Bamwani@gmail.com_20.3_Mean_W2V_and_ML_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MRP-fAQedMTd"
   },
   "source": [
    "<h2> 3.6 Featurizing text data with tfidf weighted word-vectors </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-3IbomL8dMTi",
    "outputId": "3fa8eb7c-ddf2-4f98-edee-0c49db6502e8"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import time\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import sys\n",
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "# exctract word2vec vectors\n",
    "# https://github.com/explosion/spaCy/issues/1721\n",
    "# http://landinghub.visualstudio.com/visual-cpp-build-tools\n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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.cross_validation 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": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j5XNgVyLdMT7"
   },
   "outputs": [],
   "source": [
    "# avoid decoding problems\n",
    "df = pd.read_csv(\"train.csv\")\n",
    " \n",
    "# encode questions to unicode\n",
    "# https://stackoverflow.com/a/6812069\n",
    "# ----------------- python 2 ---------------------\n",
    "# df['question1'] = df['question1'].apply(lambda x: unicode(str(x),\"utf-8\"))\n",
    "# df['question2'] = df['question2'].apply(lambda x: unicode(str(x),\"utf-8\"))\n",
    "# ----------------- python 3 ---------------------\n",
    "df['question1'] = df['question1'].apply(lambda x: str(x))\n",
    "df['question2'] = df['question2'].apply(lambda x: str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HbiMFpgRdMUJ",
    "outputId": "21c00698-7f2a-4ce4-e665-f7a2feaab6fa",
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a38GBlGWdMVQ"
   },
   "outputs": [],
   "source": [
    "#prepro_features_train.csv (Simple Preprocessing Feartures)\n",
    "#nlp_features_train.csv (NLP Features)\n",
    "if os.path.isfile('nlp_features_train.csv'):\n",
    "    dfnlp = pd.read_csv(\"nlp_features_train.csv\",encoding='latin-1')\n",
    "else:\n",
    "    print(\"download nlp_features_train.csv from drive or run previous notebook\")\n",
    "\n",
    "if os.path.isfile('df_fe_without_preprocessing_train.csv'):\n",
    "    dfppro = pd.read_csv(\"df_fe_without_preprocessing_train.csv\",encoding='latin-1')\n",
    "else:\n",
    "    print(\"download df_fe_without_preprocessing_train.csv from drive or run previous notebook\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
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      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>csc_min</th>\n",
       "      <th>csc_max</th>\n",
       "      <th>ctc_min</th>\n",
       "      <th>ctc_max</th>\n",
       "      <th>last_word_eq</th>\n",
       "      <th>first_word_eq</th>\n",
       "      <th>abs_len_diff</th>\n",
       "      <th>mean_len</th>\n",
       "      <th>token_set_ratio</th>\n",
       "      <th>token_sort_ratio</th>\n",
       "      <th>fuzz_ratio</th>\n",
       "      <th>fuzz_partial_ratio</th>\n",
       "      <th>longest_substr_ratio</th>\n",
       "    </tr>\n",
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       "      <td>0.999980</td>\n",
       "      <td>0.833319</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>0.916659</td>\n",
       "      <td>0.785709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.799984</td>\n",
       "      <td>0.399996</td>\n",
       "      <td>0.749981</td>\n",
       "      <td>0.599988</td>\n",
       "      <td>0.699993</td>\n",
       "      <td>0.466664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>86</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>0.596154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.399992</td>\n",
       "      <td>0.333328</td>\n",
       "      <td>0.399992</td>\n",
       "      <td>0.249997</td>\n",
       "      <td>0.399996</td>\n",
       "      <td>0.285712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2  is_duplicate   cwc_min   cwc_max   csc_min   csc_max  \\\n",
       "0   0     1     2             0  0.999980  0.833319  0.999983  0.999983   \n",
       "1   1     3     4             0  0.799984  0.399996  0.749981  0.599988   \n",
       "2   2     5     6             0  0.399992  0.333328  0.399992  0.249997   \n",
       "\n",
       "    ctc_min   ctc_max  last_word_eq  first_word_eq  abs_len_diff  mean_len  \\\n",
       "0  0.916659  0.785709           0.0            1.0           2.0      13.0   \n",
       "1  0.699993  0.466664           0.0            1.0           5.0      12.5   \n",
       "2  0.399996  0.285712           0.0            1.0           4.0      12.0   \n",
       "\n",
       "   token_set_ratio  token_sort_ratio  fuzz_ratio  fuzz_partial_ratio  \\\n",
       "0              100                93          93                 100   \n",
       "1               86                63          66                  75   \n",
       "2               66                66          54                  54   \n",
       "\n",
       "   longest_substr_ratio  \n",
