notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U5Plrr2NU93P"
   },
   "source": [
    "<img src='images/pic1.jpg'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from wordcloud import WordCloud               \n",
    "import re\n",
    "import os\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "from sqlalchemy import create_engine # database connection\n",
    "import datetime as dt\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score,precision_score,recall_score\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from skmultilearn.adapt import mlknn\n",
    "\n",
    "from skmultilearn.problem_transform import ClassifierChain\n",
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from skmultilearn.problem_transform import LabelPowerset\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from datetime import datetime\n",
    "import pickle\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ihs1Hb3RU93S"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rAZDkLNPU93Y"
   },
   "source": [
    "# Stack Overflow: Tag Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TpVk_usWU93a"
   },
   "source": [
    "<h1>1. Business Problem </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5-1WlUboU93c"
   },
   "source": [
    "<h2> 1.1 Description </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "37SdsKLdU93e"
   },
   "source": [
    "<p style='font-size:18px'><b> Description </b></p>\n",
    "<p>\n",
    "Stack Overflow is the largest, most trusted online community for developers to learn, share their programming knowledge, and build their careers.<br />\n",
    "<br />\n",
    "Stack Overflow is something which every programmer use one way or another. Each month, over 50 million developers come to Stack Overflow to learn, share their knowledge, and build their careers. It features questions and answers on a wide range of topics in computer programming. The website serves as a platform for users to ask and answer questions, and, through membership and active participation, to vote questions and answers up or down and edit questions and answers in a fashion similar to a wiki or Digg. As of April 2014 Stack Overflow has over 4,000,000 registered users, and it exceeded 10,000,000 questions in late August 2015. Based on the type of tags assigned to questions, the top eight most discussed topics on the site are: Java, JavaScript, C#, PHP, Android, jQuery, Python and HTML.<br />\n",
    "<br />\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9brAMjUsU93f"
   },
   "source": [
    "<p style='font-size:18px'><b> Problem Statemtent </b></p>\n",
    "Suggest the tags based on the content that was there in the question posted on Stackoverflow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hbXm8lAuU93p"
   },
   "source": [
    "<h2> 1.2 Real World / Business Objectives and Constraints </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "88TwKPItU93q"
   },
   "source": [
    "1. Predict as many tags as possible with high precision and recall.\n",
    "2. Incorrect tags could impact customer experience on StackOverflow.\n",
    "3. No strict latency constraints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lE0wX1roU93s"
   },
   "source": [
    "<h1>2. Machine Learning problem </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ynAszp_uU93u"
   },
   "source": [
    "<h2> 2.1 Data </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "elwijxMGU93w"
   },
   "source": [
    "<h3> 2.1.1 Data Overview </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mdFiIj7_U93x"
   },
   "source": [
    "Refer: https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data\n",
    "<br>\n",
    "All of the data is in 2 files: Train and Test.<br />\n",
    "<pre>\n",
    "<b>Train.csv</b> contains 4 columns: Id,Title,Body,Tags.<br />\n",
    "<b>Test.csv</b> contains the same columns but without the Tags, which you are to predict.<br />\n",
    "<b>Size of Train.csv</b> - 6.75GB<br />\n",
    "<b>Size of Test.csv</b> - 2GB<br />\n",
    "<b>Number of rows in Train.csv</b> = 6034195<br />\n",
    "</pre>\n",
    "The questions are randomized and contains a mix of verbose text sites as well as sites related to math and programming. The number of questions from each site may vary, and no filtering has been performed on the questions (such as closed questions).<br />\n",
    "<br />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ji0A66hWU93z"
   },
   "source": [
    "__Data Field Explaination__\n",
    "\n",
    "Dataset contains 6,034,195 rows. The columns in the table are:<br />\n",
    "<pre>\n",
    "<b>Id</b> - Unique identifier for each question<br />\n",
    "<b>Title</b> - The question's title<br />\n",
    "<b>Body</b> - The body of the question<br />\n",
    "<b>Tags</b> - The tags associated with the question in a space-seperated format (all lowercase, should not contain tabs '\\t' or ampersands '&')<br />\n",
    "</pre>\n",
    "\n",
    "<br />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WNDiy42GU931"
   },
   "source": [
    "<h3>2.1.2 Example Data point </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D5IcxRbYU932"
   },
   "source": [
    "<pre>\n",
    "<b>Title</b>:  Implementing Boundary Value Analysis of Software Testing in a C++ program?\n",
    "<b>Body </b>: <pre><code>\n",
    "        #include&lt;\n",
    "        iostream&gt;\\n\n",
