501\n",
      "[77]\tvalidation_0-error:0.106875\tvalidation_1-error:0.120757\n",
      "[78]\tvalidation_0-error:0.106546\tvalidation_1-error:0.120843\n",
      "[79]\tvalidation_0-error:0.105669\tvalidation_1-error:0.120501\n",
      "[80]\tvalidation_0-error:0.105596\tvalidation_1-error:0.120501\n",
      "[81]\tvalidation_0-error:0.10545\tvalidation_1-error:0.11999\n",
      "[82]\tvalidation_0-error:0.105194\tvalidation_1-error:0.119308\n",
      "[83]\tvalidation_0-error:0.10523\tvalidation_1-error:0.119393\n",
      "[84]\tvalidation_0-error:0.104646\tvalidation_1-error:0.119052\n",
      "[85]\tvalidation_0-error:0.103695\tvalidation_1-error:0.118369\n",
      "[86]\tvalidation_0-error:0.103586\tvalidation_1-error:0.118028\n",
      "[87]\tvalidation_0-error:0.103037\tvalidation_1-error:0.117005\n",
      "[88]\tvalidation_0-error:0.101941\tvalidation_1-error:0.116408\n",
      "[89]\tvalidation_0-error:0.101393\tvalidation_1-error:0.116664\n",
      "[90]\tvalidation_0-error:0.101246\tvalidation_1-error:0.116237\n",
      "[91]\tvalidation_0-error:0.100625\tvalidation_1-error:0.116067\n",
      "[92]\tvalidation_0-error:0.099784\tvalidation_1-error:0.115896\n",
      "[93]\tvalidation_0-error:0.099894\tvalidation_1-error:0.115811\n",
      "[94]\tvalidation_0-error:0.099455\tvalidation_1-error:0.116152\n",
      "[95]\tvalidation_0-error:0.099126\tvalidation_1-error:0.11564\n",
      "[96]\tvalidation_0-error:0.098066\tvalidation_1-error:0.115129\n",
      "[97]\tvalidation_0-error:0.098249\tvalidation_1-error:0.11547\n",
      "[98]\tvalidation_0-error:0.097884\tvalidation_1-error:0.115726\n",
      "[99]\tvalidation_0-error:0.097993\tvalidation_1-error:0.115385\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(eta=0.4, max_depth=6, min_child_weight=5)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model1.fit(xtrain2, ytrain2, eval_metric=['error'], eval_set=[((xtrain2, ytrain2)),(xtest2, ytest2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[6898  336]\n",
      " [1017 3475]]\n",
      "classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.95      0.91      7234\n",
      "           1       0.91      0.77      0.84      4492\n",
      "\n",
      "    accuracy                           0.88     11726\n",
      "   macro avg       0.89      0.86      0.87     11726\n",
      "weighted avg       0.89      0.88      0.88     11726\n",
      "\n",
      "-----------------------------------------------------------------------------------------\n",
      "Accuracy is :\n",
      "0.8846153846153846\n",
      "Area under the curve : 0.863575\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "y_pred2 = xgb_model1.predict(xtest2)  #predicts\n",
    "print('confusion matrix')\n",
    "print(metrics.confusion_matrix(ytest2, y_pred2))\n",
    "print('classification report')\n",
    "print(metrics.classification_report(ytest2, y_pred2))\n",
    "print(\"-----------------------------------------------------------------------------------------\")\n",
    "print(\"Accuracy is :\")\n",
    "print(metrics.accuracy_score(ytest2, y_pred2))\n",
    "print('Area under the curve : %f' % (metrics.roc_auc_score(ytest2, y_pred2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: density              Importance: 0.10987000167369843\n",
      "Variable: spd                  Importance: 0.09250999987125397\n",
      "Variable: std_5min             Importance: 0.07569999992847443\n",
      "Variable: avg_spd_5min         Importance: 0.05635000020265579\n",
      "Variable: direction__E         Importance: 0.054030001163482666\n",
      "Variable: up1_spd_diff         Importance: 0.05040999874472618\n",
      "Variable: dn1_spd_diff         Importance: 0.04259999841451645\n",
      "Variable: Precip.              Importance: 0.041430000215768814\n",
      "Variable: RAMP_1               Importance: 0.029330000281333923\n",
      "Variable: CAPLINK              Importance: 0.02831999957561493\n",
      "Variable: avg_spd_10min        Importance: 0.025499999523162842\n",