       "0              0.982759  \n",
       "1              0.596154  \n",
       "2              0.166667  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnlp = dfnlp.drop([\"question1\",\"question2\"],axis=1)\n",
    "dfnlp.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>word_Common</th>\n",
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       "      <th>freq_q1+q2</th>\n",
       "      <th>freq_q1-q2</th>\n",
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       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2  is_duplicate  freq_qid1  freq_qid2  q1len  q2len  \\\n",
       "0   0     1     2             0          1          1     66     57   \n",
       "1   1     3     4             0          4          1     51     88   \n",
       "2   2     5     6             0          1          1     73     59   \n",
       "\n",
       "   q1_n_words  q2_n_words  word_Common  word_Total  word_share  freq_q1+q2  \\\n",
       "0          14          12         10.0        23.0    0.434783           2   \n",
       "1           8          13          4.0        20.0    0.200000           5   \n",
       "2          14          10          4.0        24.0    0.166667           2   \n",
       "\n",
       "   freq_q1-q2  \n",
       "0           0  \n",
       "1           3  \n",
       "2           0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfppro = dfppro.drop([\"question1\",\"question2\"],axis=1)\n",
    "dfppro.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged = dfnlp.merge(dfppro, on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_main = df.merge(df_merged, on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>qid1_x</th>\n",
       "      <th>qid2_x</th>\n",
       "      <th>is_duplicate_x</th>\n",
       "      <th>cwc_min</th>\n",
       "      <th>...</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
       "      <th>word_Common</th>\n",
       "      <th>word_Total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>freq_q1+q2</th>\n",
       "      <th>freq_q1-q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999980</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.799984</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.399992</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "\n",
       "                                           question2  is_duplicate  qid1_x  \\\n",
       "0  What is the step by step guide to invest in sh...             0       1   \n",
       "1  What would happen if the Indian government sto...             0       3   \n",
       "2  How can Internet speed be increased by hacking...             0       5   \n",
       "\n",
       "   qid2_x  is_duplicate_x   cwc_min  ...  freq_qid2  q1len  q2len  q1_n_words  \\\n",
       "0       2               0  0.999980  ...          1     66     57          14   \n",
       "1       4               0  0.799984  ...          1     51     88           8   \n",
       "2       6               0  0.399992  ...          1     73     59          14   \n",
       "\n",
       "   q2_n_words  word_Common  word_Total  word_share  freq_q1+q2  freq_q1-q2  \n",
       "0          12         10.0        23.0    0.434783           2           0  \n",
       "1          13          4.0        20.0    0.200000           5           3  \n",
       "2          10          4.0        24.0    0.166667           2           0  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 38)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_main.sample(100000)\n",
    "df_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 37)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(100000,)\n",
      "<class 'pandas.core.series.Series'>\n",
      "====================================================================================================\n",
      "After splitting\n",
      "(70000, 37) (70000,)\n",
      "(30000, 37) (30000,)\n"
     ]
    }
   ],
   "source": [
    "# merge texts\n",
    "\n",
    "#questions = list(df1['question1']) + list(df1['question2'])\n",
    "X = df_final.drop(\"is_duplicate\",axis=1)\n",
    "Y = df_final[\"is_duplicate\"]\n",
    "print(X.shape)\n",
    "print(type(X))\n",
    "print(Y.shape)\n",
    "print(type(Y))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=12,shuffle=True)\n",
    "#X_train,X_cv,Y_train,Y_cv=train_test_split(X,Y,test_size=0.2,random_state=12,shuffle=True)\n",
    "print('='*100)\n",
    "print(\"After splitting\")\n",
    "print(X_train.shape,Y_train.shape)\n",
    "#print(X_cv.shape,Y_cv.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions1_train = np.array(X_train['question1'].astype(\"unicode\")) \n",
    "questions2_train = np.array(X_train['question2'].astype(\"unicode\"))\n",
    "\n",
    "questions1_test = np.array(X_test['question1'].astype(\"unicode\")) \n",
