4. Quora - ML Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;font-size:30px;\" > Quora Question Pair Similarity </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4 : Applying ML Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6VXPfRp-tARR",
    "outputId": "6c27acaf-2c6a-467f-fd7a-68efce98e30b"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import time\n",
    "import warnings\n",
    "import sqlite3\n",
    "from sqlalchemy import create_engine # database connection\n",
    "import csv\n",
    "import os\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.manifold import TSNE\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics.classification import accuracy_score, log_loss\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from collections import Counter\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from collections import Counter, defaultdict\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import math\n",
    "from sklearn.metrics import normalized_mutual_info_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import precision_recall_curve, auc, roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZihvUPvHtARd"
   },
   "source": [
    "<h1>4. Machine Learning Models </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CtN9VBPutARf"
   },
   "source": [
    "<h2> 4.1 Reading data from file and storing into sql table </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "owBQdjY1tARh"
   },
   "outputs": [],
   "source": [
    "#Creating db file from csv\n",
    "if not os.path.isfile('train.db'):\n",
    "    disk_engine = create_engine('sqlite:///train.db')\n",
    "    start = dt.datetime.now()\n",
    "    chunksize = 180000\n",
    "    j = 0\n",
    "    index_start = 1\n",
    "    for df in pd.read_csv('final_features.csv', names=['Unnamed: 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chunksize=chunksize, iterator=True, encoding='utf-8', ):\n",
    "        df.index += index_start\n",
    "        j+=1\n",
    "        print('{} rows'.format(j*chunksize))\n",
    "        df.to_sql('data', disk_engine, if_exists='append')\n",
    "        index_start = df.index[-1] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4hpD3aBktARn"
   },
   "outputs": [],
   "source": [
    "#http://www.sqlitetutorial.net/sqlite-python/create-tables/\n",
    "def create_connection(db_file):\n",
    "    \"\"\" create a database connection to the SQLite database\n",
    "        specified by db_file\n",
    "    :param db_file: database file\n",
    "    :return: Connection object or None\n",
    "    \"\"\"\n",
    "    try:\n",
    "        conn = sqlite3.connect(db_file)\n",
    "        return conn\n",
    "    except Error as e:\n",
    "        print(e)\n",
    " \n",
    "    return None\n",
    "\n",
    "\n",
    "def checkTableExists(dbcon):\n",
    "    cursr = dbcon.cursor()\n",
    "    str = \"select name from sqlite_master where type='table'\"\n",
    "    table_names = cursr.execute(str)\n",
    "    print(\"Tables in the databse:\")\n",
    "    tables =table_names.fetchall() \n",
    "    print(tables[0][0])\n",
    "    return(len(tables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nR8ZIUnttARs",
    "outputId": "810fb3fb-7da2-4b78-9e29-9edabbf68cf6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tables in the databse:\n",
      "data\n"
     ]
    }
   ],
   "source": [
    "read_db = 'train.db'\n",
    "conn_r = create_connection(read_db)\n",
    "checkTableExists(conn_r)\n",
    "conn_r.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SZq5gaaztARy"
   },
   "outputs": [],
   "source": [
    "# try to sample data according to the computing power you have\n",
    "if os.path.isfile(read_db):\n",
    "    conn_r = create_connection(read_db)\n",
    "    if conn_r is not None:\n",
    "        # for selecting first 1M rows\n",
    "        # data = pd.read_sql_query(\"\"\"SELECT * FROM data LIMIT 100001;\"\"\", conn_r)\n",
    "        \n",
    "        # for selecting random points\n",
    "        data = pd.read_sql_query(\"SELECT * From data ORDER BY RANDOM() LIMIT 100001;\", conn_r)\n",
    "        conn_r.commit()\n",
    "        conn_r.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZkeBKktKtAR3"
   },
   "outputs": [],
   "source": [
    "# remove the first row \n",
    "data.drop(data.index[0], inplace=True)\n",
    "y_true = data['is_duplicate']\n",
    "data.drop(['Unnamed: 0', 'id','index','is_duplicate'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QKSenpsmtAR9",
    "outputId": "81d890ce-df79-4402-9324-84817dbd5a7d"
   },
   "outputs": [
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       "      <th>cwc_min</th>\n",
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       "      <th>3</th>\n",
       "      <td>0.833319444675922</td>\n",
       "      <td>0.714275510349852</td>\n",
       "      <td>0.999983333611106</td>\n",
       "      <td>0.857130612419823</td>\n",
       "      <td>0.687495703151855</td>\n",
       "      <td>0.687495703151855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>10.6274425089359</td>\n",
       "      <td>8.40339290164411</td>\n",
       "      <td>14.16905092448</td>\n",
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       "      <td>...</td>\n",
       "      <td>9.43481918051839</td>\n",