      "Variable: avg_spd_diff_up1dn1_5min Importance: 0.022910000756382942\n",
      "Variable: avg_spd_15min        Importance: 0.021800000220537186\n",
      "Variable: grade                Importance: 0.0203000009059906\n",
      "Variable: RAMP_0               Importance: 0.019710000604391098\n",
      "Variable: solar_altitude       Importance: 0.019540000706911087\n",
      "Variable: weekday_6            Importance: 0.019430000334978104\n",
      "Variable: solar_azimuth        Importance: 0.018939999863505363\n",
      "Variable: weekday_4            Importance: 0.018400000408291817\n",
      "Variable: Pop_Dense            Importance: 0.018139999359846115\n",
      "Variable: direction__W         Importance: 0.017899999395012856\n",
      "Variable: hour_0               Importance: 0.017149999737739563\n",
      "Variable: weekday_5            Importance: 0.016920000314712524\n",
      "Variable: weekday_1            Importance: 0.016599999740719795\n",
      "Variable: weekday_0            Importance: 0.01647000014781952\n",
      "Variable: weekday_2            Importance: 0.01640000008046627\n",
      "Variable: weekday_3            Importance: 0.01576000079512596\n",
      "Variable: month_2              Importance: 0.015110000036656857\n",
      "Variable: month_3              Importance: 0.013150000013411045\n",
      "Variable: VOL                  Importance: 0.012439999729394913\n",
      "Variable: month_4              Importance: 0.012319999746978283\n",
      "Variable: cv_10min             Importance: 0.005779999773949385\n",
      "Variable: cv_15min             Importance: 0.0052999998442828655\n",
      "Variable: std_15min            Importance: 0.004430000204592943\n",
      "Variable: High                 Importance: 0.0042500002309679985\n",
      "Variable: Low                  Importance: 0.004149999935179949\n",
      "Variable: cv_5min              Importance: 0.004139999859035015\n",
      "Variable: hour_17              Importance: 0.004009999800473452\n",
      "Variable: TRK                  Importance: 0.0039900001138448715\n",
      "Variable: Snow d.              Importance: 0.0037299999967217445\n",
      "Variable: std_10min            Importance: 0.003269999986514449\n",
      "Variable: month_11             Importance: 0.001550000044517219\n",
      "Variable: hour_1               Importance: 0.0\n",
      "Variable: hour_2               Importance: 0.0\n",
      "Variable: hour_3               Importance: 0.0\n",
      "Variable: hour_4               Importance: 0.0\n",
      "Variable: hour_5               Importance: 0.0\n",
      "Variable: hour_6               Importance: 0.0\n",
      "Variable: hour_7               Importance: 0.0\n",
      "Variable: hour_8               Importance: 0.0\n",
      "Variable: hour_9               Importance: 0.0\n",
      "Variable: hour_10              Importance: 0.0\n",
      "Variable: hour_11              Importance: 0.0\n",
      "Variable: hour_12              Importance: 0.0\n",
      "Variable: hour_13              Importance: 0.0\n",
      "Variable: hour_14              Importance: 0.0\n",
      "Variable: hour_15              Importance: 0.0\n",
      "Variable: hour_16              Importance: 0.0\n",
      "Variable: hour_18              Importance: 0.0\n",
      "Variable: hour_19              Importance: 0.0\n",
      "Variable: hour_20              Importance: 0.0\n",
      "Variable: hour_21              Importance: 0.0\n",
      "Variable: hour_22              Importance: 0.0\n",
      "Variable: hour_23              Importance: 0.0\n",
      "Variable: month_1              Importance: 0.0\n",
      "Variable: month_5              Importance: 0.0\n",
      "Variable: month_6              Importance: 0.0\n",
      "Variable: month_7              Importance: 0.0\n",
      "Variable: month_8              Importance: 0.0\n",
      "Variable: month_9              Importance: 0.0\n",
      "Variable: month_10             Importance: 0.0\n",
      "Variable: month_12             Importance: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " None,\n",