    "questions2_test = np.array(X_test['question2'].astype(\"unicode\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions_train = questions1_train + questions2_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions_test = questions1_test + questions2_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "tfidf = TfidfVectorizer(lowercase=False,min_df=500, max_features=5000)\n",
    "X_train_tfidf = tfidf.fit_transform(np.array(X_train['question1'].astype(\"unicode\")) + np.array(X_train['question2'].astype(\"unicode\")))\n",
    "X_test_tfidf = tfidf.transform(np.array(X_test['question1'].astype(\"unicode\")) + np.array(X_test['question2'].astype(\"unicode\")))\n",
    "\n",
    "# dict key:word and value:tf-idf score\n",
    "word2tfidf = dict(zip(tfidf.get_feature_names(), tfidf.idf_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.drop([\"question1\",\"question2\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "X_train = X_train.drop([\"is_duplicate_x\",\"qid1_x\",\"qid2_x\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "X_train = X_train.drop([\"is_duplicate_y\",\"qid1_y\",\"qid2_y\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "#X_train = X_train.drop([\"id\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "X_train = X_train.drop([\"qid1\",\"qid2\"],axis=1)\n",
    "#print(type(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_test.drop([\"question1\",\"question2\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "X_test = X_test.drop([\"is_duplicate_x\",\"qid1_x\",\"qid2_x\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "X_test = X_test.drop([\"is_duplicate_y\",\"qid1_y\",\"qid2_y\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "#X_test = X_test.drop([\"id\"],axis=1)\n",
    "#print(type(X_train))\n",
    "\n",
    "X_test = X_test.drop([\"qid1\",\"qid2\"],axis=1)\n",
    "#print(type(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_idf = scipy.sparse.hstack([X_train.astype(float), X_train_tfidf.astype(float)], format = \"csr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_idf = scipy.sparse.hstack([X_test.astype(float), X_test_tfidf.astype(float)], format = \"csr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "2JKI2yT4dMUv"
   },
   "source": [
    "- After we find TF-IDF scores, we convert each question to a weighted average of word2vec vectors by these scores.\n",
    "- here we use a pre-trained GLOVE model which comes free with \"Spacy\".  https://spacy.io/usage/vectors-similarity\n",
    "- It is trained on Wikipedia and therefore, it is stronger in terms of word semantics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PFS6m8z5dMUz",
    "outputId": "3c4fb6fd-7f86-4955-8b8f-762b5969ecce",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eebee1c6e9c2496b803a623c7338c9fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=70000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# en_vectors_web_lg, which includes over 1 million unique vectors.\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "vecs1 = []\n",
    "# https://github.com/noamraph/tqdm\n",
    "# tqdm is used to print the progress bar\n",
    "for qu1 in tqdm(questions1_train):\n",
    "    doc1 = nlp(qu1) \n",
    "    # 384 is the number of dimensions of vectors \n",
    "    mean_vec1 = np.zeros([len(doc1), len(doc1[0].vector)])\n",
    "    for word1 in doc1:\n",
    "        # word2vec\n",
    "        vec1 = word1.vector\n",
    "        # fetch df score\n",
    "        try:\n",
    "            idf = word2tfidf[str(word1)]\n",
    "        except:\n",
    "            idf = 0\n",
    "        # compute final vec\n",
    "        mean_vec1 += vec1 * idf\n",
    "    mean_vec1 = mean_vec1.mean(axis=0)\n",
    "    vecs1.append(mean_vec1)\n",
    "X_train['q1_feats_m'] = list(vecs1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "62GEF-RbdMVB",
    "outputId": "60a4f5f8-5582-4886-befd-2ab6ed99c753",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2b326f8ed7f4c358cda35537114f7cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=70000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "vecs2 = []\n",
    "for qu2 in tqdm(questions2_train):\n",
    "    doc2 = nlp(qu2) \n",
    "    mean_vec2 = np.zeros([len(doc1), len(doc2[0].vector)])\n",
    "    for word2 in doc2:\n",
    "        # word2vec\n",
    "        vec2 = word2.vector\n",
    "        # fetch df score\n",
    "        try:\n",
    "            idf = word2tfidf[str(word2)]\n",
    "        except:\n",
    "            #print word\n",
    "            idf = 0\n",
    "        # compute final vec\n",
    "        mean_vec2 += vec2 * idf\n",
    "    mean_vec2 = mean_vec2.mean(axis=0)\n",
    "    vecs2.append(mean_vec2)\n",
    "X_train['q2_feats_m'] = list(vecs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PFS6m8z5dMUz",
    "outputId": "3c4fb6fd-7f86-4955-8b8f-762b5969ecce",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4343aef650714869800021790eb4252c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=30000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# en_vectors_web_lg, which includes over 1 million unique vectors.\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "vecs1 = []\n",
    "# https://github.com/noamraph/tqdm\n",
