8.in-vehicle coupon recommendation Data Set_Gradientboosting_06.05.2022.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c77c308",
   "metadata": {},
   "source": [
    "# import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d2b9c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12529, 59) (12529,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import RandomState\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "seed = 42\n",
    "rng = RandomState(seed)\n",
    "\n",
    "df = pd.read_csv('Model.csv')\n",
    "\n",
    "X, y = df.iloc[:, 1:-1], df.iloc[:, -1]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab7e979",
   "metadata": {},
   "source": [
    "# train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "daea370a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set:  (10023, 59) (10023,)\n",
      "Test set:  (2506, 59) (2506,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=seed)\n",
    "print('Train set: ', X_train.shape, y_train.shape)\n",
    "print('Test set: ', X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae40ddd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=1.0, max_depth=1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=1.0, max_depth=1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=1.0, max_depth=1, random_state=42)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbc= GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
    "                                max_depth=1, random_state=seed)\n",
    "gbc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd371396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6918088396687618"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6769eef2",
   "metadata": {},
   "source": [
    "# accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "449f1075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6819632881085395"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54647bd8",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56ab4946",
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'max_depth': 4,\n",
    "        'learning_rate': 1.0,\n",
    "        'n_estimators':500}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c8ebb13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=1.0, max_depth=4, n_estimators=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=1.0, max_depth=4, n_estimators=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=1.0, max_depth=4, n_estimators=500)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc1=GradientBoostingClassifier(**params)\n",
    "gbc1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec4d0499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9860321261099472"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc1.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ed8adb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7402234636871509"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63841575",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validation_score(estimator):\n",