    "        #include&lt;\n",
    "        stdlib.h&gt;\\n\\n\n",
    "        using namespace std;\\n\\n\n",
    "        int main()\\n\n",
    "        {\\n\n",
    "                 int n,a[n],x,c,u[n],m[n],e[n][4];\\n         \n",
    "                 cout&lt;&lt;\"Enter the number of variables\";\\n         cin&gt;&gt;n;\\n\\n         \n",
    "                 cout&lt;&lt;\"Enter the Lower, and Upper Limits of the variables\";\\n         \n",
    "                 for(int y=1; y&lt;n+1; y++)\\n         \n",
    "                 {\\n                 \n",
    "                    cin&gt;&gt;m[y];\\n                 \n",
    "                    cin&gt;&gt;u[y];\\n         \n",
    "                 }\\n         \n",
    "                 for(x=1; x&lt;n+1; x++)\\n         \n",
    "                 {\\n                 \n",
    "                    a[x] = (m[x] + u[x])/2;\\n         \n",
    "                 }\\n         \n",
    "                 c=(n*4)-4;\\n         \n",
    "                 for(int a1=1; a1&lt;n+1; a1++)\\n         \n",
    "                 {\\n\\n             \n",
    "                    e[a1][0] = m[a1];\\n             \n",
    "                    e[a1][1] = m[a1]+1;\\n             \n",
    "                    e[a1][2] = u[a1]-1;\\n             \n",
    "                    e[a1][3] = u[a1];\\n         \n",
    "                 }\\n         \n",
    "                 for(int i=1; i&lt;n+1; i++)\\n         \n",
    "                 {\\n            \n",
    "                    for(int l=1; l&lt;=i; l++)\\n            \n",
    "                    {\\n                 \n",
    "                        if(l!=1)\\n                 \n",
    "                        {\\n                    \n",
    "                            cout&lt;&lt;a[l]&lt;&lt;\"\\\\t\";\\n                 \n",
    "                        }\\n            \n",
    "                    }\\n            \n",
    "                    for(int j=0; j&lt;4; j++)\\n            \n",
    "                    {\\n                \n",
    "                        cout&lt;&lt;e[i][j];\\n                \n",
    "                        for(int k=0; k&lt;n-(i+1); k++)\\n                \n",
    "                        {\\n                    \n",
    "                            cout&lt;&lt;a[k]&lt;&lt;\"\\\\t\";\\n               \n",
    "                        }\\n                \n",
    "                        cout&lt;&lt;\"\\\\n\";\\n            \n",
    "                    }\\n        \n",
    "                 }    \\n\\n        \n",
    "                 system(\"PAUSE\");\\n        \n",
    "                 return 0;    \\n\n",
    "        }\\n\n",
    "        </code></pre>\\n\\n\n",
    "        <p>The answer should come in the form of a table like</p>\\n\\n\n",
    "        <pre><code>       \n",
    "        1            50              50\\n       \n",
    "        2            50              50\\n       \n",
    "        99           50              50\\n       \n",
    "        100          50              50\\n       \n",
    "        50           1               50\\n       \n",
    "        50           2               50\\n       \n",
    "        50           99              50\\n       \n",
    "        50           100             50\\n       \n",
    "        50           50              1\\n       \n",
    "        50           50              2\\n       \n",
    "        50           50              99\\n       \n",
    "        50           50              100\\n\n",
    "        </code></pre>\\n\\n\n",
    "        <p>if the no of inputs is 3 and their ranges are\\n\n",
    "        1,100\\n\n",
    "        1,100\\n\n",
    "        1,100\\n\n",
    "        (could be varied too)</p>\\n\\n\n",
    "        <p>The output is not coming,can anyone correct the code or tell me what\\'s wrong?</p>\\n'\n",
    "<b>Tags </b>: 'c++ c'\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xqsVRjSmU94C"
   },
   "source": [
    "<h1> 3. Exploratory Data Analysis </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "azda-BmRU94H"
   },
   "source": [
    "<h2> 3.1 Data Loading and Cleaning </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "62GDC_VjU94J"
   },
   "source": [
    "<h3>3.1.1 Using Pandas with SQLite to Load the data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "archive = zipfile.ZipFile('Train.zip', 'r')\n",
    "csvfile = archive.open('Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "289tS71cU94L"
   },
   "outputs": [],
   "source": [
    "#Creating db file from csv\n",
    "#Learn SQL: https://www.w3schools.com/sql/default.asp\n",
    "if not os.path.isfile('train.db'):\n",
    "    start = datetime.now()\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(csvfile, names=['Id', 'Title', 'Body', 'Tags'], 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\n",
    "    print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d5yUhXVNU94Q"
   },
   "source": [
    "<h3> 3.1.2 Counting the number of rows </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3ORCclXYU94R",
    "outputId": "9625d8e0-bf34-413e-8246-932fa1cb21b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in the database : \n",
      " 6034196\n",
      "Time taken to count the number of rows : 0:00:31.207379\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile('train.db'):\n",