    "# tqdm is used to print the progress bar\n",
    "for qu1 in tqdm(questions1_test):\n",
    "    doc1 = nlp(qu1) \n",
    "    # 384 is the number of dimensions of vectors \n",
    "    mean_vec1 = np.zeros([len(doc1), len(doc1[0].vector)])\n",
    "    for word1 in doc1:\n",
    "        # word2vec\n",
    "        vec1 = word1.vector\n",
    "        # fetch df score\n",
    "        try:\n",
    "            idf = word2tfidf[str(word1)]\n",
    "        except:\n",
    "            idf = 0\n",
    "        # compute final vec\n",
    "        mean_vec1 += vec1 * idf\n",
    "    mean_vec1 = mean_vec1.mean(axis=0)\n",
    "    vecs1.append(mean_vec1)\n",
    "X_test['q1_feats_t'] = list(vecs1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "62GEF-RbdMVB",
    "outputId": "60a4f5f8-5582-4886-befd-2ab6ed99c753",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e400b3eb7bae41f8a0ade86b61125b63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=30000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "vecs2 = []\n",
    "for qu2 in tqdm(questions2_test):\n",
    "    doc2 = nlp(qu2) \n",
    "    mean_vec2 = np.zeros([len(doc1), len(doc2[0].vector)])\n",
    "    for word2 in doc2:\n",
    "        # word2vec\n",
    "        vec2 = word2.vector\n",
    "        # fetch df score\n",
    "        try:\n",
    "            idf = word2tfidf[str(word2)]\n",
    "        except:\n",
    "            #print word\n",
    "            idf = 0\n",
    "        # compute final vec\n",
    "        mean_vec2 += vec2 * idf\n",
    "    mean_vec2 = mean_vec2.mean(axis=0)\n",
    "    vecs2.append(mean_vec2)\n",
    "X_test['q2_feats_t'] = list(vecs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finalizing DataFrame (Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_q1 = pd.DataFrame(X_train.q1_feats_m.values.tolist(), index= X_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_q2 = pd.DataFrame(X_train.q2_feats_m.values.tolist(), index= X_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_q1['id']=X_train['id']\n",
    "df3_q2['id']=X_train['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_train = X_train.merge(df3_q1,on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_train = df_final_train.merge(df3_q2, on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_train = df_final_train.drop([\"q1_feats_m\",\"q2_feats_m\",\"id\"],axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cwc_min</th>\n",
       "      <th>cwc_max</th>\n",
       "      <th>csc_min</th>\n",
       "      <th>csc_max</th>\n",
       "      <th>ctc_min</th>\n",
       "      <th>ctc_max</th>\n",
       "      <th>last_word_eq</th>\n",
       "      <th>first_word_eq</th>\n",
       "      <th>abs_len_diff</th>\n",
       "      <th>mean_len</th>\n",
       "      <th>...</th>\n",
       "      <th>86_y</th>\n",
       "      <th>87_y</th>\n",
       "      <th>88_y</th>\n",
       "      <th>89_y</th>\n",
       "      <th>90_y</th>\n",
       "      <th>91_y</th>\n",
       "      <th>92_y</th>\n",
       "      <th>93_y</th>\n",
       "      <th>94_y</th>\n",
       "      <th>95_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999950</td>\n",
       "      <td>0.666644</td>\n",
       "      <td>0.999975</td>\n",
       "      <td>0.799984</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>0.749991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.910008</td>\n",
       "      <td>-4.046771</td>\n",
       "      <td>-7.272167</td>\n",
       "      <td>28.026936</td>\n",
       "      <td>3.827360</td>\n",
       "      <td>19.044288</td>\n",
       "      <td>2.028453</td>\n",
       "      <td>-7.314273</td>\n",
       "      <td>-3.565585</td>\n",
       "      <td>9.330807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.874989</td>\n",
       "      <td>0.874989</td>\n",
       "      <td>0.999980</td>\n",
       "      <td>0.999980</td>\n",
       "      <td>0.923070</td>\n",
       "      <td>0.923070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56.480804</td>\n",
       "      <td>-24.346811</td>\n",
       "      <td>3.303107</td>\n",
       "      <td>49.851028</td>\n",
       "      <td>50.125510</td>\n",
       "      <td>21.113664</td>\n",
       "      <td>19.891602</td>\n",
       "      <td>13.075670</td>\n",
       "      <td>13.607688</td>\n",
       "      <td>-18.577727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.666644</td>\n",
       "      <td>0.499988</td>\n",
       "      <td>0.199996</td>\n",
       "      <td>0.199996</td>\n",
       "      <td>0.374995</td>\n",
       "      <td>0.333330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>16.839105</td>\n",
       "      <td>-14.021545</td>\n",
       "      <td>-4.133141</td>\n",
       "      <td>29.829819</td>\n",
       "      <td>2.070338</td>\n",
       "      <td>15.802740</td>\n",
       "      <td>14.680832</td>\n",
       "      <td>10.105310</td>\n",
       "      <td>-14.677738</td>\n",