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       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances_xgb = list(xgb_model.feature_importances_)\n",
    "\n",
    "# List of tuples with variable and importance\n",
    "feature_importances_xgb = [(feature, round(importance, 5)) for feature, importance in zip(xtrain.columns, importances_xgb)]\n",
    "\n",
    "# Sort the feature importances by most important first\n",
    "xgb_feature_importances = sorted(feature_importances_xgb, key = lambda x: x[1], reverse = True )\n",
    "\n",
    "# Print out the feature and importances \n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in xgb_feature_importances]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_feature_importances_top20 = xgb_feature_importances[:20]\n",
    "featureNames, featureScores = zip(*list(xgb_feature_importances_top20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19267, 72) (19267,)\n",
      "(10376, 72) (10376,) \n",
      "\n",
      "Test Accuracy: 0.843195836545875\n",
      "Test F1: 0.38254269449715367\n",
      "Test Precision: 0.7\n",
      "Test Recall: 0.26318537859007834\n",
      "Test AUC: 0.7844760308625384\n",
      "1915\n",
      "1915 \n",
      "\n",
      "Fraction Predicted: 1.0\n",
      "Fraction False Alarm 4.418276762402089\n",
      "Train Accuracy: 0.8520267815435719\n",
      "Train F1: 0.43844790230451053\n",
      "Train Precision: 0.7761506276150628\n",
      "Train Recall: 0.30551743068899256\n",
      "Train AUC: 0.8164841715392706\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35, shuffle = True, random_state=10)\n",
    "\n",
    "print (X_train.shape,y_train.shape)\n",
    "print (X_test.shape,y_test.shape,\"\\n\")\n",
    "X_train = xgb.DMatrix(X_train,label=y_train,feature_names=feature_names)\n",
    "X_test =  xgb.DMatrix(X_test,label=y_test,feature_names=feature_names)\n",
    "# params = {\n",
    "#     'n_estimators': 2000,\n",
    "#     'max_depth':6,\n",
    "#     'reg_lambda': 0.0,\n",
    "#     'reg_alpha':1.0\n",
    "# }\n",
    "\n",
    "params = {\n",
    "#     'learning_rate': 0.1,\n",
    "#     'n_estimators':1000,\n",
    "#     'max_depth':7,\n",
    "#     'min_child_weight':5,\n",
    "#     'gamma':0.4,\n",
    "#     'subsample':0.8,\n",
    "#     'colsample_bytree':0.8,\n",
    "#     'objective': 'binary:logistic',\n",
    "#     'nthread':4,\n",
    "#     'scale_pos_weight':1,\n",
    "#     'seed':27\n",
    "    'learning_rate':0.1,      \n",
    "    'max_depth':7,\n",
    "    'min_child_weight': 5,\n",
    "    'gamma':0.4,\n",
    "    'reg_lambda': 1.0,\n",
    "    'reg_alpha':0.0,\n",
    "    'scale_pos_weight':1.0,\n",
    "    'eval_metric':'auc',\n",
    "    'objective':'binary:logistic',\n",
    "    'eta':0.5\n",
    "}\n",
    "\n",
    "booster = xgb.train(params,X_train)\n",
    "\n",
    "y_pred_test = np.round(booster.predict(X_test))\n",
    "print ('Test Accuracy:',accuracy_score(y_test,y_pred_test))\n",
    "print ('Test F1:',f1_score(y_test,y_pred_test))\n",
    "print ('Test Precision:',precision_score(y_test,y_pred_test))\n",
    "print ('Test Recall:',recall_score(y_test,y_pred_test))\n",
    "y_pred_test = booster.predict(X_test)\n",
    "print ('Test AUC:',roc_auc_score(y_test,y_pred_test))\n",
    "\n",
    "num_accidents_predicted_correctly = np.sum((y_test > 0) * (y_pred_test > 0))\n",
    "num_accidents = np.sum(y_test > 0)\n",
    "\n",
    "print(num_accidents_predicted_correctly)\n",
    "print(num_accidents,\"\\n\")\n",
    "\n",
    "print ('Fraction Predicted:',num_accidents_predicted_correctly/float(num_accidents))\n",
    "print ('Fraction False Alarm',(np.sum(y_pred_test>0) - num_accidents_predicted_correctly)/float(num_accidents))\n",
    "y_pred_train = np.round(booster.predict(X_train))\n",
    "print ('Train Accuracy:',accuracy_score(y_train,y_pred_train))\n",
    "print ('Train F1:',f1_score(y_train,y_pred_train))\n",
    "print ('Train Precision:',precision_score(y_train,y_pred_train))\n",