    "    start = datetime.now()\n",
    "    con = sqlite3.connect('train.db')\n",
    "    num_rows = pd.read_sql_query(\"\"\"SELECT count(*) FROM data\"\"\", con)\n",
    "    #Always remember to close the database\n",
    "    print(\"Number of rows in the database :\",\"\\n\",num_rows['count(*)'].values[0])\n",
    "    con.close()\n",
    "    print(\"Time taken to count the number of rows :\", datetime.now() - start)\n",
    "else:\n",
    "    print(\"Please download the train.db file from drive or run the above cell to genarate train.db file\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Xso2eOEvU94Z"
   },
   "source": [
    "<h3>3.1.3 Checking for duplicates </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iBHCcr3DU94b",
    "outputId": "2340d414-8570-4d7e-fda6-a69c39f13780"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 0:02:23.193506\n"
     ]
    }
   ],
   "source": [
    "#Learn SQl: https://www.w3schools.com/sql/default.asp\n",
    "if os.path.isfile('train.db'):\n",
    "    start = datetime.now()\n",
    "    con = sqlite3.connect('train.db')\n",
    "    df_no_dup = pd.read_sql_query('SELECT Title, Body, Tags, COUNT(*) as cnt_dup FROM data GROUP BY Title, Body, Tags', con)\n",
    "    con.close()\n",
    "    print(\"Time taken to run this cell :\", datetime.now() - start)\n",
    "else:\n",
    "    print(\"Please download the train.db file from drive or run the first to genarate train.db file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gap4NRPWU94h",
    "outputId": "e0af72d7-4faf-4232-849b-d27bc1963221"
   },
   "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>Title</th>\n",
       "      <th>Body</th>\n",
       "      <th>Tags</th>\n",
       "      <th>cnt_dup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Implementing Boundary Value Analysis of S...</td>\n",
       "      <td>&lt;pre&gt;&lt;code&gt;#include&amp;lt;iostream&amp;gt;\\n#include&amp;...</td>\n",
       "      <td>c++ c</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dynamic Datagrid Binding in Silverlight?</td>\n",
       "      <td>&lt;p&gt;I should do binding for datagrid dynamicall...</td>\n",
       "      <td>c# silverlight data-binding</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dynamic Datagrid Binding in Silverlight?</td>\n",
       "      <td>&lt;p&gt;I should do binding for datagrid dynamicall...</td>\n",
       "      <td>c# silverlight data-binding columns</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>java.lang.NoClassDefFoundError: javax/serv...</td>\n",
       "      <td>&lt;p&gt;I followed the guide in &lt;a href=\"http://sta...</td>\n",
       "      <td>jsp jstl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>java.sql.SQLException:[Microsoft][ODBC Dri...</td>\n",
       "      <td>&lt;p&gt;I use the following code&lt;/p&gt;\\n\\n&lt;pre&gt;&lt;code&gt;...</td>\n",
       "      <td>java jdbc</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title  \\\n",
       "0       Implementing Boundary Value Analysis of S...   \n",
       "1           Dynamic Datagrid Binding in Silverlight?   \n",
       "2           Dynamic Datagrid Binding in Silverlight?   \n",
       "3      java.lang.NoClassDefFoundError: javax/serv...   \n",
       "4      java.sql.SQLException:[Microsoft][ODBC Dri...   \n",
       "\n",
       "                                                Body  \\\n",
       "0  <pre><code>#include&lt;iostream&gt;\\n#include&...   \n",
       "1  <p>I should do binding for datagrid dynamicall...   \n",
       "2  <p>I should do binding for datagrid dynamicall...   \n",
       "3  <p>I followed the guide in <a href=\"http://sta...   \n",
       "4  <p>I use the following code</p>\\n\\n<pre><code>...   \n",
       "\n",
       "                                  Tags  cnt_dup  \n",
       "0                                c++ c        1  \n",
       "1          c# silverlight data-binding        1  \n",
       "2  c# silverlight data-binding columns        1  \n",
       "3                             jsp jstl        1  \n",
       "4                            java jdbc        2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_dup.head()\n",
    "# we can observe that there are duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JzFO4EeDU94n",
    "outputId": "198980b2-6480-4a49-ab0a-8074199f5edb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of duplicate questions : 1827881 ( 30.292038906260256 % )\n"
     ]
    }
   ],
   "source": [
    "print(\"number of duplicate questions :\", num_rows['count(*)'].values[0]- df_no_dup.shape[0], \"(\",(1-((df_no_dup.shape[0])/(num_rows['count(*)'].values[0])))*100,\"% )\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gd2VdpN6U94t",
    "outputId": "05af061e-31e0-4a93-d41a-d738c5b9e890"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2656284\n",
       "2    1272336\n",
       "3     277575\n",
       "4         90\n",
       "5         25\n",
       "6          5\n",
       "Name: cnt_dup, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of times each question appeared in our database\n",
    "df_no_dup.cnt_dup.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Title  \\\n",
      "0       Implementing Boundary Value Analysis of S...   \n",
      "1           Dynamic Datagrid Binding in Silverlight?   \n",
      "2           Dynamic Datagrid Binding in Silverlight?   \n",