       "      <td>12.302985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 218 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cwc_min   cwc_max   csc_min   csc_max   ctc_min   ctc_max  last_word_eq  \\\n",
       "0  0.999950  0.666644  0.999975  0.799984  0.999983  0.749991           0.0   \n",
       "1  0.874989  0.874989  0.999980  0.999980  0.923070  0.923070           0.0   \n",
       "2  0.666644  0.499988  0.199996  0.199996  0.374995  0.333330           0.0   \n",
       "\n",
       "   first_word_eq  abs_len_diff  mean_len  ...       86_y       87_y      88_y  \\\n",
       "0            1.0           2.0       7.0  ...  -2.910008  -4.046771 -7.272167   \n",
       "1            1.0           0.0      13.0  ...  56.480804 -24.346811  3.303107   \n",
       "2            0.0           1.0       8.5  ...  16.839105 -14.021545 -4.133141   \n",
       "\n",
       "        89_y       90_y       91_y       92_y       93_y       94_y       95_y  \n",
       "0  28.026936   3.827360  19.044288   2.028453  -7.314273  -3.565585   9.330807  \n",
       "1  49.851028  50.125510  21.113664  19.891602  13.075670  13.607688 -18.577727  \n",
       "2  29.829819   2.070338  15.802740  14.680832  10.105310 -14.677738  12.302985  \n",
       "\n",
       "[3 rows x 218 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finalizing DataFrame (Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_q1 = pd.DataFrame(X_test.q1_feats_t.values.tolist(), index= X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_q2 = pd.DataFrame(X_test.q2_feats_t.values.tolist(), index= X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_q1['id']=X_test['id']\n",
    "df4_q2['id']=X_test['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_test = X_test.merge(df4_q1,on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_test = df_final_test.merge(df4_q2, on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_final_test = df_final_test.drop([\"q1_feats_t\",\"q2_feats_t\",\"id\"],axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cwc_min</th>\n",
       "      <th>cwc_max</th>\n",
       "      <th>csc_min</th>\n",
       "      <th>csc_max</th>\n",
       "      <th>ctc_min</th>\n",
       "      <th>ctc_max</th>\n",
       "      <th>last_word_eq</th>\n",
       "      <th>first_word_eq</th>\n",
       "      <th>abs_len_diff</th>\n",
       "      <th>mean_len</th>\n",
       "      <th>...</th>\n",
       "      <th>86_y</th>\n",
       "      <th>87_y</th>\n",
       "      <th>88_y</th>\n",
       "      <th>89_y</th>\n",
       "      <th>90_y</th>\n",
       "      <th>91_y</th>\n",
       "      <th>92_y</th>\n",
       "      <th>93_y</th>\n",
       "      <th>94_y</th>\n",
       "      <th>95_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.499988</td>\n",
       "      <td>0.249997</td>\n",
       "      <td>0.571420</td>\n",
       "      <td>0.333331</td>\n",
       "      <td>0.545450</td>\n",
       "      <td>0.230768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.705267</td>\n",
       "      <td>-4.366542</td>\n",
       "      <td>-10.356503</td>\n",
       "      <td>26.469776</td>\n",
       "      <td>-10.572244</td>\n",
       "      <td>21.491007</td>\n",
       "      <td>14.787162</td>\n",
       "      <td>-4.753901</td>\n",
       "      <td>-2.725673</td>\n",
       "      <td>-0.893614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333328</td>\n",
       "      <td>0.199998</td>\n",
       "      <td>0.199996</td>\n",
       "      <td>0.099999</td>\n",
       "      <td>0.272725</td>\n",
       "      <td>0.130434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.510744</td>\n",
       "      <td>-3.795767</td>\n",
       "      <td>-6.977510</td>\n",
       "      <td>6.598917</td>\n",
       "      <td>12.864263</td>\n",
       "      <td>40.352769</td>\n",
       "      <td>0.504109</td>\n",
       "      <td>9.255565</td>\n",
       "      <td>-27.041372</td>\n",
       "      <td>-15.159643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.399992</td>\n",
       "      <td>0.249997</td>\n",
       "      <td>0.333322</td>\n",
       "      <td>0.249994</td>\n",
       "      <td>0.299997</td>\n",
       "      <td>0.272725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>43.811498</td>\n",
       "      <td>-3.633122</td>\n",
       "      <td>16.050691</td>\n",
       "      <td>8.762669</td>\n",
       "      <td>-11.210980</td>\n",
       "      <td>38.347708</td>\n",
       "      <td>7.157411</td>\n",
       "      <td>-2.878363</td>\n",
       "      <td>0.312637</td>\n",
       "      <td>18.778862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 218 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cwc_min   cwc_max   csc_min   csc_max   ctc_min   ctc_max  last_word_eq  \\\n",
       "0  0.499988  0.249997  0.571420  0.333331  0.545450  0.230768           0.0   \n",
       "1  0.333328  0.199998  0.199996  0.099999  0.272725  0.130434           0.0   \n",
       "2  0.399992  0.249997  0.333322  0.249994  0.299997  0.272725           0.0   \n",