    "print ('Train Recall:',recall_score(y_train,y_pred_train))\n",
    "y_pred_train = booster.predict(X_train)\n",
    "print ('Train AUC:',roc_auc_score(y_train,y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "xgb.plot_importance(booster,ax=plt.gca(),importance_type='weight', max_num_features=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain1 = xgb.DMatrix(xtrain,label=ytrain,feature_names=feature_names)\n",
    "dtest1 =  xgb.DMatrix(xtest,label=ytest,feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17c3d8e4508>"
      ]
     },
     "execution_count": 57,
     "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": [
    "y_pred_test = booster.predict(dtest1)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(ytest,y_pred_test)\n",
    "\n",
    "y_pred_train = booster.predict(dtrain1)\n",
    "fpr_train, tpr_train, thresholds_train = roc_curve(ytrain,y_pred_train)\n",
    "fig,ax = plt.subplots()\n",
    "plt.plot([0,1],[0,1],'r-',label='Random Guess',color='orange',lw=3)\n",
    "plt.plot(fpr,tpr,label='ROC (Test)',lw=3)\n",
    "plt.plot(fpr_train,tpr_train,'r:',label='ROC (Train)',color='steelblue',lw=3)\n",
    "plt.grid()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17c3d722cc8>"
      ]
     },
     "execution_count": 58,
     "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(thresholds,tpr,'r-',label='TPR (Test)',color='orange',lw=3)\n",
    "plt.plot(thresholds_train,tpr_train,'r:',label='TPR (Train',color='orange',lw=3)\n",
    "plt.plot(thresholds,fpr,'r-',label='FPR (Test)',color='steelblue',lw=3)\n",
    "plt.plot(thresholds_train,fpr_train,'r:',label='FPR (Train)',color='steelblue',lw=3)\n",
    "plt.gca().set_xbound(lower=0,upper=1)\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('True/False Positive Rate')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.3.3'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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.figure(figsize=(15,15))\n",
    "\n",
    "y_pred_test = booster.predict(dtest1)\n",
    "y_pred_train = booster.predict(dtrain1)\n",
    "\n",
    "precision,recall,thresholds = precision_recall_curve(ytest,y_pred_test)\n",
    "precision_train, recall_train, thresholds_train = precision_recall_curve(ytrain,y_pred_train)\n",
    "fig,ax = plt.subplots()\n",
    "plt.plot(precision,recall,label='PR (Test)',lw=3)\n",
    "plt.plot(precision_train,recall_train,label='PR (Train)',lw=3)\n",
    "plt.xlabel('Precision')\n",
    "plt.ylabel('Recall')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17c3d847b08>"
      ]
     },
     "execution_count": 60,
     "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(thresholds,precision[:-1],'r-',label='Precision (Test)',color='orange',lw=3)\n",
    "plt.plot(thresholds_train,precision_train[:-1],'r:',label='Precision (Train',color='orange',lw=3)\n",
    "plt.plot(thresholds,recall[:-1],'r-',label='Recall (Test)',color='steelblue',lw=3)\n",
    "plt.plot(thresholds_train,recall_train[:-1],'r:',label='Recall (Train)',color='steelblue',lw=3)\n",
    "#plt.plot([0,1],[0,1],'k-',lw=2)\n",
    "plt.gca().set_xbound(lower=0,upper=1)\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision/Recall')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = booster.predict(dtest1) > 0.32\n",
    "print ('Test Accuracy:',accuracy_score(y_test,y_pred_test))\n",
    "print ('Test F1:',f1_score(y_test,y_pred_test))\n",
    "print ('Test Precision:',precision_score(y_test,y_pred_test))\n",
    "print ('Test Recall:',recall_score(y_test,y_pred_test))\n",
    "y_pred_test = booster.predict(dtest1)\n",
    "print ('Test AUC:',roc_auc_score(y_test,y_pred_test))\n",
    "print ('Test AP:',average_precision_score(y_test,y_pred_test))\n",
    "\n",
    "y_pred_train = booster.predict(dtrain) > 0.32\n",
    "print ('Train Accuracy:',accuracy_score(y_train,y_pred_train))\n",
    "print ('Train F1:',f1_score(y_train,y_pred_train))\n",
    "print ('Train Precision:',precision_score(y_train,y_pred_train))\n",
    "print ('Train Recall:',recall_score(y_train,y_pred_train))\n",