      "3      java.lang.NoClassDefFoundError: javax/serv...   \n",
      "4      java.sql.SQLException:[Microsoft][ODBC Dri...   \n",
      "\n",
      "                                                Body  \\\n",
      "0  <pre><code>#include&lt;iostream&gt;\\n#include&...   \n",
      "1  <p>I should do binding for datagrid dynamicall...   \n",
      "2  <p>I should do binding for datagrid dynamicall...   \n",
      "3  <p>I followed the guide in <a href=\"http://sta...   \n",
      "4  <p>I use the following code</p>\\n\\n<pre><code>...   \n",
      "\n",
      "                                  Tags  cnt_dup  \n",
      "0                                c++ c        1  \n",
      "1          c# silverlight data-binding        1  \n",
      "2  c# silverlight data-binding columns        1  \n",
      "3                             jsp jstl        1  \n",
      "4                            java jdbc        2  \n"
     ]
    }
   ],
   "source": [
    "print(df_no_dup.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4206308\n",
      "Time taken to run this cell : 0:02:22.340723\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64), array([], dtype=int64))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "aa_count=[]\n",
    "hh=[]\n",
    "for j in range(len(df_no_dup)):\n",
    "    tex=df_no_dup['Tags'][j]\n",
    "    #print(tex)\n",
    "    if tex is not None:\n",
    "        #print(\"heyram\")\n",
    "        #start=datetime.now()\n",
    "        hh.append(tex)\n",
    "        text=len(tex.split(\" \") ) \n",
    "        #print(text)\n",
    "        aa_count.append(text)    \n",
    "        \n",
    "print(len(aa_count))\n",
    "aaa=pd.DataFrame(aa_count,columns=['tag_count'])\n",
    "hhh=pd.DataFrame(hh,columns=['Tags'])\n",
    "df_no_dup=pd.concat([hhh,aaa],axis=1)\n",
    "# adding a new feature number of tags per question\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)\n",
    "df_no_dup.head()\n",
    "np.where(pd.isnull(df_no_dup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_dup=df_no_dup.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 0:00:03.483702\n"
     ]
    },
    {
     "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>Tags</th>\n",
       "      <th>tag_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c++ c</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c# silverlight data-binding</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c# silverlight data-binding columns</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jsp jstl</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>java jdbc</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Tags  tag_count\n",
       "0                                c++ c          2\n",
       "1          c# silverlight data-binding          3\n",
       "2  c# silverlight data-binding columns          4\n",
       "3                             jsp jstl          2\n",
       "4                            java jdbc          2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "df_no_dup[\"tag_count\"] = df_no_dup[\"Tags\"].apply(lambda text: len(text.split(\" \")))\n",
    "# adding a new feature number of tags per question\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)\n",
    "df_no_dup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iItMHo6MU948",
    "outputId": "824508df-c0c0-4d33-a667-83af84360bfc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1206157\n",
       "2    1111706\n",
       "4     814996\n",
       "1     568291\n",
       "5     505158\n",
       "Name: tag_count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of number of tags per question\n",
    "df_no_dup.tag_count.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2xMgCGUKU95C"
   },
   "outputs": [],
   "source": [
    "#Creating a new database with no duplicates\n",
    "if not os.path.isfile('train_no_dup.db'):\n",
    "    disk_dup = create_engine(\"sqlite:///train_no_dup.db\")\n",
    "    no_dup = pd.DataFrame(df_no_dup, columns=['Title', 'Body', 'Tags'])\n",
    "    no_dup.to_sql('no_dup_train',disk_dup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6Ou53MzeU95H",
    "outputId": "0b643de8-3481-45d0-ff2e-8400f7f5f12b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 0:00:53.947521\n"
     ]
    }
   ],
   "source": [
    "#This method seems more appropriate to work with this much data.\n",
    "#creating the connection with database file.\n",
    "if os.path.isfile('train_no_dup.db'):\n",
    "    start = datetime.now()\n",
    "    con = sqlite3.connect('train_no_dup.db')\n",
    "    tag_data = pd.read_sql_query(\"\"\"SELECT Tags FROM no_dup_train\"\"\", con)\n",
    "    #Always remember to close the database\n",
    "    con.close()\n",
    "\n",
    "    # Let's now drop unwanted column.\n",
    "    tag_data.drop(tag_data.index[0], inplace=True)\n",
    "    #Printing first 5 columns from our data frame\n",
    "    tag_data.head()\n",
    "    print(\"Time taken to run this cell :\", datetime.now() - start)\n",
    "else:\n",
    "    print(\"Please download the train.db file from drive or run the above cells to genarate train.db file\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hwZVL3doU95O"
   },
   "source": [
    "<h2> 3.2 Analysis of Tags </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zYs5peW8U95P"
   },
   "source": [