       "      <td>47.0162477493286</td>\n",
       "      <td>6.11483122035861</td>\n",
       "      <td>-9.12068559974432</td>\n",
       "      <td>-3.70159254875034</td>\n",
       "      <td>-6.60001696646214</td>\n",
       "      <td>-26.1024898421019</td>\n",
       "      <td>13.254497602582</td>\n",
       "      <td>10.0541534200311</td>\n",
       "      <td>-6.71233975887299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.749981250468738</td>\n",
       "      <td>0.749981250468738</td>\n",
       "      <td>0.499987500312492</td>\n",
       "      <td>0.499987500312492</td>\n",
       "      <td>0.624992187597655</td>\n",
       "      <td>0.624992187597655</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.158434137701988</td>\n",
       "      <td>13.7061135396361</td>\n",
       "      <td>6.35377380251884</td>\n",
       "      <td>-7.79693192709237</td>\n",
       "      <td>7.42785215843469</td>\n",
       "      <td>0.602214589715004</td>\n",
       "      <td>-17.1489275898784</td>\n",
       "      <td>-1.66515278816223</td>\n",
       "      <td>4.06413406133652</td>\n",
       "      <td>3.87387558817863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 794 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             cwc_min            cwc_max            csc_min            csc_max  \\\n",
       "1  0.199996000079998  0.166663888935184                0.0                0.0   \n",
       "2  0.399992000159997  0.399992000159997  0.499987500312492  0.499987500312492   \n",
       "3  0.833319444675922  0.714275510349852  0.999983333611106  0.857130612419823   \n",
       "4                0.0                0.0  0.599988000239995  0.499991666805553   \n",
       "5  0.749981250468738  0.749981250468738  0.499987500312492  0.499987500312492   \n",
       "\n",
       "             ctc_min             ctc_max last_word_eq first_word_eq  \\\n",
       "1   0.14285510206997  0.0999990000099999          0.0           0.0   \n",
       "2  0.444439506227709   0.444439506227709          0.0           0.0   \n",
       "3  0.687495703151855   0.687495703151855          0.0           1.0   \n",
       "4  0.249997916684028   0.230767455634957          0.0           0.0   \n",
       "5  0.624992187597655   0.624992187597655          1.0           0.0   \n",
       "\n",
       "  abs_len_diff mean_len         ...                       374_y  \\\n",
       "1          3.0      8.5         ...          -0.452613651752472   \n",
       "2          0.0      9.0         ...           -7.29706252366304   \n",
       "3          0.0     16.0         ...            10.6274425089359   \n",
       "4          1.0     12.5         ...            9.43481918051839   \n",
       "5          0.0      8.0         ...          -0.158434137701988   \n",
       "\n",
       "              375_y             376_y               377_y              378_y  \\\n",
       "1   15.652570605278  6.08213365077972   -11.8397651314735   13.1632919311523   \n",
       "2  8.17464561760426  11.8678730130196  -0.180742904543877   -7.7285645082593   \n",
       "3  8.40339290164411    14.16905092448   -14.7492953687906   13.0176268517971   \n",
       "4  47.0162477493286  6.11483122035861   -9.12068559974432  -3.70159254875034   \n",
       "5  13.7061135396361  6.35377380251884   -7.79693192709237   7.42785215843469   \n",
       "\n",
       "               379_y              380_y              381_y             382_y  \\\n",
       "1  -2.98961134254932  -22.7671056985855  -0.55659094452858  16.7820047736168   \n",
       "2  -1.18125408887863  -10.2087883651257   1.95615618489683   5.9711550520733   \n",
       "3   15.6888161092065  -26.4774019122124   24.7521114945412    14.29726113379   \n",
       "4  -6.60001696646214  -26.1024898421019    13.254497602582  10.0541534200311   \n",
       "5  0.602214589715004  -17.1489275898784  -1.66515278816223  4.06413406133652   \n",
       "\n",
       "                383_y  \n",
       "1    20.3098403215408  \n",
       "2  -0.634018301963806  \n",
       "3   0.945210464298725  \n",
       "4   -6.71233975887299  \n",
       "5    3.87387558817863  \n",
       "\n",
       "[5 rows x 794 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KaWHDzqUtASD"
   },
   "source": [
    "<h2> 4.2 Converting strings to numerics </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iLV60gkptASD",
    "outputId": "f297e0f4-52d5-4ab4-8a43-f0ff82f63698"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cwc_min\n",
      "cwc_max\n",
      "csc_min\n",
      "csc_max\n",
      "ctc_min\n",
      "ctc_max\n",
      "last_word_eq\n",
      "first_word_eq\n",
      "abs_len_diff\n",
      "mean_len\n",
      "token_set_ratio\n",
      "token_sort_ratio\n",
      "fuzz_ratio\n",
      "fuzz_partial_ratio\n",
      "longest_substr_ratio\n",
      "freq_qid1\n",
      "freq_qid2\n",
      "q1len\n",
      "q2len\n",
      "q1_n_words\n",
      "q2_n_words\n",
      "word_Common\n",
      "word_Total\n",
      "word_share\n",
      "freq_q1+q2\n",
      "freq_q1-q2\n",
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    }
   ],
   "source": [
    "# after we read from sql table each entry was read it as a string\n",
    "# we convert all the features into numaric before we apply any model\n",
    "cols = list(data.columns)\n",