    "y_pred_train = booster.predict(dtrain)\n",
    "print ('Train AUC:',roc_auc_score(y_train,y_pred_train))\n",
    "print ('Test AP:',average_precision_score(y_train,y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Density - Flow - Speed\n",
    "df_1['density1'] = df['VOL']/df['spd']\n",
    "\n",
    "#ndf['density'] = (ndf['VOL']+ndf['TRK'])/ndf['spd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = pd.concat([df1, ndf1])\n",
    "df_1 = df_1.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density - Speed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_crash = df_1[df_1['target']==0]\n",
    "crash = df_1[df_1['target']==1]\n",
    "print('Density - Speed')\n",
    "plt.scatter(crash['density'], crash['spd'],s=1, color='r', label='Crash')\n",
    "\n",
    "plt.scatter(non_crash['density'], non_crash['spd'], s=1, color='g', label ='Non-Crash')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Density (veh/mi)', fontsize=12)\n",
    "plt.ylabel('Speed (mph)', fontsize=12)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1['volume'] = df_1['VOL']+df_1['TRK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flow - Speed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_crash = df_1[df_1['target']==0]\n",
    "crash = df_1[df_1['target']==1]\n",
    "print('Flow - Speed')\n",
    "plt.scatter(crash['VOL'], crash['spd'],s=1, color='red', label = 'Crash')\n",
    "plt.scatter(non_crash['VOL'], non_crash['spd'], s=1, color='green', label = 'Non-Crash')\n",
    "plt.legend()\n",
    "plt.xlabel('Flow Rate (vph)', fontsize=12)\n",
    "plt.ylabel('Speed (mph)', fontsize=12)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flow - Speed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2 = df_1[df_1['density']<1050]\n",
    "\n",
    "non_crash = df_2[df_2['target']==0]\n",
    "crash = df_2[df_2['target']==1]\n",
    "print('Flow - Speed')\n",
    "plt.scatter(crash['density'], crash['VOL'],s=1, color='r', label='Crash')\n",
    "plt.scatter(non_crash['density'], non_crash['VOL'], s=1, color='g', label='Non-Crash')\n",
    "plt.legend()\n",
    "plt.xlabel('Density (veh/mi)', fontsize=12)\n",
    "plt.ylabel('Flow Rate (vph)', fontsize=12)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density - Speed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_crash = df_2[df_2['target']==0]\n",
    "crash = df_2[df_2['target']==1]\n",
    "print('Density - Speed')\n",
    "plt.scatter(crash['density'], crash['spd'],s=1, color='r', label='Crash')\n",
    "\n",
    "plt.scatter(non_crash['density'], non_crash['spd'], s=1, color='g', label ='Non-Crash')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Density (veh/mi)', fontsize=12)\n",
    "plt.ylabel('Speed (mph)', fontsize=12)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "\n",
    "def annotate_axes(fig):\n",
    "    for i, ax in enumerate(fig.axes):\n",
    "        ax.text(0.5, 0.5, \"ax%d\" % (i+1), va=\"center\", ha=\"center\")\n",
    "        ax.tick_params(labelbottom=True, labelleft=True)\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10), dpi= 80)\n",
    "\n",
    "#fig.suptitle(\"Controlling subplot sizes with width_ratios and height_ratios\")\n",
    "\n",
    "legend=['Eastbound', 'Westbound']\n",
    "gs = GridSpec(3, 1, height_ratios=[4, 1, 1])\n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax3 = fig.add_subplot(gs[2])\n",
    "\n",
    "ax1.hist([df_E['MP'], df_W['MP']], 40, histtype='stepfilled', orientation='vertical', color =['blue', 'red'])\n",
    "ax2.plot(df_E['MP'], df_E['grade'])\n",
    "ax3.plot(df_E['MP'], df_E['RAMP'])\n",
    "\n",
    "ax1.set_ylabel('Crash Frequency', fontsize=14)\n",
    "ax2.set_ylabel('Grade (%)', fontsize = 12)\n",
    "ax3.set_ylabel('Ramp Existence (0 or 1)', fontsize= 12)\n",
    "ax3.set_xlabel('Milepost', fontsize=14)\n",
    "\n",
    "ax1.legend(legend)\n",
    "\n",
    "# xlabels = ax1.get_xticks().tolist()\n",
    "# ax1.set_xticklabels(xlabels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}