    "<h3> 3.2.1 Total number of unique tags </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "tag_data=tag_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking only 0.5 million data points\n",
    "#tag_data=tag_data[0:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Tags\n",
      "1          c# silverlight data-binding\n",
      "2  c# silverlight data-binding columns\n",
      "3                             jsp jstl\n",
      "4                            java jdbc\n",
      "5        facebook api facebook-php-sdk\n",
      "4206307\n"
     ]
    }
   ],
   "source": [
    "print(tag_data.head())\n",
    "print(len(tag_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ROdC95M_U95Q"
   },
   "outputs": [],
   "source": [
    "# Importing & Initializing the \"CountVectorizer\" object, which \n",
    "#is scikit-learn's bag of words tool.\n",
    "\n",
    "#by default 'split()' will tokenize each tag using space.\n",
    "vectorizer = CountVectorizer(tokenizer = lambda x: x.split())\n",
    "# fit_transform() does two functions: First, it fits the model\n",
    "# and learns the vocabulary; second, it transforms our training data\n",
    "# into feature vectors. The input to fit_transform should be a list of strings.\n",
    "tag_dtm = vectorizer.fit_transform(tag_data['Tags'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oz5N0GH0U95V",
    "outputId": "a65cd48f-6df2-44f5-e77c-99b38bb7ae2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points : 4206307\n",
      "Number of unique tags : 42048\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of data points :\", tag_dtm.shape[0])\n",
    "print(\"Number of unique tags :\", tag_dtm.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Otn6CUuQU95b",
    "outputId": "91ff8093-ebae-4218-a786-a117a9eadc0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some of the tags we have : ['.a', '.app', '.asp.net-mvc', '.aspxauth', '.bash-profile', '.class-file', '.cs-file', '.doc', '.drv', '.ds-store']\n"
     ]
    }
   ],
   "source": [
    "#'get_feature_name()' gives us the vocabulary.\n",
    "tags = vectorizer.get_feature_names()\n",
    "#Lets look at the tags we have.\n",
    "print(\"Some of the tags we have :\", tags[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NQa3ETSeU95g"
   },
   "source": [
    "<h3> 3.2.3 Number of times a tag appeared </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "beThyuyqU95h"
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/15115765/how-to-access-sparse-matrix-elements\n",
    "#Lets now store the document term matrix in a dictionary.\n",
    "freqs = tag_dtm.sum(axis=0).A1\n",
    "result = dict(zip(tags, freqs))\n",
    "#print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ALwSSx9U95m",
    "outputId": "452a449b-5605-4c32-e571-961eee563161"
   },
   "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>Tags</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.a</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>.app</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>.asp.net-mvc</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.aspxauth</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.bash-profile</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Tags  Counts\n",
       "0             .a      18\n",
       "1           .app      37\n",
       "2   .asp.net-mvc       1\n",
       "3      .aspxauth      21\n",
       "4  .bash-profile     138"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Saving this dictionary to csv files.\n",
    "if not os.path.isfile('tag_counts_dict_dtm.csv'):\n",
    "    with open('tag_counts_dict_dtm.csv', 'w') as csv_file:\n",
    "        writer = csv.writer(csv_file)\n",
    "        for key, value in result.items():\n",
    "            writer.writerow([key, value])\n",
    "tag_df = pd.read_csv(\"tag_counts_dict_dtm.csv\", names=['Tags', 'Counts'])\n",
    "tag_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mzmS8yiNU95t"
   },
   "outputs": [],
   "source": [
    "tag_df_sorted = tag_df.sort_values(['Counts'], ascending=False)\n",
    "tag_counts = tag_df_sorted['Counts'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6mcj55FIU95z",
    "outputId": "d6c9d567-a9a4-48e6-9d44-6f099f198339"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tag_counts)\n",
    "plt.title(\"Distribution of number of times tag appeared questions\")\n",
    "plt.grid()\n",
    "plt.xlabel(\"Tag number\")\n",
    "plt.ylabel(\"Number of times tag appeared\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UzTqln6XU955",
    "outputId": "07f08ef0-d26b-4797-eb3c-6586011e2588"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400 [331505  44829  22429  17728  13364  11162  10029   9148   8054   7151\n",
      "   6466   5865   5370   4983   4526   4281   4144   3929   3750   3593\n",
      "   3453   3299   3123   2986   2891   2738   2647   2527   2431   2331\n",
      "   2259   2186   2097   2020   1959   1900   1828   1770   1723   1673\n",
      "   1631   1574   1532   1479   1448   1406   1365   1328   1300   1266\n",
      "   1245   1222   1197   1181   1158   1139   1121   1101   1076   1056\n",
      "   1038   1023   1006    983    966    952    938    926    911    891\n",
      "    882    869    856    841    830    816    804    789    779    770\n",
      "    752    743    733    725    712    702    688    678    671    658\n",