    "for i in cols:\n",
    "    data[i] = data[i].apply(pd.to_numeric)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_LpfQwc9tASJ"
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/7368789/convert-all-strings-in-a-list-to-int\n",
    "y_true = list(map(int, y_true.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CuMTqWGutASO"
   },
   "source": [
    "<h2> 4.3 Random train test split( 70:30) </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3Rat2obGtASP"
   },
   "outputs": [],
   "source": [
    "X_train,X_test, y_train, y_test = train_test_split(data, y_true, stratify=y_true, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1Iw9zCHqtASS",
    "outputId": "910b684b-0876-4dd8-e0d9-457846236833"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data : (70000, 794)\n",
      "Number of data points in test data : (30000, 794)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of data points in train data :\",X_train.shape)\n",
    "print(\"Number of data points in test data :\",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0oDV15LJtASY",
    "outputId": "70a1e4eb-3f31-4f1e-a53b-ad972978505d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Distribution of output variable in train data ----------\n",
      "Class 0:  0.6324857142857143 Class 1:  0.36751428571428574\n",
      "---------- Distribution of output variable in train data ----------\n",
      "Class 0:  0.3675 Class 1:  0.3675\n"
     ]
    }
   ],
   "source": [
    "print(\"-\"*10, \"Distribution of output variable in train data\", \"-\"*10)\n",
    "train_distr = Counter(y_train)\n",
    "train_len = len(y_train)\n",
    "print(\"Class 0: \",int(train_distr[0])/train_len,\"Class 1: \", int(train_distr[1])/train_len)\n",
    "print(\"-\"*10, \"Distribution of output variable in train data\", \"-\"*10)\n",
    "test_distr = Counter(y_test)\n",
    "test_len = len(y_test)\n",
    "print(\"Class 0: \",int(test_distr[1])/test_len, \"Class 1: \",int(test_distr[1])/test_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XfxcPT6jtASg"
   },
   "outputs": [],
   "source": [
    "# This function plots the confusion matrices given y_i, y_i_hat.\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j\n",
    "    \n",
    "    A =(((C.T)/(C.sum(axis=1))).T)\n",
    "    #divid each element of the confusion matrix with the sum of elements in that column\n",
    "    \n",
    "    # C = [[1, 2],\n",
    "    #     [3, 4]]\n",
    "    # C.T = [[1, 3],\n",
    "    #        [2, 4]]\n",
    "    # C.sum(axis = 1)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array\n",
    "    # C.sum(axix =1) = [[3, 7]]\n",
    "    # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]\n",
    "    #                           [2/3, 4/7]]\n",
    "\n",
    "    # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]\n",
    "    #                           [3/7, 4/7]]\n",
    "    # sum of row elements = 1\n",
    "    \n",
    "    B =(C/C.sum(axis=0))\n",
    "    #divid each element of the confusion matrix with the sum of elements in that row\n",
    "    # C = [[1, 2],\n",
    "    #     [3, 4]]\n",
    "    # C.sum(axis = 0)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array\n",
    "    # C.sum(axix =0) = [[4, 6]]\n",
    "    # (C/C.sum(axis=0)) = [[1/4, 2/6],\n",
    "    #                      [3/4, 4/6]] \n",
    "    plt.figure(figsize=(20,4))\n",
    "    \n",
    "    labels = [1,2]\n",
    "    # representing A in heatmap format\n",
    "    cmap=sns.light_palette(\"blue\")\n",
    "    plt.subplot(1, 3, 1)\n",
    "    sns.heatmap(C, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 2)\n",
    "    sns.heatmap(B, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Precision matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 3)\n",
    "    # representing B in heatmap format\n",
    "    sns.heatmap(A, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Recall matrix\")\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UStQJ5F_tASk"
   },
   "source": [
    "<h2> 4.4 Building a random model (Finding worst-case log-loss) </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qwMDqcU7tASl",
    "outputId": "c1e90d53-25ec-445b-e33a-299538520e32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on Test Data using Random Model 0.887242646958\n"
     ]
    },
    {
     "data": {
      "image/png": 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/PEyD0rFjqLtqVRiqCCFJ+Prr4clqEG44xXqVPfUU9O4dfgtffhnOI6R6M6+k\nAxbNqJwNE5EK5U6SUduZ23vvzI8tkyeX7rskuxQnRCQZxQmJGThQcUICPRpe4j37rOJEpipi0msR\nEREREREREanElDASEREREREREZEEShiJiIiIiIiIiEgCJYxERERERERERCSBEkYiIiIiIiIiIpJA\nCSMREREREREREUmghJGIiIiISJaYWTczm25mM83sL0mWX2hmi8zsi+h1SdyygrjyEeXbchERqe5q\nVnQDRERERESqIjPLAx4GjgbygQlmNsLdpxarOszd+yTZxBp33yfb7RQREUlGPYxERERERLKjEzDT\n3We7+2/AUKBHBbdJREQqiVL2Qr3AzGZErwuisi3N7C0zm2ZmU8zsrky2lYoSRiIiIiIim8HMepvZ\nxLhX72JVmgNz4z7nR2XFnWpmX5rZcDNrGVdeJ9ruODM7qazbLyIiFSeuF+qxQDvgLDNrl6TqMHff\nJ3o9Ea3bEOgPHEi4OdHfzLaN6t/r7rsDHYGDzezYdNtKR0PSREREREQ2g7sPAgalqWLJViv2+U3g\nRXdfa2aXA08DXaJlO7r7fDPbGfjAzL5y91mlbriIiFQGhb1QAcws1gu1+LDlZLoCo919SbTuaKCb\nu78I/AfA3X8zs8+AFpvbQPUwEhERERHJjnwgvsdQC2B+fAV3X+zua6OPjwP7xS2bH/05G/iQcLdY\nRERyRAk9UUvTC7XEdc1sG+BE4P0StpWSEkYiIiIiItkxAWhrZq3NrDbQE0h42pmZ7RD3sTvwTVS+\nrZltEb1vDBxMZnedRUSkknD3Qe6+f9wrvldqpr1QW7l7B+A9Qi/UEtc1s5rAi8A