       "\n",
       "   first_word_eq  abs_len_diff  mean_len  ...       86_y      87_y       88_y  \\\n",
       "0            1.0          15.0      18.5  ...  32.705267 -4.366542 -10.356503   \n",
       "1            0.0          12.0      17.0  ...  10.510744 -3.795767  -6.977510   \n",
       "2            1.0           1.0      10.5  ...  43.811498 -3.633122  16.050691   \n",
       "\n",
       "        89_y       90_y       91_y       92_y      93_y       94_y       95_y  \n",
       "0  26.469776 -10.572244  21.491007  14.787162 -4.753901  -2.725673  -0.893614  \n",
       "1   6.598917  12.864263  40.352769   0.504109  9.255565 -27.041372 -15.159643  \n",
       "2   8.762669 -11.210980  38.347708   7.157411 -2.878363   0.312637  18.778862  \n",
       "\n",
       "[3 rows x 218 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_test.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standardizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "df_final_train = normalize(df_final_train)\n",
    "df_final_test = normalize(df_final_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZihvUPvHtARd"
   },
   "source": [
    "<h1>4. Machine Learning Models </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1Iw9zCHqtASS",
    "outputId": "910b684b-0876-4dd8-e0d9-457846236833",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data : (70000, 218)\n",
      "Number of data points in test data : (30000, 218)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of data points in train data :\",df_final_train.shape)\n",
    "print(\"Number of data points in test data :\",df_final_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0oDV15LJtASY",
    "outputId": "70a1e4eb-3f31-4f1e-a53b-ad972978505d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Distribution of output variable in train data ----------\n",
      "Class 0:  0.6313571428571428 Class 1:  0.36864285714285716\n",
      "---------- Distribution of output variable in train data ----------\n",
      "Class 0:  0.3701333333333333 Class 1:  0.3701333333333333\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": 44,
   "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": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qwMDqcU7tASl",
    "outputId": "c1e90d53-25ec-445b-e33a-299538520e32",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on Test Data using Random Model 0.8832758549873778\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 46,
   "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-07 The log loss is: 0.5054425290671226\n",
      "For values of alpha =  1e-06 The log loss is: 0.49946386008993626\n",
      "For values of alpha =  1e-05 The log loss is: 0.49377039600644523\n",
      "For values of alpha =  0.0001 The log loss is: 0.5252172804491245\n",
      "For values of alpha =  0.001 The log loss is: 0.5663377971848111\n",
      "For values of alpha =  0.01 The log loss is: 0.5987329044468662\n",
      "For values of alpha =  0.1 The log loss is: 0.6088930036000808\n",
      "For values of alpha =  1 The log loss is: 0.6140179355827127\n",
      "For values of alpha =  10 The log loss is: 0.6215235701466523\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  1e-05 The train log loss is: 0.4937056846033866\n",
      "For values of best alpha =  1e-05 The test log loss is: 0.49377039600644523\n",
      "Total number of data points : 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-7, 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,class_weight=\"balanced\")\n",
    "    clf.fit(df_final_train, Y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(df_final_train, Y_train)\n",
    "    predict_y = sig_clf.predict_proba(df_final_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_LR = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha_LR], penalty='l2', loss='log', random_state=42, class_weight=\"balanced\")\n",
    "clf.fit(df_final_train, Y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(df_final_train, Y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(df_final_train)\n",
    "print('For values of best alpha = ', alpha[best_alpha_LR], \"The train log loss is:\",log_loss(Y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(df_final_test)\n",
    "print('For values of best alpha = ', alpha[best_alpha_LR], \"The test log loss is:\",log_loss(Y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "log_loss_LR = log_loss(Y_test,predict_y)\n",
    "predicted_y =np.argmax(predict_y,axis=1)\n",
    "\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": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AOFfZ5PLtAS0",
    "outputId": "d31eb598-e275-48cb-c49b-98e9eb76d8ba",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-07 The log loss is: 0.4845353883209408\n",
      "For values of alpha =  1e-06 The log loss is: 0.46940470096532055\n",
      "For values of alpha =  1e-05 The log loss is: 0.46165431636051774\n",
      "For values of alpha =  0.0001 The log loss is: 0.49614060178915353\n",
      "For values of alpha =  0.001 The log loss is: 0.5514125047057531\n",
      "For values of alpha =  0.01 The log loss is: 0.637116283229448\n",
      "For values of alpha =  0.1 The log loss is: 0.6590313745273192\n",
      "For values of alpha =  1 The log loss is: 0.659031374527318\n",
      "For values of alpha =  10 The log loss is: 0.6590313745273185\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  1e-05 The train log loss is: 0.46144235898196817\n",
      "For values of best alpha =  1e-05 The test log loss is: 0.46165431636051774\n",
      "Total number of data points : 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-7, 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,class_weight=\"balanced\")\n",
    "    clf.fit(df_final_train, Y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(df_final_train, Y_train)\n",
    "    predict_y = sig_clf.predict_proba(df_final_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_svm = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha_svm], penalty='l1', loss='hinge', random_state=42,class_weight=\"balanced\")\n",
    "clf.fit(df_final_train, Y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(df_final_train, Y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(df_final_train)\n",
    "print('For values of best alpha = ', alpha[best_alpha_svm], \"The train log loss is:\",log_loss(Y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(df_final_test)\n",
    "print('For values of best alpha = ', alpha[best_alpha_svm], \"The test log loss is:\",log_loss(Y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "log_loss_svm = log_loss(Y_test,predict_y)\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": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9U367-xetAS7",
    "outputId": "167e8588-2ac4-4c6d-ac22-f56a2fce5657"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "          estimator=XGBClassifier(base_score=0.5, booster='gbtree', class_weight='balanced',\n",
       "       colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=15, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
       "       subsample=1),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=-1,\n",
       "          param_distributions={'max_depth': [3, 4, 5, 6, 8, 9, 10, 15], 'n_estimators': [80, 100, 150, 200, 300, 500]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "depth=[3,4,5,6,8,9,10,15]\n",
    "n_estimators=[80,100,150,200,300,500]\n",
    "parameters = {\"max_depth\":depth,\"n_estimators\":n_estimators}\n",
    "\n",
    "XGB=XGBClassifier(random_state=15,class_weight=\"balanced\",objective ='binary:logistic')\n",
    "\n",
    "rsc = RandomizedSearchCV(XGB,param_distributions=parameters,n_jobs=-1)\n",
    "rsc.fit(df_final_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', class_weight='balanced',\n",
       "       colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=9, min_child_weight=1, missing=None,\n",
       "       n_estimators=500, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=15, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_max_depth = rsc.cv_results_[\"param_max_depth\"]\n",
    "param_n_estimators = rsc.cv_results_[\"param_n_estimators\"]\n",
    "mean_test_score = rsc.cv_results_[\"mean_test_score\"]\n",
    "mean_train_score = rsc.cv_results_[\"mean_train_score\"]\n",
    "    \n",
    "df_train = pd.DataFrame({\"param_max_depth\": param_max_depth,\n",
    "                        \"param_n_estimators\" : param_n_estimators,\n",
    "                        \"mean_train_score\" : mean_train_score\n",
    "                            })\n",
    "df_test = pd.DataFrame({\"param_max_depth\": param_max_depth,\n",
    "                            \"param_n_estimators\" : param_n_estimators,\n",
    "                             \"mean_test_score\" : mean_test_score\n",
    "                            })\n",
    "df_train = df_train.pivot(index=\"param_max_depth\", columns=\"param_n_estimators\",values=\"mean_train_score\")\n",
    "df_test = df_test.pivot(index=\"param_max_depth\", columns=\"param_n_estimators\", values=\"mean_test_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best HyperParameter:  {'n_estimators': 500, 'max_depth': 9}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "print(\"Best HyperParameter: \",rsc.best_params_)\n",
    "#print(GSC.best_score_)\n",
    "#testscores = rsc.cv_results_['mean_test_score'].reshape(len(n_estimators),len(depth))\n",
    "\n",
    "plt.figure(figsize=[len(n_estimators),len(depth)])\n",
    "sns.heatmap(df_test, annot=True, cmap=plt.cm.coolwarm, fmt=\".3f\", xticklabels=n_estimators, yticklabels=depth)\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('max_depth')\n",
    "plt.xticks(np.arange(len(n_estimators)), n_estimators)\n",
    "plt.yticks(np.arange(len(depth)), depth)\n",
    "plt.title('Random Search f1 Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Observation: </b>\n",
    "1. RandomSearchCV is fasster than GridSearchCV as it randomly choses parameters to calculate instead of all possible points like GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=9, min_child_weight=1, missing=None, n_estimators=235,\n",