      "    650    643    634    627    616    607    598    589    583    577\n",
      "    568    559    552    545    540    533    526    518    512    506\n",
      "    500    495    490    485    480    477    469    465    457    450\n",
      "    447    442    437    432    426    422    418    413    408    403\n",
      "    398    393    388    385    381    378    374    370    367    365\n",
      "    361    357    354    350    347    344    342    339    336    332\n",
      "    330    326    323    319    315    312    309    307    304    301\n",
      "    299    296    293    291    289    286    284    281    278    276\n",
      "    275    272    270    268    265    262    260    258    256    254\n",
      "    252    250    249    247    245    243    241    239    238    236\n",
      "    234    233    232    230    228    226    224    222    220    219\n",
      "    217    215    214    212    210    209    207    205    204    203\n",
      "    201    200    199    198    196    194    193    192    191    189\n",
      "    188    186    185    183    182    181    180    179    178    177\n",
      "    175    174    172    171    170    169    168    167    166    165\n",
      "    164    162    161    160    159    158    157    156    156    155\n",
      "    154    153    152    151    150    149    149    148    147    146\n",
      "    145    144    143    142    142    141    140    139    138    137\n",
      "    137    136    135    134    134    133    132    131    130    130\n",
      "    129    128    128    127    126    126    125    124    124    123\n",
      "    123    122    122    121    120    120    119    118    118    117\n",
      "    117    116    116    115    115    114    113    113    112    111\n",
      "    111    110    109    109    108    108    107    106    106    106\n",
      "    105    105    104    104    103    103    102    102    101    101\n",
      "    100    100     99     99     98     98     97     97     96     96\n",
      "     95     95     94     94     93     93     93     92     92     91\n",
      "     91     90     90     89     89     88     88     87     87     86\n",
      "     86     86     85     85     84     84     83     83     83     82\n",
      "     82     82     81     81     80     80     80     79     79     78\n",
      "     78     78     78     77     77     76     76     76     75     75\n",
      "     75     74     74     74     73     73     73     73     72     72]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(tag_counts[0:10000])\n",
    "plt.title('first 10k tags: Distribution of number of times tag appeared questions')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Tag number\")\n",
    "plt.ylabel(\"Number of times tag appeared\")\n",
    "plt.show()\n",
    "print(len(tag_counts[0:10000:25]), tag_counts[0:10000:25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ntm8E_K9U95-",
    "outputId": "750edd11-e52b-46bd-caba-1835994a0e05"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 [331505 221533 122769  95160  62023  44829  37170  31897  26925  24537\n",
      "  22429  21820  20957  19758  18905  17728  15533  15097  14884  13703\n",
      "  13364  13157  12407  11658  11228  11162  10863  10600  10350  10224\n",
      "  10029   9884   9719   9411   9252   9148   9040   8617   8361   8163\n",
      "   8054   7867   7702   7564   7274   7151   7052   6847   6656   6553\n",
      "   6466   6291   6183   6093   5971   5865   5760   5577   5490   5411\n",
      "   5370   5283   5207   5107   5066   4983   4891   4785   4658   4549\n",
      "   4526   4487   4429   4335   4310   4281   4239   4228   4195   4159\n",
      "   4144   4088   4050   4002   3957   3929   3874   3849   3818   3797\n",
      "   3750   3703   3685   3658   3615   3593   3564   3521   3505   3483\n",
      "   3453   3427   3396   3363   3326   3299   3272   3232   3196   3168\n",
      "   3123   3094   3073   3050   3012   2986   2983   2953   2934   2903\n",
      "   2891   2844   2819   2784   2754   2738   2726   2708   2681   2669\n",
      "   2647   2621   2604   2594   2556   2527   2510   2482   2460   2444\n",
      "   2431   2409   2395   2380   2363   2331   2312   2297   2290   2281\n",
      "   2259   2246   2222   2211   2198   2186   2162   2142   2132   2107\n",
      "   2097   2078   2057   2045   2036   2020   2011   1994   1971   1965\n",
      "   1959   1952   1940   1932   1912   1900   1879   1865   1855   1841\n",
      "   1828   1821   1813   1801   1782   1770   1760   1747   1741   1734\n",
      "   1723   1707   1697   1688   1683   1673   1665   1656   1646   1639]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(tag_counts[0:1000])\n",
    "plt.title('first 1k tags: Distribution of number of times tag appeared questions')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Tag number\")\n",
    "plt.ylabel(\"Number of times tag appeared\")\n",
    "plt.show()\n",
    "print(len(tag_counts[0:1000:5]), tag_counts[0:1000:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-SRUeKuWU96I",
    "outputId": "f20a242c-2f7d-4e7d-f3f2-291d2ea38caa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 [331505 221533 122769  95160  62023  44829  37170  31897  26925  24537\n",
      "  22429  21820  20957  19758  18905  17728  15533  15097  14884  13703\n",
      "  13364  13157  12407  11658  11228  11162  10863  10600  10350  10224\n",
      "  10029   9884   9719   9411   9252   9148   9040   8617   8361   8163\n",
      "   8054   7867   7702   7564   7274   7151   7052   6847   6656   6553\n",
      "   6466   6291   6183   6093   5971   5865   5760   5577   5490   5411\n",
      "   5370   5283   5207   5107   5066   4983   4891   4785   4658   4549\n",
      "   4526   4487   4429   4335   4310   4281   4239   4228   4195   4159\n",
      "   4144   4088   4050   4002   3957   3929   3874   3849   3818   3797\n",
      "   3750   3703   3685   3658   3615   3593   3564   3521   3505   3483]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(tag_counts[0:500])\n",
    "plt.title('first 500 tags: Distribution of number of times tag appeared questions')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Tag number\")\n",
    "plt.ylabel(\"Number of times tag appeared\")\n",
    "plt.show()\n",
    "print(len(tag_counts[0:500:5]), tag_counts[0:500:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CCOE0Nu4U96N",
    "outputId": "1f8df41e-b0dc-4d7f-a3b2-0a0c472ef448"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 [331505 221533 122769  95160  62023  44829  37170  31897  26925  24537\n",
      "  22429  21820  20957  19758  18905  17728  15533  15097  14884  13703]\n"
     ]
    }
   ],
   "source": [
    "plt.plot(tag_counts[0:100], c='b')\n",
    "plt.scatter(x=list(range(0,100,5)), y=tag_counts[0:100:5], c='orange', label=\"quantiles with 0.05 intervals\")\n",
    "# quantiles with 0.25 difference\n",
    "plt.scatter(x=list(range(0,100,25)), y=tag_counts[0:100:25], c='m', label = \"quantiles with 0.25 intervals\")\n",
    "\n",
    "for x,y in zip(list(range(0,100,25)), tag_counts[0:100:25]):\n",
    "    plt.annotate(s=\"({} , {})\".format(x,y), xy=(x,y), xytext=(x-0.05, y+500))\n",
    "\n",
    "plt.title('first 100 tags: Distribution of number of times tag appeared questions')\n",
    "plt.grid()\n",
    "plt.xlabel(\"Tag number\")\n",
    "plt.ylabel(\"Number of times tag appeared\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(len(tag_counts[0:100:5]), tag_counts[0:100:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MnwXytypU96R",
    "outputId": "3427f2fd-4b27-435e-d1a8-f1bcf4515d9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "153 Tags are used more than 10000 times\n",
      "14 Tags are used more than 100000 times\n"
     ]
    }
   ],
   "source": [
    "# Store tags greater than 10K in one list\n",
    "lst_tags_gt_10k = tag_df[tag_df.Counts>10000].Tags\n",
    "#Print the length of the list\n",
    "print ('{} Tags are used more than 10000 times'.format(len(lst_tags_gt_10k)))\n",
    "# Store tags greater than 100K in one list\n",
    "lst_tags_gt_100k = tag_df[tag_df.Counts>100000].Tags\n",
    "#Print the length of the list.\n",
    "print ('{} Tags are used more than 100000 times'.format(len(lst_tags_gt_100k)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oOXPF7q1U96W"
   },
   "source": [
    "<b>Observations:</b><br />\n",
    "1. There are total 153 tags which are used more than 10000 times.\n",
    "2. 14 tags are used more than 100000 times.\n",
    "3. Most frequent tag (i.e. c#) is used 331505 times.\n",
    "4. Since some tags occur much more frequenctly than others, Micro-averaged F1-score is the appropriate metric for this probelm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zb-Tg_sgU96a"
   },
   "source": [
    "<h3> 3.2.4 Tags Per Question </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bsPStbjGU96g",
    "outputId": "2e57d47f-1ae9-4776-ab17-d4315b9d0c14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have total 4206307 datapoints.\n",
      "[3, 4, 2, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "#Storing the count of tag in each question in list 'tag_count'\n",
    "tag_quest_count = tag_dtm.sum(axis=1).tolist()\n",
    "#Converting each value in the 'tag_quest_count' to integer.\n",
    "tag_quest_count=[int(j) for i in tag_quest_count for j in i]\n",
    "print ('We have total {} datapoints.'.format(len(tag_quest_count)))\n",
    "\n",
    "print(tag_quest_count[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DLbm7crfU96n",
    "outputId": "f71ff030-9937-4081-9d4a-fb2c62386e11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of tags per question: 5\n",
      "Minimum number of tags per question: 1\n",
      "Avg. number of tags per question: 2.899443\n"
     ]
    }
   ],
   "source": [
    "print( \"Maximum number of tags per question: %d\"%max(tag_quest_count))\n",
    "print( \"Minimum number of tags per question: %d\"%min(tag_quest_count))\n",
    "print( \"Avg. number of tags per question: %f\"% ((sum(tag_quest_count)*1.0)/len(tag_quest_count)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mb1vdd8KU96x",
    "outputId": "4579b7c7-ff27-4fb3-f3b9-29401610a3a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(tag_quest_count, palette='gist_rainbow')\n",
    "plt.title(\"Number of tags in the questions \")\n",
    "plt.xlabel(\"Number of Tags\")\n",
    "plt.ylabel(\"Number of questions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0RsUcQkNU963"
   },
   "source": [
    "<b>Observations:</b><br />\n",
    "1. Maximum number of tags per question: 5\n",
    "2. Minimum number of tags per question: 1\n",