/Yj2Y0mwrJQ1J\nE5FqpUOHim6BiIhUZmUZJ9x9vZn1AUYBecAQd59iZgOAie4+ArjKzLoD64ElwIXR6nsAj5nZBsJN\n3ruSPF1NRETKWRnGiYx6ocZ9fBy4O27dw4ut+2Hc50HADHd/IINtpaSEkYiIiIhIlrj7SGBksbKb\n4973A/olWe9/QPusN1BERCpKYS9UYB6hF+rZ8RXMbAd3XxB9LOyFSrgRcWfcRNfHEMUSM7sdaABc\nkuG2UlLCSERERERERESkHJWmF6q7LzGz2whJJ4ABUVkL4EZgGvCZmQE8FD0RLVWP1pRKTBiZWT1g\njbtvMLNdgd2Bt919XaZ/ESIiUnUpToiISDqKEyIiyW1uL9Ro2RBgSLGyfJLPb5R2W6lkMun1x0Ad\nM2tOmF1+6OUaAAAgAElEQVT7IuCpTfkSEZGqysyuMbMpZva1mb1oZnWiyU3Hm9kMMxsWTXSKmW0R\nfZ4ZLW8Vt51+Ufl0M+taUfuzmRQnREQkHcUJEZEclEnCyNz9F+AU4J/ufjLQLrvNEhGp/KIT36uA\n/d19L0JX0p6ECeTud/e2wFKgV7RKL2Cpu7cB7o/qYWbtovX2BLoB/zKzvPLcl1JSnBARkXQUJ0RE\nclBGCSMz+x1wDvBWVKa5j0REgppA3ejRlVsCC4AuwPBo+dPASdH7HhQ9vnI4cKSFgcU9gKHuvtbd\nvwNmAp3Kqf1lQXFCRETSUZwQEclBmSSM/kQY5/ZaNAHTzsB/stssEZGKZ2a9zWxi3Kt3/HJ3nwfc\nC/xASBQtByYBy9x9fVQtH2gevW8OzI3WXR/VbxRfnmSdXKA4ISIi6ShOiIjkoBIz++7+EfARgJnV\nAH5296uy3TARkYrm7oOAQamWR4+x7AG0BpYBLwPHJttUbJUUy1KV5wTFCRERSUdxQkQkN5XYw8jM\nXjCzraOnG0wFpptZ3+w3TUSk0jsK+M7dF0VPenkV6AxsEw1RA2gBzI/e5wMtAaLlDQiPtCwsT7JO\npac4ISIi6ShOiIjkpkyGpLVz9xWEOThGAjsC52W1VSIiueEH4CAz2zKai+hIwonwf4DTojoXAG9E\n70dEn4mWf+DuHpX3jJ6i1hpoC3xaTvtQFhQnREQkHcUJEZEclEnCqJaZ1SIc4N+I7qLnzFAJEZFs\ncffxhMmrPwO+IhxTBwE3ANea2UzCHEWDo1UGA42i8muBv0TbmQK8REg2vQNc4e4F5bgrpaU4ISIi\n6ShOiIjkoEyeTvAYMAeYDHxsZjsBK7LZKBGRXOHu/YH+xYpnk+QpZ+7+K3B6iu3cAdxR5g0sH4oT\nIiKSjuKEiEgOymTS638A/4gr+t7Mjshek0REJJcoToiISDqKEyIiuSmTHkaY2fHAnkCduOIBWWmR\niIjkHMUJERFJR3FCRCT3ZPKUtEeBM4ErCY9+Ph3YKcvtEhGRHKE4ISIi6ShOiIjkpkwmve7s7ucD\nS939VuB3JD7+WUREqjfFCRERSUdxQkQkB2WSMFoT/fmLmTUD1gGts9ckERHJMYoTIiKSjuKEiEgO\nymQOo3+b2TbAQMKjox14IqutEhGRXKI4ISIi6ShOiIjkoEyeknZb9PYVM/s3UMfdl2e3WSIikisU\nJ0REJB3FCRGR3JQyYWRmp6RZhru/mp0miYhILlCcEBGRdBQnRERyW7oeRiemWeaADvAiItWb4oSI\niKSjOCEiksNSJozc/aLybIiIiOQWxQkREUlHcUJEJLelfEqamV1rZr2SlF9pZn/KbrNERKSyU5wQ\nEZF0FCdERHJbyoQRcDHwbJLyQdEyERGp3hQnREQkHcUJEZEcli5h5O7+W5LCtYBlr0kiIpIjFCdE\nRCQdxQkRkRyWLmGEmW2XSZmIiFRPihMiIpKO4oSISO5KlzAaCLxlZoeZWf3odTjwJnBvubROREQq\nM8UJERFJR3FCRCSHpXtK2jNmtggYAOxFePTlFKC/u79dTu0TEZFKSnFCRETSUZwQEcltKRNGANGB\nXAdzERFJSnFCRETSUZwQEcldaecwEhERERERERGR6kcJIxERERERERERSaCEkYiIiIiIiIiIJEg5\nh5GZXZtuRXe/r+ybIyIiuUJxQkSkZGbWDXgQyAOecPe7ii2/kPA0sXlR0UPu/kS07ALgpqj8dnd/\nulwaXUYUJ0RE0stGjDCz/YCngLrASOBqd3czawgMA1oBc4Az3H1puvalm/S6fkZ7KCIi1ZXihIhI\nGmaWBzwMHA3kAxPMbIS7Ty1WdZi79ym2bkOgP7A/4elik6J1057cVzKKEyIiKWQxRjwC9AbGERJG\n3QgPH/gL8L6732Vmf4k+35CujSkTRu5+a8Z7KiIi1Y7ihIhIiToBM919NoCZDQV6AMUvBpLpCox2\n9yXRuqMJJ/0vZqmtZU5xQkQkrTKPEWb2IbC1u38SlT8DnERIGPUADo/Wfxr4kM1NGMWYWR2gF7An\nUCdW7u4XZ7ATm23evJLriIhIxVOcEJHqysx6E+7ixgxy90Fxn5sDc+M+5wMHJtnUqWb2e+Bb4Bp3\nn5ti3eZl0vByVlFx4vrrs7l1ySU6Z5CKUkKcyEaMaB69L14OsJ27LwBw9wVm1rSk9peYMAKeBaYR\nMlgDgHOAbzJYT0Sk0unQoaJbUCUpTohIlbEpcSI66R+UpoolW63Y5zeBF919rZldTrjr2yXDdXOF\n4oSIVBllGCeyESPKNHZk8pS0Nu7+N2B1NInS8UD7zf1CERGpchQnRESSywdaxn1uAcyPr+Dui919\nbfTxcWC/TNfNIYoTIiIby0aMyI/eJ9vmT2a2A0D058KSGphJwmhd9OcyM9sLaECYVVtERAQUJ0RE\nUpkAtDWz1mZWG+gJjIivEDt5j3SnqOfNKOAYM9vWzLYFjonKcpHihIjIxso8RkRDzlaa2UFmZsD5\nwBvROiOAC6L3F8SVp5TJkLRBUQP+Fn3BVsDNGawnIiLVg+KEiEgS7r7ezPoQTuzzgCHuPsXMBgAT\n3X0EcJWZdQfWA0uAC6N1l5jZbYQLCoABsclNc5DihIhIMVmMEX8AngLqEia7fjsqvwt4ycx6AT8A\np5fURnOvnEOh58/P2THaIpJFzZolHZebsYEDMz+29O1buu+S7FKcEJFkFCckxkxxQgJNei3xFCcy\nl8lT0rYATiV0Gy2s7+4DstcsERHJFYoTIiKSjuKEiEhuymRI2hvAcmASsLaEuiIiUv0oToiISDqK\nEyIiOSiThFELd++W9ZaIiEiuUpwQEZF0FCdERHJQJk9J+5+Z6bGXIiKSiuKEiIikozghIpKDMulh\ndAhwoZl9R+hCaoC7e4estkxERHKF4oSIiKSjOCEikoMySRgdm/VWiIjkIDPbDRgWV7Qz4THBzYET\ngd+AWcBF7r4sWqcf0AsoAK5y91FReTfgQcIjNZ9w97vKaz/KgOKEiIikozghIpKDUiaMzGxrd18B\nrCzH9oiI5Ax3nw7sA2BmecA84DVgN6Cfu683s7uBfsANZtYO6AnsCTQD3jOzXaPNPQwcDeQDE8xs\nhLtPLdcd2kSKEyIiko7ihIhIbkvXw+gF4ATC0wyc0HU0xgl30kVEJDgSmOXu3wPfx5WPA06L3vcA\nhrr7WuA7M5sJdIqWzXT32QBmNjSqW6kTRihOiIhIeooTIiI5LGXCyN1PiP5sXX7NERGpPMysN9A7\nrmiQuw9KUb0n8GKS8ospGrbWnJBAismPygDmFis/cJMbXM4UJ0REJB3FCRGR3FbiHEZmtm+S4uXA\n9+6+vuybJCJSOUTJoVQJokJmVhvoThh6Fl9+I7AeeD5WlOxrSP7ESt+kxlYgxQkREUlHcUJEJDdl\nMun1v4B9gS8JFzvtgclAIzO73N3fzWL7RERywbHAZ+7+U6zAzC4gdMM/0t1jyZ98oGXcei2A+dH7\nVOW5QHFCRETSUZwQEclBye5qFzcH6Oju+7v7foQJXr8GjgLuyWLbRERyxVnEDUeLnnh2A9Dd3X+J\nqzcC6GlmW5hZa6At8CkwAWhrZq2j3ko9o7q5Yg6KEyIiktocFCdERHJOJj2Mdnf3KbEP7j7VzDq6\n+2yzZKMrRESqDzPbkvB0s8viih8CtgBGR8fJce5+ubtPMbOXCJNZrweucPeCaDt9gFFAHjAk/rib\nAxQnREQkHcUJEZEclEnCaLqZPQIMjT6fCXxrZlsA67LWMhGRHBD1IGpUrKxNmvp3AHckKR8JjCzz\nBpYPxQkREUlHcUJEJAdlMiTtQmAm8CfgGmB2VLYOOCJbDRMRkZxxIYoTIiKS2oUoToiI5JwSexi5\n+xrg/6JXcavKvEUiIpJTFCdERCQdxQkRkdyUMmFkZi+5+xlm9hVJHu/s7h2y2jIREanUFCdERCQd\nxQkRkdyWrofR1dGfJ5RHQ0REJOcoToiISDqKEyIiOSxlwsjdF5hZHjDY3Y8qxzaJiEgOUJwQEZF0\nFCdERHJb2kmvo8c9/2JmDcqpPSIikkMUJ0REJB3FCRGR3FXipNfAr8BXZjYaWB0rdPerstYqERHJ\nJYoTIiKSjuKEiEgOyiRh9Fb0EhERSUZxQkRE0lGcEBHJQZkkjIYBbQhPNpjl7r9mt0kiIpJjFCdE\nRCQdxQkRkRyUcg4jM6tpZvcA+cDTwHPAXDO7x8xqlVcDRUSkclKcEBGRdBQnRERyW7pJrwcCDYHW\n7r6fu3cEdgG2Ae4tj8aJiEilpjghIiLpKE6IiOSwdAmjE4BL3X1lrMDdVwB/AI7LdsNERKTSU5wQ\nEZF0FCdERHJYuoSRu7snKSwgjD8WEZHqTXFCRETSUZwQEclh6RJGU83s/OKFZnYuMC17TRIRkRyh\nOCEiIukoToiI5LB0T0m7AnjVzC4GJhHuAhwA1AVOLoe2iYhI5aY4ISIi6ShOiIjksJQ9jNx9nrsf\nCAwA5gA/AAPcvZO7zyun9omISCWlOCEiIukoToiIpGdm3cxsupnNNLO/pKl3mpm5me0ffa5tZk+a\n2VdmNtnMDo/K65vZF3Gvn83sgWjZhWa2KG7ZJSW1L10PIwDc/QPgg0x3WEREqhfFCRERSUdxQkRk\nY2aWBzwMHA3kAxPMbIS7Ty1Wrz5wFTA+rvhSAHdvb2ZNgbfN7IDoIQP7xK07CXg1br1h7t4n0zaW\nmDASEalKOnSo6BaIiEhlVtZxwsy6AQ8CecAT7n5XinqnAS8DB7j7RDNrBXwDTI+qjHP3y8u2dSIi\nsqnKME50Ama6+2wAMxsK9ACmFqt3G3