       "       n_jobs=-1, nthread=None, objective='binary:logistic',\n",
       "       random_state=15, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=1)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_model = XGBClassifier(max_depth=9,n_estimators=235,random_state=15,objective ='binary:logistic',n_jobs=-1,learning_rate=0.1)\n",
    "optimal_model.fit(df_final_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = optimal_model.predict_proba(df_final_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method XGBModel.get_xgb_params of XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=9, min_child_weight=1, missing=None, n_estimators=235,\n",
       "       n_jobs=-1, nthread=None, objective='binary:logistic',\n",
       "       random_state=15, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=1)>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_model.get_xgb_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3426542846162706\n"
     ]
    }
   ],
   "source": [
    "log_loss_xgb = log_loss(Y_test,prediction)\n",
    "best_depth = 9\n",
    "best_n_estimators = 235\n",
    "print(log_loss_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train log loss: 0.10475364433671278\n",
      "Test log loss: 0.3426542846162706\n"
     ]
    }
   ],
   "source": [
    "print(\"Train log loss:\", log_loss(Y_train,optimal_model.predict_proba(df_final_train))) \n",
    "print(\"Test log loss:\",log_loss(Y_test,prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Observation: </b>\n",
    "1. The low difference between Train Log loss and Test Log loss concludes that our model is not overfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6U5b17AatAS_",
    "outputId": "ca83b680-023b-4bc5-f499-8d8d85c2ff5e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data points : 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_y =np.argmax(prediction>0.5,axis=1)\n",
    "print(\"Total number of data points :\", len(predicted_y))\n",
    "plot_confusion_matrix(Y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### <b> Observation: </b>\n",
    "1. 85% of points predicted as \"is duplicate\" are correctly classified\n",
    "2. 79% of points predicted as \"not duplicate\" are correctly classified"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WmiIgHOJtATF"
   },
   "source": [
    "<h1> 5. Conclusion </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab_type": "text",
    "id": "CWS6JoB0tATF",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------+------------+-------+-------+----------------------+---------------------+\n",
      "|        Model        | Vectorizer | alpha | Depth | No. of base learners |       Log Loss      |\n",
      "+---------------------+------------+-------+-------+----------------------+---------------------+\n",
      "| Logistic Regression | TFIDF W2V  |   2   |   -   |          -           | 0.49377039600644523 |\n",
      "|         SVM         | TFIDF W2V  |   2   |   -   |          -           | 0.46165431636051774 |\n",
      "|       XGBoost       | TFIDF W2V  |   -   |   9   |         235          |  0.3426542846162706 |\n",
      "+---------------------+------------+-------+-------+----------------------+---------------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "comparison = PrettyTable()\n",
    "comparison.field_names = [\"Model\",\"Vectorizer\",\"alpha\", \"Depth\", \"No. of base learners\", \"Log Loss\"]\n",
    "comparison.add_row([\"Logistic Regression\",\"TFIDF W2V\", best_alpha_LR, \"-\" , \"-\" ,log_loss_LR])\n",
    "comparison.add_row([\"SVM\",\"TFIDF W2V\", best_alpha_svm, \"-\" , \"-\", log_loss_svm])\n",
    "comparison.add_row([\"XGBoost\",\"TFIDF W2V\", \"-\", best_depth, best_n_estimators, log_loss_xgb])\n",
    "\n",
    "print(comparison)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Conclusion: </b>\n",
    "1. With reference to the Comparison Report above, XGBoost performs well with log loss of 0.34. Which is much better than the Random Model(worst case) with log loss of 0.88 \n",
    "\n",
    "<b> Steps followed to solve this problem: </b>\n",
    "1. Understandng Business Problem and Converting it into Machine Learning Problem.\n",
    "2. Understanding the data. Performing EDA. Chosing the right Metric for the problem.\n",
    "3. Feature Engineering. As this is a text data. looking up various possible feature engineering ways.\n",
    "3. Cleaning the data, preprocessing it such that its ready for ML Models.\n",
    "4. Getting the worst case scenario result to have a comparison point.\n",
    "5. Comparing Different Models' performance  and choosing the best one.\n",
    "6. Making sure there are no errors like overfit/underfit or data leakage etc. in the complete solution.\n",
    "   "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "3.Q_Mean_W2V.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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}