    "    scores = cross_val_score(\n",
    "        estimator, X_train, y_train, cv=10, n_jobs=-1,\n",
    "        scoring='roc_auc'\n",
    "    )\n",
    "    \n",
    "    return scores.mean(), scores.std()\n",
    "\n",
    "\n",
    "def plot_cross_validation(title, X, Y, error, xlabel, ylabel='ROC AUC'):\n",
    "    plt.title(title)\n",
    "    plt.grid()\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    plt.errorbar(X, Y, error, linestyle='None', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac54167",
   "metadata": {},
   "source": [
    "# change n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ab363c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [05:37<00:00, 67.59s/it]\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"
    }
   ],
   "source": [
    "title = 'Change no_of_estimators'\n",
    "xlabel = 'No of Estimators'\n",
    "n_estimators = [600,700,800,900,1000]\n",
    "means = []\n",
    "stddevs = []\n",
    "\n",
    "for n in tqdm(n_estimators):\n",
    "    clf = GradientBoostingClassifier(random_state=seed,\n",
    "                          n_estimators=n,learning_rate=0.1)\n",
    "    mean, std = cross_validation_score(clf)\n",
    "    means.append(mean)\n",
    "    stddevs.append(std)\n",
    "\n",
    "plot_cross_validation(title, n_estimators, means, stddevs, xlabel)\n",
    "plt.savefig('GradientBoost_no_of_estimators.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df38a41",
   "metadata": {},
   "source": [
    "# chnage learning_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f4a22eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [02:21<00:00, 47.02s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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DLJQ0JyIez9UZAVwLjIuIVZIOBIiI5cBRuf2sBW7L7f7bEfHNsvpuZmbdK/OKYyywIiJWRsRmYCYwvqrOucCsiFgFEBEbauznJOCJiHi6xL6amVmDypxzfCSwOre+Bji2qs47gcGSKsBewFURcVNVnbOBm6vKJkn6FPAg8FcR8UL1wSVNBCYCtLS0UKlUdjKMndfevgmgT47dpaOjo0+P39ccv+Nv5vihnHNQZuJQjbKocfxjyK4qhgILJN0fEb8GkDQE+BhwSa7NdcDlaV+XA98CPrPdgSKmA9MBWltbo62tbVdi2SnXLV8AQFvbcb1+7C6VSoW+iL2/cPyOv5njh3LOQZmJYw0wKrd+CLCuRp1nI2IjsFHSfOBI4Ndp+6nAwxHxTFeD/LKk7wI/LqHvZma7vU98ZwHt7Zvo6dxZ5j2OhcAYSYelK4ezgTlVdW4HTpC0h6RhZENZy3Lbz6FqmErSQbnVM4AlPd5zMzPrVrdXHJIOAA7Ifwsqlb8L2BARv62344jYImkSMA8YBMyIiKWSLk7bp0XEMkl3Ao8CW4HrI2JJOs4wsm9kXVS16yslHUU2VPVUje1mZlaiekNV/0J2P6HaIcBfk30jqq6ImAvMrSqbVrU+FZhao+0rwH41ys/b0XHNzKw89RLHeyLi3urCiJgn6Vsl9mnAuOWivrspbmZWlnr3OAbv5DYzMxvA6iWO39R6nYekU4GV5XXJzMz6s3pDVV8Efizp48BDqawVOA74aNkdMzOz/qnbK470EN57gHuB0elzL/Dergf0zMys+dR9ADAi/gf4f73UFzMz2w3Ue47jZbZ9RUgAzwI/Bb4SEc+V3DczM+uH6g1V7RURb8599ia7x7EUmNZdOzMzG9gKvXIkIl6IiG8Dby+pP2Zm1s8VfleVpMGU+3JEMzPrx+rd4/jjGsX7AJ8Aflhaj8zMrF+rd+VwetV6AM+RTbb0n+V1yczM+rNuE0dEXNDdNknvi4iF5XTJzMz6s4bvVUg6gmxOjXOAF8m+YWVmZk2mbuKQdChZojgH2AIcCrRGxFPld83MzPqjbr9VJekXZHNpDAbOjIhjgJedNMzMmlu9r+P+FtgLaAEOSGXRffXtSRonabmkFZKmdFOnTdJiSUsl3Zsrf0rSY2nbg7nyfSXdLek36ec+RfpkZma7pt6T4+PJXnL4MPA1SU8C+0ga28iOJQ0CrgFOBY4Azkn3SfJ1RgDXAh+LiHcBZ1Xt5sSIOCoi8vdTpgD3RMQY4J60bmZmvaTuA4AR8WJEzIiIk4FjgUuBf5K0uoF9jwVWRMTKiNgMzATGV9U5F5gVEavS8TY0sN/xwI1p+UZgQgNtzMyshzT8rar0S/1fgH9JN813ZCSQTzBryJJP3juBwZIqZMNiV0XETV2HBO6SFMB3ImJ6Km+JiPWpT+slHVjr4JImAhMBWlpaqFQqDXR54Ono6Gja2MHxO/7mjr+9fROdnZ09fg526tUhEfF0A9VUq2mN4x8DnAQMBRZIuj/N93F8RKxLieFuSb+KiPkF+jgdmA7Q2toabW1tjTYdUCqVCs0aOzh+x9/c8V+3fAHt7e09fg4Kv6uqgDXAqNz6IcC6GnXujIiNEfEsMB84EiAi1qWfG4DbyIa+AJ6RdBBA+tnI8JaZmfWQMhPHQmCMpMMkDSF7eHBOVZ3bgRMk7SFpGNlQ1jJJe0raC0DSnsBHgCWpzRzg/LR8ftqHmZn1knovObwSWBkR06rKvwi8JSK+Um/HEbFF0iRgHjAImBERSyVdnLZPi4hlku4EHgW2AtdHxBJJbwNuk9TVxx9ExJ1p11cAt0q6EFjF9t/EMjOzEtW7x/FR4N01yq8i+0VfN3EARMRcsocI82XTqtanAlOrylaShqxq7PM5snsiZmbWB+oNVUVEbK1RuJXaN77NzKwJ1Escr0gaU12YyjaV1yUzM+vP6g1VXQrcIenvgIdSWStwCfCFkvtlZmb9VL35OO6QNAGYDPx5Kl4K/K+IeKwX+mZmZv1Q3QcAI2IJcL6k4dlqbOydbpmZWX9V9zkOSZ+TtAp4Glgl6WlJn+udrpmZWX9Ubz6OvyH7Sm5bROwXEfsBJwKnpm1mZtaE6l1xnAf8cXqmAnjt+YqPA58qu2NmZtY/7ei16r+rUbaJ7ClvMzNrQvUSxxpJ2z2hLelDwPryumRmZv1ZvW9V/QVwu6T7yJ7jCOB9wPFsPyGTmZk1iXpTxy4le1fVfGA08La0/O60zczMmtCOnuP4HTAjXyZpkKRPRsT3S+2ZmZn1S/W+jvtmSZdIulrSycpMArq+WWVmZk2o3hXHvwEvAAuAPwO+DAwBxkfE4vK7ZmZm/VG9xPG2iHgPgKTrgWeBt0bEy73SMzMz65fqfR331a6FiOgEniyaNCSNk7Rc0gpJU7qp0yZpsaSlku5NZaMk/VTSslT++Vz9yyStTW0WSzqtSJ/MzGzX1LviOFLSS2lZwNC0LrIXHr653o4lDQKuAU4G1gALJc2JiMdzdUYA1wLjImKVpAPTpi3AX0XEw2nu8Yck3Z1r++2I+GaxUM3MrCfUe636oF3c91hgRdcrSyTNJHv+4/FcnXOBWRGxKh1zQ/q5nvSQYUS8LGkZMLKqrZmZ9YG6X8fdRSOB1bn1NcCxVXXeCQyWVAH2Aq6KiJvyFSSNBo4GHsgVT5L0KeBBsiuTF6oPLmkiMBGgpaWFSqWyK7Hstjo6Opo2dnD8jr+5429v30RnZ2ePn4MyE0etecmjxvGPAU4ChgILJN0fEb8GSPOA/Aj4QkR0DZtdB1ye9nU58C3gM9sdKGI6MB2gtbU12tradjWe3VKlUqFZYwfH7/ibO/7rli+gvb29x89BmYljDTAqt34IsK5GnWfTBFEbJc0HjgR+LWkwWdL4fkTM6moQEc90LUv6LvDjkvpvZmY11H077i5aCIyRdJikIcDZwJyqOrcDJ0jaQ9IwsqGsZZIE/CuwLCL+Md9A0kG51TOAJaVFYGZm2yntiiMitqQnzecBg4AZEbFU0sVp+7SIWCbpTuBRsle1Xx8RSyR9gGw+kMckLU67/GpEzAWulHQU2VDVU8BFZcVgZmbbK3OoivSLfm5V2bSq9anA1Kqy+6h9j4SIOK+Hu2lmZgWUOVRlZmYDkBOHmZkV4sRhZmaFOHGYmVkhThxmZgPQ7EVrWbSqneUvbOX4K37C7EVre2zfThxmZgPM7EVruWTWY2zu3ArA2vZNXDLrsR5LHk4cZmYDzNR5y9n0auc2ZZte7WTqvOU9sn8nDjOzAWZd+6ZC5UU5cZiZDTAHjxhaqLwoJw4zswFm8imHM3TwtlMqDR08iMmnHN4j+y/1lSNmZtb7Jhw9EoAv//BRNnduZeSIoUw+5fDXyneVE4eZ2QA04eiR3PzLVbS3tzPvKx/q0X17qMrMzApx4jAzs0KcOMzMrBAnDjMzK6TUxCFpnKTlklZImtJNnTZJiyUtlXTvjtpK2lfS3ZJ+k37uU2YMZma2rdISh6RBwDXAqcARwDmSjqiqMwK4FvhYRLwLOKuBtlOAeyJiDHBPWjczs15S5hXHWGBFRKyMiM3ATGB8VZ1zgVkRsQogIjY00HY8cGNavhGYUF4IZmZWrcznOEYCq3Pra4Bjq+q8ExgsqQLsBVwVETftoG1LRKwHiIj1kg6sdXBJE4GJAC0tLVQqlV0KZnfV0dHRtLGD43f8zR1/e/smOjs7e/wclJk4VKMsahz/GOAkYCiwQNL9DbatKyKmA9MBWltbo62trUjzAaNSqdCssYPjd/zNHf91yxfQ3t7e4+egzMSxBhiVWz8EWFejzrMRsRHYKGk+cOQO2j4j6aB0tXEQsAEzM+s1Zd7jWAiMkXSYpCHA2cCcqjq3AydI2kPSMLLhqGU7aDsHOD8tn5/2YWZmvaS0K46I2CJpEjAPGATMiIilki5O26dFxDJJdwKPAluB6yNiCUCttmnXVwC3SroQWEX6JpaZmfWOUl9yGBFzgblVZdOq1qcCUxtpm8qfI7snYmZmfcBPjpuZWSFOHGZmVogTh5mZFeLEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoU4cZiZWSFOHGZmVogTh5mZFVJq4pA0TtJySSskTamxvU3Si5IWp8+lqfzwXNliSS9J+kLadpmktbltp5UZg5mZbau0iZwkDQKuAU4mm0N8oaQ5EfF4VdWfRcRH8wURsRw4KreftcBtuSrfjohvltV3MzPrXplXHGOBFRGxMiI2AzOB8Tuxn5OAJyLi6R7tnZmZ7ZQyE8dIYHVufU0qq3acpEck3SHpXTW2nw3cXFU2SdKjkmZI2qeH+mtmZg0oc85x1SiLqvWHgUMjoiPdq5gNjHltB9IQ4GPAJbk21wGXp31dDnwL+Mx2B5cmAhMBWlpaqFQqOxvHbq2jo6NpYwfH7/ibO/729k10dnb2+DkoM3GsAUbl1g8B1uUrRMRLueW5kq6VtH9EPJuKTwUejohncvVeW5b0XeDHtQ4eEdOB6QCtra3R1ta2a9HspiqVCs0aOzh+x9/c8V+3fAHt7e09fg7KHKpaCIyRdFi6cjgbmJOvIOktkpSWx6b+PJercg5Vw1SSDsqtngEsKaHvZmbWjdKuOCJii6RJwDxgEDAjIpZKujhtnwacCXxW0hZgE3B2RASApGFk38i6qGrXV0o6imyo6qka283MrERlDlUREXOBuVVl03LLVwNXd9P2FWC/GuXn9XA3zcysAD85bmZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoU4cZiZWSFOHGZmVogTh5mZFeLEYWZmhThxmJlZIU4cZmZWiBOHmZkVUmrikDRO0nJJKyRNqbG9TdKLkhanz6W5bU9JeiyVP5gr31fS3ZJ+k37uU2YMZma2rdISh6RBwDXAqcARwDmSjqhR9WcRcVT6fL1q24mpvDVXNgW4JyLGAPekdTMz6yVlXnGMBVZExMqI2AzMBMb3wH7HAzem5RuBCT2wTzMza1CZiWMksDq3viaVVTtO0iOS7pD0rlx5AHdJekjSxFx5S0SsB0g/D+zpjpuZDQS3XHQclxw7tMf3u0eP7/F1qlEWVesPA4dGRIek04DZwJi07fiIWCfpQOBuSb+KiPkNHzxLNhMBWlpaqFQqRfs/IHR0dDRt7OD4HX9zxw/lnIMyE8caYFRu/RBgXb5CRLyUW54r6VpJ+0fEsxGxLpVvkHQb2dDXfOAZSQdFxHpJBwEbah08IqYD0wFaW1ujra2tB0PbfVQqFZo1dnD8jr+544dyzkGZQ1ULgTGSDpM0BDgbmJOvIOktkpSWx6b+PCdpT0l7pfI9gY8AS1KzOcD5afl84PYSYzAzsyqlXXFExBZJk4B5wCBgRkQslXRx2j4NOBP4rKQtwCbg7IgISS3AbSmn7AH8ICLuTLu+ArhV0oXAKuCssmIwM7PtlTlURUTMBeZWlU3LLV8NXF2j3UrgyG72+RxwUs/21MzMGuUnx83MrBAnDjMzK8SJw8zMCnHiMDOzQhRR/UzewCPpt8DTfd2PPrI/8Gxfd6IPOX7H38zxw66dg0Mj4oDqwqZIHM1M0oNVL4lsKo7f8Tdz/FDOOfBQlZmZFeLEYWZmhThxDHzT+7oDfczxN7dmjx9KOAe+x2FmZoX4isPMzApx4jAzs0KcOAYISeMkLZe0QtJ287BL+qSkR9PnF5JqvkRyd7Wj+HP13iepU9KZvdm/sjUSv6Q2SYslLZV0b2/3sUwN/Pe/t6T/SLONLpV0QV/0syySZkjaIGlJN9sl6Z/T+XlU0h/s0gEjwp/d/EP22vongLcBQ4BHgCOq6vwhsE9aPhV4oK/73Zvx5+r9hOyNzWf2db97+d9/BPA48Na0fmBf97uX4/8q8A9p+QDgeWBIX/e9B8/BHwF/ACzpZvtpwB1kM7O+f1f///cVx8AwFlgRESsjYjMwExifrxARv4iIF9Lq/WQzMg4UO4w/+XPgR3Qza+RurJH4zwVmRcQqyGbW7OU+lqmR+APYK00cN5wscWzp3W6WJ7JptZ+vU2U8cFNk7gdGpBlUd4oTx8AwElidW1+TyrpzIdlfHwPFDuOXNBI4A5jGwNPIv/87gX0kVSQ9JOlTvda78jUS/9XA75NNX/0Y8PmI2No73esXiv6OqKvUiZys16hGWc3vWUs6kSxxfKDUHvWuRuL/J+ArEdGZZpYcSBqJfw/gGLJJ0IYCCyTdHxG/LrtzvaCR+E8BFgMfAt4O3C3pZxHxUsl96y8a/h3RCCeOgWENMCq3fgjZX1bbkPRe4Hrg1MhmUhwoGom/FZiZksb+wGmStkTE7F7pYbkaiX8N8GxEbAQ2SppPNsvmQEgcjcR/AXBFZAP+KyQ9Cfwe8Mve6WKfa+h3RKM8VDUwLATGSDpM0hDgbGBOvoKktwKzgPMGyF+ZeTuMPyIOi4jRETEa+CHwuQGSNKCB+IHbgRMk7SFpGHAssKyX+1mWRuJfRZpyWlILcDiwsld72bfmAJ9K3656P/BiRKzf2Z35imMAiIgtkiYB88i+YTIjIpZKujhtnwZcCuwHXJv+6t4SA+StoQ3GP2A1En9ELJN0J/AosBW4PiJqfnVzd9Pgv//lwA2SHiMbtvlKRAyY161LuhloA/aXtAb4W2AwvBb/XLJvVq0AXiG7Atv546WvapmZmTXEQ1VmZlaIE4eZmRXixGFmZoU4cZiZWSFOHGZmVogThzU9SR29cIyLe/s1H5ImSDqiN49pzcFfx7WmJ6kjIob3wH4GRURnT/SpJ44p6QbgxxHxw97skw18vuIwy5E0WdLCNGfB13Lls9PLAZdKmpgr75D0dUkPAMel9W+keR/uT08pI+kySV9KyxVJ/yDpl5J+LemEVD5M0q3p2LdIekDSdg9pSnpK0qWS7gPOkvRnqc+PSPpR2s8fAh8DpqY5ON6ePnemOH4m6ffKPZs2UDlxmCWSPgKMIXtN91HAMZL+KG3+TEQcQ/bOq7+QtF8q35NsDoRjI+K+tH5/RBwJzAf+rJvD7RERY4EvkD3lC/A54IWIeC/Zk87H1Onu7yLiAxExk+x16e9Lx1wGXBgRvyB7zcTkiDgqIp4ApgN/nuL4EnBt42fH7HV+5YjZ6z6SPovS+nCyRDKfLFmckcpHpfLngE6yOT66bAZ+nJYfAk7u5lizcnVGp+UPAFcBRMQSSY/W6estueV3S/o7ssmahpO9emMbkoaTTeb177m3A7+xzv7NuuXEYfY6AX8fEd/ZplBqAz4MHBcRr0iqAG9Km39XdY/h1Xj9xmEn3f8/9j816hR53/vG3PINwISIeETSp8neWVTtDUB7RBxV4BhmNXmoyux184DPpL/OkTRS0oHA3mRDSK+k+wLvL+n49wEfT8c+AnhPg+32AtZLGgx8Mlf+ctpGmnfiSUlnpf1LA2zeees9ThxmSUTcBfyAbJKjx8hev74XcCewRxo6upxs6t0yXAsckI7zFbI32b7YQLv/AzwA3A38Klc+E5gsaZGkt5MllQslPQIspfb0umY75K/jmvUTkgYBgyPid+kX/T3AO9M82mb9hu9xmPUfw4CfpiEnAZ910rD+yFccZmZWiO9xmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkh/x8XWXxFkDr4lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Change learning rate'\n",
    "xlabel = 'learning rate'\n",
    "learning_rate = [0.1,0.05,1.0]\n",
    "means = []\n",
    "stddevs = []\n",
    "\n",
    "for n in tqdm(learning_rate):\n",
    "    clf = GradientBoostingClassifier(random_state=0,\n",
    "                          n_estimators=600,learning_rate=n)\n",
    "    mean, std = cross_validation_score(clf)\n",
    "    means.append(mean)\n",
    "    stddevs.append(std)\n",
    "\n",
    "plot_cross_validation(title, learning_rate, means, stddevs, xlabel)\n",
    "plt.savefig('GradientBoost_learning_rate.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c83b24e",
   "metadata": {},
   "source": [
    "# ROC_AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c3f515ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set:  0.74780526735834\n",
      "ROC AUC on test set:  0.7387029559455401\n"
     ]
    }
   ],
   "source": [
    "# Best params\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "\n",
    "gbc = GradientBoostingClassifier(max_depth=4,learning_rate=0.05,n_estimators=600, \n",
    "                              random_state=rng)\n",
    "gbc.fit(X_train, y_train)\n",
    "y_pred = gbc.predict(X_test)\n",
    "\n",
    "print('Accuracy on test set: ', accuracy_score(y_test, y_pred))\n",
    "print('ROC AUC on test set: ', roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a78bd97",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "312e57e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Preetam\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(gbc, X_test, y_test)\n",
    "plt.savefig('GBC_confmat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "333c57b7",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}