APcF1cWTvgfQB3X2hmy4D9gU9jFcysLdAU+O/mNjDdpNci\nIiIiIrKZ4u4eH0s4uT/LzNolqZfs7jHALHffJ3opWSQikmPMrLeZTYx79Y5b3ByYG/c5PyqLX78j\n0NLd/11s05OBHmZW08xaA/sBLYvVOYvQoyj+qZSnmtmXZjbczIrX34gSRiIiIiIi2VF499jdfwNi\nd4+Li909/rU8GyciItnl7oPcff+416C4xZZslcKFZjWA+4E/J6k3hJBgmgg8APwPWF+sTk/gxbjP\nbwKt3L0D8B7wdEntV8JIRERERGQzlHDnGEp39xigtZl9bmYfmdmhZdt6ERGpYPkk9gpqAcyP+1wf\n2Av40MzmAAcBI8xsf3df7+7XRD1QewDbADNiK5rZ3kBNd58UK3P3xe6+Nvr4OKFXUlqaw0hERERE\nZDNEd4oHpamS6d3jC5PUWwDs6O6LzWw/4HUz29PdV5SiySIiUnlMANpGQ8rmEXoEnR1b6O7Lgcax\nz2b2IXBdNM/dloC5+2ozOxpYX2yy7LNI7F2Eme3g7guij90J8+SlpYSRiIiIiEh2bMrdY4DtCXeP\nu7v7RGAtgLtPMrNZwK6E4QciIpLj3H29mfUBRhEejDDE3aeY2QBgoruPSLN6U2CUmW0gJJvOK7b8\nDOC4YmVXmVl3wtC1JSS/WZFACSMRERERkewozd3jJsASdy8ws52BtsDs8my8iIhkl7uPBEYWK7s5\nRd3D497PAXZLs92dk5T1A/ptSvuUMBIRERERyYJS3j3+PTDAzNYDBcDl7r4k+60WEREJlDASERER\nEcmSUtw9fgV4JauNExERSUNPSRMRERERERERkQRKGImIiIiIiIiISAIljEREREREREREJIESRiIi\nIiIiIiIikkCTXpeRnj27sOWW9ahRowZ5eXk89tirPPXUP3nrrZdo0KAhAJdcci0HHXQY69b9xn33\n9Wf69K8xM6688kb22edAfv11DbfccjXz5/9AjRp5dO58BL17X5f0+55//jFGjhxOXl4N+vS5iU6d\nDgXg008/5qGH7qCgYAPHH386Z5/dG4AFC+YyYMC1rFy5nLZt2/HXv95DrVq1y+cvp5pJ9luIGTZs\nMI8+eg+vv/4JDRo0ZMyY93jyyQcxC3X79Pkr7dvvz48/zuPmm69kw4YC1q9fzymnnEv37mdt9F0r\nVixjwIBr+PHHeWy/fXP693+A+vUb4O788593MH78R9SpU4cbbriLXXfdE4B33nmN5557BIBzz/0D\n3bqdXD5/MSJSolTH8JgRI17k9ddfoEaNGtStuyV//vNttGrVhtGjRzBs2ODCerNnT2fQoNdo02YP\nnnjift5993VWrlzB229/Xt67JJtpc38LALNmTeO++/qzevUqatSowaOPDqd27S0K173xxsuZPz+f\nJ5/8d7nuk4iUXteu8OCDkJcHTzwBd9+duPyCC2DgQJg3L3x+6CEYHIWH88+Hm24K72+/HZ55Brba\nCv7736L1W7SA556Da67J/r5I6ZQUJ2I++ugdbrnlah59dDi77dY+5bUowPXX92Lx4kUUFBTQocN+\nXH11f/Ly8spzt6SSUcKoDN1//9OFyaGY0067kDPP7JVQ9u9/vwzAkCFvsnTpYm644VIefXQ4AGee\neTEdOx7EunW/8ec/X8j48R9x4IGHJaw/Z85MPvjgLZ588i0WL/6J6667iGeeGQXAgw8OYODAJ2nS\nZDsuv/w0OnfuQqtWbXjssXs5/fQL6dLleO6772ZGjhxOjx5nZ+uvotpL9ltYuHABEyf+j+22a1ZY\ntt9+v+Pgg4/EzJg1axq33vonnnnmHRo1asJDDw2ldu3arFmzmosuOpHOnbvQuPF2Cdt84YVB7Lvv\n7zj77N688MIgXnhhEJdd1pfx4z9m3rw5PPfcu3zzzWTuv/8WHnnkZVasWMYzzzzEo4++gplx2WWn\ncPDBXahfv0G5/L2ISGoFBQUpj+ExRx55YmHyeOzY9/nXv/7OPfcM5uiju3P00d2BkCy66aY/0qbN\nHgB07nwEJ598Duee27X8d0o2S2l+CwUF67nzzr706zeQNm12Z/nypeTlFZ3uffzxu9SpU6/c90lE\nSq9GDXj4YTj6aMjPhwkTYMQI+OabxHrDhsGVVyaWbbst9O8P++8P7jBpUlh32TLo2LGo3sSJ8Oqr\nSCWXSZwA+OWXVbz66rPsscfehWWprkVr1KhB//4PUq/eVrg7/ftfxUcfvUOXLseX675J5aIhaRXg\n++9nsu++BwGw7baN2Gqr+kyf/jV16tSlY8dQXqtWbdq2bceiRT9ttP7Yse/Tpcvx1K5dmx12aEmz\nZjsxbdqXTJv2Jc2a7USzZi2pVas2Xbocz9ix7+PufP75OA47LFwsdO16MmPGvF9+OywAPPzw37ns\nsr6AFZbVrVsPs/D511/XFL6vVas2tWuHHmC//fYb7huSbvN//3ufrl1PAqBr15MYO/Y9IPxGjjnm\nJMyMdu32YfXqFSxevJAJE8aw334Hs/XW21C/fgP22+9gPv30v0m3LSLlK9UxPF69elsVvo8/ZsR7\n//236NLlhMLP7drtQ6NGTbPXcClzpfktTJgwlp133o02bXYHoEGDbQvvDq9Zs5qXX36S8877Qznt\niYiUpU6dYOZM+O47WLcOhg6FHj0yW7drVxg9GpYuDUmi0aOhW7fEOm3aQNOmiT2OpHLKJE4ADBny\nID17XpLQyzTVtSgUxZaCgvWsX7+O+OsWqZ6yljAys93N7Egz26pYebdU6+QyM+jbtxe9e5/Cm28O\nKyx/7bXn6dXrRO6+ux8rVy4HYJdddmfs2PcpKFjPggVz+fbbKSxcuCBhe6tWreCTT/7Dvvv+bqPv\n+vnnn2jadPvCz02abMfPP/+UsnzFiqVstdXWhXcYmzTZnp9/3jgRJWUj2W9h7Nj3ady4aeEJfLz/\n/nc055/fjX79LuP66+8sLF+4cAG9ep3ImWceTs+el27UuwhgyZLFhReCjRo1ZenSJcDGv5HGjbdP\n+xsRqQjVLU6UJNP/n6+99jznnHMUjz02kCuvvGmj5R9+OJIjj9TdwFxWmt9Cfv53mFkUh07mxRcf\nL6w/ZMiDnHHGxdSpUyf7OyFSBhQnEjVvDnPnFn3Ozw9lxZ16KkyeDC+/HIaYZbruWWeF3klS+WUS\nJ2bMmMrChT/yu98dkVBe0rVo3769OPnkztStW6+ww4FUX1lJGJnZVcAbwJXA12YWn/u+M/laYGa9\nzWyimU187rlB2Wha1vzzny8yaNBr3H3347z++vNMnjyB7t3P4vnnR/P442/QqFFT/vWvuwA47rhT\nadJkey677FQeeuhO9tqrY8LY0IKC9dx227Wccsp5NGvWcqPvcvckLbCk5WZGsurJ7kpL2Uj2W3ju\nuUe56KKrk9Y/9NCjeeaZd7jttocZMuTBwvKmTXdg8OA3ee65d3n33ddYsuTnjNuwqb8R2Xxmto2Z\nDTezaWb2jZn9Lm7ZdWbmZtY4+mxm9g8zm2lmX5rZvnF1LzCzGdHrgorYl/JUHeNESTL9/3nyyefw\n/PPv0bv3dTz77CMJy6ZOncwWW9Sldetds9ZOyb7S/BYKCgr46qtJ3HTTQP7xjxcYM+Y9Jk36hJkz\nv2HevB849NCjs95+kbJQFnECqlacSHbKVvxw8eab0KoV7L03vPcePP105uv27AkvvlgmTZUsKylO\nbNiwgYcf/jt//OMNG9Ur6Vp04MDBvPLKGNat+43PPx+XnR2QnJGtOYwuBfZz91Vm1goYbmat3P1B\n0vRrc/dBREf2+fNJdsVbacV6f2y7bSMOPfRopk37kr33PqBw+QknnE6/fpcDkJdXkyuu+Gvhsj59\netKiRavCz/fe+zeaN2/FaaddmPS7mjTZnoULfyz8vGjRTzRuHHqZFC9v1KgpDRpsy6pVKygoWE9e\nXk0WLfpRwxOyqPhvYfLkT/nxx3wuuSSc5yxa9CO9e5/CI4+8TMOGTQrX23vvA5g//weWL1+SMP9R\n48bb0apVW776aiKHHZZ4Q61hw0YsXryQRo2asnjxQrbdNqxX/Dfy888/0rhxU5o02Z4vvvi0sHzR\nop/YZ59OZf+XUL08CLzj7qeZWW1gSwAzawkcDfwQV/dYoG30OhB4BDjQzBoC/YH9AQcmmdkId19a\nfrtR7qpdnChJsmN7umN1ly7H88ADtySU/ec/b2mugSqgNL+FJk22Z++9OxXGkQMP/D0zZkyhbt0t\n+fbbr+nZswsFBetZtmwJf/rTeTzwwLNZ3ReRUih1nDCrWnEiPx9axt1LbtEC5s9PrLNkSdH7xx8v\nmhQ7Px8OPzxx3Q8/LPrcoQPUrAmffVbWrZZsKClO/PLLar777lv+9KfzAViyZBE33vgH7rjjEXbb\nrX3aa1GA2rW3oHPnLowd+z77739wdndGKrVsDUnLc/dVAO4+BzgcONbM7qMKDoRcs+YXfvllVeH7\niRPH0rp1WxYvXlhY57//fY/WrdsCYa6BNWt+AWDixLHk5eUVTlA2ePD9rF69ij59/koqnTt34YMP\n3uK3335jwYK5zJs3h91378Duu7dn3rw5LFgwl3XrfuODD96ic+cumBkdOx7IRx+FibFHjXqNgw/u\nkpW/i+ou2W9h993b89prnzB06AcMHfoBTZpsz6BBr9KwYRPmzfu+8A7Bt99OYf36dWy99bYsWvQj\na9f+CsDKlcv5+uvPaNmy9Ubf17lzF0aNeh2AUaNep3PnIwvL3333ddydqVO/oF69+jRq1JQDDjiE\niRPHsHLlclauXM7EiWM44IBDyuOvpkoys62B3wODAdz9N3dfFi2+H7geEk5WewDPeDAO2MbMdgC6\nAqPdfUmUJBoNVPXu9tUqTmQi1TE8Xn7+nML348Z9SPPmOxV+3rBhAx9+qMkpq4LS/BYOOOAQZs+e\nzq+/rqGgYD2TJ09gp53a0KPH2QwfPoahQz/gn/98gRYtWilZJJWd4kQxEyZA27ahB1GtWqFH0IgR\niXW2LxqlRPfuRRNijxoFxxwD22wTXsccE8pizjpLvYtySUlxYqut6vPGG+MLrz/atdunMFmU6lp0\nzZrVhdevBQXrGT/+I3bccecK2T+pPLLVw+hHM9vH3b8AiO4MnAAMAdpn6TsrzNKli/nb364AQlfw\no446gU6dfs+dd/Zl5sxpmMH22zfn2msHALBs2WKuv74XZjVo3Hg7+vW7Bwg9T5577lF23HFnevcO\njzo/+eRzOf740xk79n2mT/+aiy++mtat23LEEcdy0UXHkZeXx9VX31zYjfCqq27m+usvYcOGAo49\n9tTCJFXv3n257bZrGDz4Adq23YPjjju9vP+aqoVUv4VUPv54FKNGvUHNmjXZYos63Hzz/ZgZ338/\ni0ceuYtwPuScccbF7LzzbgAMHHgj3bv3ZLfd2nPWWb259dY/MXLkcJo23YFbbglD2v6/vbsPtqsq\n7zj+/ZlIRA3iqIgC00SFKKDyEjQ2I/IiAVGTtgM2joBpnWZAEK2DNY4vo1BGpFMdO2ptGGPr+IJU\npRMVmyq+VQoxIQZIAtRUsQTsIIPiCxFMfPrHWRfPuZx7k3tvknNv+H5mzmSftdfea+07e/YDz15r\nnXnzXsbq1d/hrLNOYcaMfXn72zsjt/fbb3/OPvuNnHvuGQCcc8757Lff/rvvDzLFJVkKdP9G6fL2\n5nLIs4CfAZ9M8kLgRuDNwMnAXVV107BpJAcBXSsIsKWVjVS+N3tUxYmdMW3a9L7P8BUrPsycOUcy\nf/7JXH31p7nxxuuZPn06M2fux7Jlf/g95ZtvXsPTnnbgI6Yyf/zjl3PttV/hwQe3cuaZx/PKV57J\nkiVvGt68JpGJ3AszZz6JM89cwrnnnkESXvzi43nJS04Y7AVJ42OcGGb7drjggk6iZ9o0WLECNm2C\n972v8+tmX/4yXHhhJ1G0bVtntNGSJZ1jf/5zuOSSTtIJ4OKLO2VDXvMaOP30PX5JGqediRMjGen/\nRbdu3co733kev/vdQ2zf/nuOOWYeCxcu3lOXpEkq/dc6meBJk4OBbVX1f332za+q63Z0jr1tqoGk\nXeOZz5zYW8VVq3b+2XLqqaO3lWQucAMwv6pWJ/kw8BCdUUcLqur+JHcAc6vq3iRfBd5fVd9rx19L\nZ7hF+mUAAA0OSURBVBTSScCMqvrbVv5u4IGq+vuxX+HUYJyQtLtMpjih8dsVcWJvm5Km8bvrrkH3\nQJOJcWLn7ZYpaVW1pd/Dve3b4cNdkqaILcCWqlrdvn8BOAaYDdzUkkUHA+uSHNjqdw//OBi4e5Ty\nvZZxQpI0GuOEJA3e7lrDSJL2eu0/ZO9MMqcVnQysq6oDqmpWVc2ikww6ptVdCZzTfi1tHnB/Vf0U\nWAUsSPLkJE8GFrQySZIkSRqI3bWGkSQ9WrwJ+Ez7hbQfAX8xSt1rgNOBzcADQ3Wr6r4klwBtZQEu\nrqr7+p9CkiRJknY/E0aSNAFtMc65o+yf1bVdwPkj1FtBZyFPSZIkSRo4p6RJkiRJkiSphwkjSZIk\nSZIk9TBhJEmSJEmSpB4mjCRJkiRJktTDhJEkSZIkSZJ6mDCSJEmSJElSDxNGkiRJkiRJ6mHCSJIk\nSZIkST1MGEmSJEmSJKmHCSNJkiRJkqQ9LMlpSW5PsjnJslHqnZGkksxt3/dJ8skktyS5KckJXXW/\n3c65vn0OaOUzkny+tbU6yawd9W/6hK9QkqaQ5z9/0D2QJE1mxglJ0mh2VZxIMg34KHAKsAVYk2Rl\nVW0aVm8mcCGwuqv4rwCq6vktIfS1JMdV1e/b/tdV1dphTb4B+HlVPSfJYuADwJ+P1kdHGEmSJEmS\nJO1ZLwI2V9WPquoh4EpgUZ96lwCXA7/tKjscuBagqu4BfgHM3UF7i4B/adtfAE5OktEOMGEkSZIk\nSZK0iyVZmmRt12dp1+6DgDu7vm9pZd3HHw0cUlVfGXbqm4BFSaYnmQ0cCxzStf+TbTrau7uSQg+3\nV1XbgPuBp4zWf6ekSZIkSZIk7WJVtRxYPsLufqN76uGdyWOADwFL+tRbATwPWAv8BPgvYFvb97qq\nuqtNZfsicDbwqR21148jjCRJkqTdZLwLmrayd7Tjbk9y6p7psSRpD9lC76igg4G7u77PBI4Evp3k\nDmAesDLJ3KraVlV/XVVHVdUiYH/ghwBVdVf791fAZ+lMfetpL8l04EnAfaN10ISRJEmStBt0LWj6\nCjrrTbw2yeF96j1iQdNWbzFwBHAa8LF2PknS3mENcGiS2Un2ofPMXzm0s6rur6qnVtWsqpoF3AAs\nrKq1SR6f5AkASU4BtlXVpjZF7amt/LHAq4AN7ZQrgde37TOAb1aVI4wkSZKkAZjIgqaLgCur6sGq\n+jGwmT+8JZYkTXFtHaELgFXArcBVVbUxycVJFu7g8AOAdUluBd5OZ9oZwAxgVZKbgfXAXcAVbd8n\ngKck2Qy8FRhx1OsQ1zCSJEmSxqEtXtq9gOnytl7FkH4Lmr542DkeXtA0yUXDjr1h2LE9i6FKkqa2\nqroGuGZY2XtGqHtC1/YdwJw+dX5DZwHsfsf/FjhzLP0zYSRJkiSNww4WM4WJLWg65sVJJUnalUwY\nSZIkSbvHWBY0BTiQzoKmC3fiWEmSdivXMJIkSZJ2j3EvaNrqLU4yI8ls4FDg+3v+EiRJj1aOMJIk\nSZJ2g6ralmRoQdNpwIqhBU2BtVW1cpRjNya5CtgEbAPOr6rte6TjkiRhwkiSJEnabca7oGn7filw\n6W7rnCRJo3BKmiRJkiRJknqYMJIkSZIkSVIPE0aSJEmSJEnqYcJIkiRJkiRJPUwYSZIkSZIkqYcJ\nI0mSJEmSJPUwYSRJkiRJkqQeJowkSZIkSZLUw4SRJEmSJEmSepgwkiRJkiRJUg8TRpIkSZIkSeph\nwkiSJiDJHUluSbI+ydqu8jcluT3JxiSXd5W/I8nmtu/UrvLTWtnmJMv29HVIkiRJUrfpg+6AJO0F\nTqyqe4e+JDkRWAS8oKoeTHJAKz8cWAwcATwT+EaSw9phHwVOAbYAa5KsrKpNe/IiJEmSJGmICSNJ\n2vXOAy6rqgcBquqeVr4IuLKV/zjJZuBFbd/mqvoRQJIrW10TRpIkSZIGwilpkjSCJEuTrO36LO1T\nrYD/SHJj1/7DgJcmWZ3kO0mOa+UHAXd2HbullY1ULkmSJEkD4QgjSRpBVS0Hlu+g2vyqurtNO/t6\nktvoPFufDMwDjgOuSvIsIP2aoX/yvsbfc0mSJEmaGBNGkjQBVXV3+/eeJFfTmWK2BfhSVRXw/SS/\nB57ayg/pOvxg4O62PVK5JEmSJO1xTkmTpHFK8oQkM4e2gQXABuDfgJNa+WHAPsC9wEpgcZIZSWYD\nhwLfB9YAhyaZnWQfOgtjr9zT1yNJkiRJQxxhJEnj93Tg6iTQeZ5+tqr+vSV9ViTZADwEvL6NNtqY\n5Co6i1lvA86vqu0ASS4AVgHTgBVVtXHPX44kSZIkdZgwkqRxar9q9sI+5Q8BZ41wzKXApX3KrwGu\n2dV9lCRJkqTxcEqaJEmSJEmSepgwkiRJkiRJUg8TRpIkSZIkSeqRzjqsmqySLK2q5YPuhwbPe0FS\nPz4bNMR7QVI/Phs0xHtBY+UIo8lv6aA7oEnDe0FSPz4bNMR7QVI/Phs0xHtBY2LCSJIkSZIkST1M\nGEmSJEmSJKmHCaPJzzmmGuK9IKkfnw0a4r0gqR+fDRrivaAxcdFrSZIkSZIk9XCEkSRJkiRJknqY\nMJIkSZIkSVIPE0aTVJIVSe5JsmHQfdFgJTkkybeS3JpkY5I3D7pPkgbPOKEhxglJ/RgnBMYITYxr\nGE1SSY4Hfg18qqqOHHR/NDhJngE8o6rWJZkJ3Aj8SVVtGnDXJA2QcUJDjBOS+jFOCIwRmhhHGE1S\nVfVd4L5B90ODV1U/rap1bftXwK3AQYPtlaRBM05oiHFCUj/GCYExQhNjwkiaQpLMAo4GVg+2J5Kk\nycg4IUkaiTFCY2XCSJoikjwR+CLwlqr65aD7I0maXIwTkqSRGCM0HiaMpCkgyWPpPOA/U1VfGnR/\nJEmTi3FCkjQSY4TGy4SRNMklCfAJ4Naq+uCg+yNJmlyME5KkkRgjNBEmjCapJJ8DrgfmJNmS5A2D\n7pMGZj5wNnBSkvXtc/qgOyVpsIwT6mKckPQIxgk1xgiNW6pq0H2QJEmSJEnSJOIII0mSJEmSJPUw\nYSRJkiRJkqQeJowkSZIkSZLUw4SRJEmSJEmSepgwkiRJkiRJUg8TRuqRZHv7qcUNSf41yeMncK4T\nknylbS9MsmyUuvsneeM42nhvkotG2HdOu46NSTYN1Uvyz0nOGGtbkiTjhCRpdMYJae9hwkjDba2q\no6rqSOAh4NzunekY831TVSur6rJRquwPjPkBP5IkrwDeAiyoqiOAY4D7d9X5JelRzDghSRqNcULa\nS5gw0mj+E3hOkllJbk3yMWAdcEiSBUmuT7KuvTl4IkCS05LcluR7wJ8NnSjJkiQfadtPT3J1kpva\n54+By4Bnt7cRf9fqvS3JmiQ3J3lf17nemeT2JN8A5ozQ93cAF1XV3QBV9duqumJ4pSTvaW1sSLI8\nSVr5he0tws1JrmxlL2v9W5/kB0lmTvDvK0lTnXHCOCFJozFOGCc0hZkwUl9JpgOvAG5pRXOAT1XV\n0cBvgHcBL6+qY4C1wFuTPA64Ang18FLgwBFO/w/Ad6rqhXQy9RuBZcD/tLcRb0uyADgUeBFwFHBs\nkuOTHAssBo6mE0COG6GNI4Ebd+JSP1JVx7U3IPsCr2rly4Cjq+oF/OGtyEXA+VV1VLu+rTtxfkna\nKxknjBOSNBrjhHFCU58JIw23b5L1dB7a/wt8opX/pKpuaNvzgMOB61rd1wN/BDwX+HFV/bCqCvj0\nCG2cBPwjQFVtr6p+QzsXtM8P6LyFeC6dB/5Lgaur6oGq+iWwckJXCycmWZ3kltavI1r5zcBnkpwF\nbGtl1wEfTHIhsH9VbXvk6SRpr2ec6DBOSFJ/xokO44SmvOmD7oAmna0t4/2wNqryN91FwNer6rXD\n6h0F1C7qR4D3V9U/DWvjLTvZxkbgWOCbIzbQeYPxMWBuVd2Z5L3A49ruVwLHAwuBdyc5oqouS/JV\n4HTghiQvr6rbxnhdkjTVGSc6jBOS1J9xosM4oSnPEUYajxuA+UmeA5Dk8UkOA24DZid5dqv32hGO\nvxY4rx07Lcl+wK+A7jm8q4C/7JrLfFCSA4DvAn+aZN825/fVI7TxfuDyJAe242e0TH63oYf5va2d\nM1rdxwCHVNW3gL+hs4DeE5M8u6puqaoP0Hlj8tzR/kiS9ChmnDBOSNJojBPGCU0BjjDSmFXVz5Is\nAT6XZEYrfldV/XeSpcBXk9wLfI/O3N/h3gwsT/IGYDtwXlVdn+S6JBuAr7V5x88Drm9vJH4NnFVV\n65J8HlgP/ITOQnr9+nhNkqcD30jnBAWsGFbnF0muoDOv+g5gTds1Dfh0kifReTPxoVb3kiQntj5v\nAr42tr+cJD06GCeME5I0GuOEcUJTQzpTQyVJkiRJkqQOp6RJkiRJkiSphwkjSZIkSZIk9TBhJEmS\nJEmSpB4mjCRJkiRJktTDhJEkSZIkSZJ6mDCSJEmSJElSDxNGkiRJkiRJ6vH/wmi68lBuEKkAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2567c619c18>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we need to generate 9 numbers and the sum of numbers should be 1\n",
    "# one solution is to genarate 9 numbers and divide each of the numbers by their sum\n",
    "# ref: https://stackoverflow.com/a/18662466/4084039\n",
    "# we create a output array that has exactly same size as the CV data\n",
    "predicted_y = np.zeros((test_len,2))\n",
    "for i in range(test_len):\n",
    "    rand_probs = np.random.rand(1,2)\n",
    "    predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log loss on Test Data using Random Model\",log_loss(y_test, predicted_y, eps=1e-15))\n",
    "\n",
    "predicted_y =np.argmax(predicted_y, axis=1)\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YgY29g_qtASq"
   },
   "source": [
    "<h2> 4.4 Logistic Regression with hyperparameter tuning </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wb2tOE3GtASr",
    "outputId": "d7e4fc88-7d4e-4313-cda7-462a2409292e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 0.592800211149\n",
      "For values of alpha =  0.0001 The log loss is: 0.532351700629\n",
      "For values of alpha =  0.001 The log loss is: 0.527562275995\n",
      "For values of alpha =  0.01 The log loss is: 0.534535408885\n",
      "For values of alpha =  0.1 The log loss is: 0.525117052926\n",
      "For values of alpha =  1 The log loss is: 0.520035530431\n",
      "For values of alpha =  10 The log loss is: 0.521097925307\n"
     ]
    },
    {
     "data": {
      "image/png": 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X9VlJ4yqo4kngL5LeMLPDgZLq2nTOOedczYjmPghHAeOBzgRutQyApP7VFO0J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\neHXu3FmdOnXSCSecoOeee06SlJKSojZ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H6qxU5r4/l1dnvsrKPSvxqpfC+ELSv02nyRVN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\nY4wxxpTz//GxuV19gAz6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25667090eb8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  1 The train log loss is: 0.513842874233\n",
      "For values of best alpha =  1 The test log loss is: 0.520035530431\n",
      "Total number of data points : 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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GAN3NbDrQGTgrqvMMMBB4rGRaLyIi28vdhwBDElSxwjYrom4P4FV3z93uhomIiBRCPYBE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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x256793c19b0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 2)] # hyperparam for SGD classifier.\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "\n",
    "log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(X_train, y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, y_train)\n",
    "    predict_y = sig_clf.predict_proba(X_test)\n",
    "    log_error_array.append(log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_train)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(X_test)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predicted_y =np.argmax(predict_y,axis=1)\n",
    "print(\"Total number of data points :\", len(predicted_y))\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ouQSEnr3tASy"
   },
   "source": [
    "<h2> 4.5 Linear SVM with hyperparameter tuning </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AOFfZ5PLtAS0",
    "outputId": "d31eb598-e275-48cb-c49b-98e9eb76d8ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 0.657611721261\n",
      "For values of alpha =  0.0001 The log loss is: 0.489669093534\n",
      "For values of alpha =  0.001 The log loss is: 0.521829068562\n",
      "For values of alpha =  0.01 The log loss is: 0.566295616914\n",
      "For values of alpha =  0.1 The log loss is: 0.599957866217\n",
      "For values of alpha =  1 The log loss is: 0.635059427016\n",
      "For values of alpha =  10 The log loss is: 0.654159467907\n"
     ]
    },
    {
     "data": {
      "image/png": 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4sKakpGhoaKiu37Je98bs1U69OunoiaP1P5v/oy37tNSOozrq4PmDNWhIkFZv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\nu7Vedo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2568b154438>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.0001 The train log loss is: 0.478054677285\n",
      "For values of best alpha =  0.0001 The test log loss is: 0.489669093534\n",
      "Total number of data points : 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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dlhUrlvP114to0KAhkjjvvN+tqtevXw9atmydbZP+a22++ZYcdtjRTJo0btVw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\nqnH/Hz1BISHclkdCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2567423deb8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 2)] # hyperparam for SGD classifier.\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "\n",
    "log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l1', loss='hinge', random_state=42)\n",
    "    clf.fit(X_train, y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, y_train)\n",
    "    predict_y = sig_clf.predict_proba(X_test)\n",
    "    log_error_array.append(log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l1', loss='hinge', random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_train)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(X_test)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predicted_y =np.argmax(predict_y,axis=1)\n",
    "print(\"Total number of data points :\", len(predicted_y))\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZhTJgclztAS6"
   },
   "source": [
    "<h2> 4.6 XGBoost </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9U367-xetAS7",
    "outputId": "167e8588-2ac4-4c6d-ac22-f56a2fce5657"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-logloss:0.684819\tvalid-logloss:0.684845\n",
      "Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until valid-logloss hasn't improved in 20 rounds.\n",
      "[10]\ttrain-logloss:0.61583\tvalid-logloss:0.616104\n",
      "[20]\ttrain-logloss:0.564616\tvalid-logloss:0.565273\n",
      "[30]\ttrain-logloss:0.525758\tvalid-logloss:0.52679\n",
      "[40]\ttrain-logloss:0.496661\tvalid-logloss:0.498021\n",
      "[50]\ttrain-logloss:0.473563\tvalid-logloss:0.475182\n",
      "[60]\ttrain-logloss:0.455315\tvalid-logloss:0.457186\n",
      "[70]\ttrain-logloss:0.440442\tvalid-logloss:0.442482\n",
      "[80]\ttrain-logloss:0.428424\tvalid-logloss:0.430795\n",
      "[90]\ttrain-logloss:0.418803\tvalid-logloss:0.421447\n",
      "[100]\ttrain-logloss:0.41069\tvalid-logloss:0.413583\n",
      "[110]\ttrain-logloss:0.403831\tvalid-logloss:0.40693\n",
      "[120]\ttrain-logloss:0.398076\tvalid-logloss:0.401402\n",
      "[130]\ttrain-logloss:0.393305\tvalid-logloss:0.396851\n",
      "[140]\ttrain-logloss:0.38913\tvalid-logloss:0.392952\n",
      "[150]\ttrain-logloss:0.385469\tvalid-logloss:0.389521\n",
      "[160]\ttrain-logloss:0.382327\tvalid-logloss:0.386667\n",
      "[170]\ttrain-logloss:0.379541\tvalid-logloss:0.384148\n",
      "[180]\ttrain-logloss:0.377014\tvalid-logloss:0.381932\n",
      "[190]\ttrain-logloss:0.374687\tvalid-logloss:0.379883\n",
      "[200]\ttrain-logloss:0.372585\tvalid-logloss:0.378068\n",
      "[210]\ttrain-logloss:0.370615\tvalid-logloss:0.376367\n",
      "[220]\ttrain-logloss:0.368559\tvalid-logloss:0.374595\n",
      "[230]\ttrain-logloss:0.366545\tvalid-logloss:0.372847\n",
      "[240]\ttrain-logloss:0.364708\tvalid-logloss:0.371311\n",
      "[250]\ttrain-logloss:0.363021\tvalid-logloss:0.369886\n",
      "[260]\ttrain-logloss:0.36144\tvalid-logloss:0.368673\n",
      "[270]\ttrain-logloss:0.359899\tvalid-logloss:0.367421\n",
      "[280]\ttrain-logloss:0.358465\tvalid-logloss:0.366395\n",
      "[290]\ttrain-logloss:0.357128\tvalid-logloss:0.365361\n",
      "[300]\ttrain-logloss:0.355716\tvalid-logloss:0.364315\n",
      "[310]\ttrain-logloss:0.354425\tvalid-logloss:0.363403\n",
      "[320]\ttrain-logloss:0.353276\tvalid-logloss:0.362595\n",
      "[330]\ttrain-logloss:0.352084\tvalid-logloss:0.361823\n",
      "[340]\ttrain-logloss:0.351051\tvalid-logloss:0.361167\n",
      "[350]\ttrain-logloss:0.349867\tvalid-logloss:0.36043\n",
      "[360]\ttrain-logloss:0.348829\tvalid-logloss:0.359773\n",
      "[370]\ttrain-logloss:0.347689\tvalid-logloss:0.359019\n",
      "[380]\ttrain-logloss:0.346607\tvalid-logloss:0.358311\n",
      "[390]\ttrain-logloss:0.345568\tvalid-logloss:0.357674\n",
      "The test log loss is: 0.357054433715\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "params = {}\n",
    "params['objective'] = 'binary:logistic'\n",
    "params['eval_metric'] = 'logloss'\n",
    "params['eta'] = 0.02\n",
    "params['max_depth'] = 4\n",
    "\n",
    "d_train = xgb.DMatrix(X_train, label=y_train)\n",
    "d_test = xgb.DMatrix(X_test, label=y_test)\n",
    "\n",
    "watchlist = [(d_train, 'train'), (d_test, 'valid')]\n",
    "\n",
    "bst = xgb.train(params, d_train, 400, watchlist, early_stopping_rounds=20, verbose_eval=10)\n",
    "\n",
    "xgdmat = xgb.DMatrix(X_train,y_train)\n",
    "predict_y = bst.predict(d_test)\n",
    "print(\"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "param_grid={\n",
    "    'eta':list(range(10**-3,10)),\n",
    "    'max_depth':list(range(2,10))\n",
    "}\n",
    "xgb = XGBClassifier(objective='binary:logistic')\n",
    "rscv = RandomSearchCV(xgb, param_grid, scoring='neg_log_loss', n_jobs=-1, return_train_score=True)\n",
    "rscv.fit(X_train, y_train)\n",
    "print(\"Best ETA Value:\",rscv.best_params_['eta'])\n",
    "print(\"Best Max_Depth:\",rscv.best_params_['max_depth'])\n",
    "print(\"Best Log Loss Score: %.5f\"%(rscv.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6U5b17AatAS_",
    "outputId": "ca83b680-023b-4bc5-f499-8d8d85c2ff5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data points : 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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UABIRERERERERyXBKAImIiIiIiIiIZDglgEREREREREREMpwSQCIiIiIiIiIiGU4JIBER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\nLUzf5fCqDSwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2567e903cf8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_y =np.array(predict_y>0.5,dtype=int)\n",
    "print(\"Total number of data points :\", len(predicted_y))\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "4.ML_models.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}