    "3. Avg. number of tags per question: 2.899\n",
    "4. Most of the questions are having 2 or 3 tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hpc8IWjqU96_"
   },
   "source": [
    "<h3> 3.2.5 The top 20 tags </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ov3WmIEHU97A",
    "outputId": "8e4867c3-81da-445d-a7b3-fc8d4c283be0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=np.arange(30)\n",
    "tag_df_sorted.head(30).plot(kind='bar')\n",
    "plt.title('Frequency of top 20 tags')\n",
    "plt.xticks(i, tag_df_sorted['Tags'])\n",
    "plt.xlabel('Tags')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rlmizz7LU97D"
   },
   "source": [
    "<b>Observations:</b><br />\n",
    "1. Majority of the most frequent tags are programming language.\n",
    "2. C# is the top most frequent programming language.\n",
    "3. Android, IOS, Linux and windows are among the top most frequent operating systems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I-Z7F0_mU97F"
   },
   "source": [
    "<h3> 3.3 Cleaning and preprocessing of Questions </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cWzF-nN6U97G"
   },
   "source": [
    "<h3> 3.3.1 Preprocessing </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MeR1aoQaU97H"
   },
   "source": [
    "<ol> \n",
    "    <li> Sample 0.5M data points </li>\n",
    "    <li> Separate out code-snippets from Body </li>\n",
    "    <li> Remove Spcial characters from Question title and description (not in code)</li>\n",
    "    <li> Remove stop words (Except 'C') </li>\n",
    "    <li> Remove HTML Tags </li>\n",
    "    <li> Convert all the characters into small letters </li>\n",
    "    <li> Use SnowballStemmer to stem the words </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qr2xhpsAU97I"
   },
   "outputs": [],
   "source": [
    "def striphtml(data):\n",
    "    cleanr = re.compile('<.*?>')\n",
    "    cleantext = re.sub(cleanr, ' ', str(data))\n",
    "    return cleantext\n",
    "stop_words = set(stopwords.words('english'))\n",
    "stemmer = SnowballStemmer(\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LCDUa4KxU97L",
    "outputId": "8270b10f-cf17-4025-9440-5f2b2b62579c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tables in the databse:\n",
      "QuestionsProcessed\n"
     ]
    }
   ],
   "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",
    "def create_table(conn, create_table_sql):\n",
    "    \"\"\" create a table from the create_table_sql statement\n",
    "    :param conn: Connection object\n",
    "    :param create_table_sql: a CREATE TABLE statement\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    try:\n",
    "        c = conn.cursor()\n",
    "        c.execute(create_table_sql)\n",
    "    except Error as e:\n",
    "        print(e)\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))\n",
    "\n",
    "def create_database_table(database, query):\n",
    "    conn = create_connection(database)\n",
    "    if conn is not None:\n",
    "        create_table(conn, query)\n",
    "        checkTableExists(conn)\n",
    "    else:\n",
    "        print(\"Error! cannot create the database connection.\")\n",
    "    conn.close()\n",
    "\n",
    "sql_create_table = \"\"\"CREATE TABLE IF NOT EXISTS QuestionsProcessed (question text NOT NULL, code text, tags text, words_pre integer, words_post integer, is_code integer);\"\"\"\n",
    "create_database_table(\"Processed.db\", sql_create_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FELLR5FBU97U"
   },
   "source": [
    "__ we create a new data base to store the sampled and preprocessed questions __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qB0bL2drU97w"
   },
   "source": [
    "<h1>4. Machine Learning Models </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BZ3-VPbqU97w"
   },
   "source": [
    "<h2> 4.1 Converting tags for multilabel problems </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K88oaTD9U97y"
   },
   "source": [
    "<table>\n",
    "<tr>\n",
    "<th>X</th><th>y1</th><th>y2</th><th>y3</th><th>y4</th>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>x1</td><td>0</td><td>1</td><td>1</td><td>0</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>x1</td><td>1</td><td>0</td><td>0</td><td>0</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>x1</td><td>0</td><td>1</td><td>0</td><td>0</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LvjtTBZ6U98y"
   },
   "source": [
    "<h2> 4.5 Modeling with less data points (0.5M data points) and more weight to title and 500 tags only. </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n0QKMrEwU98y",
    "outputId": "3f1295d3-1aac-4cf0-f7a7-fab13cab6d6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tables in the databse:\n",
      "QuestionsProcessed\n"
     ]
    }
   ],
   "source": [
    "sql_create_table = \"\"\"CREATE TABLE IF NOT EXISTS QuestionsProcessed (question text NOT NULL, code text, tags text, words_pre integer, words_post integer, is_code integer);\"\"\"\n",
    "create_database_table(\"Titlemoreweight.db\", sql_create_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XHLunpYsU982",
    "outputId": "e79456dd-e137-4e91-8171-8379f4c0c286"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tables in the databse:\n",
      "QuestionsProcessed\n",
      "Cleared All the rows\n"
     ]
    }
   ],
   "source": [
    "# http://www.sqlitetutorial.net/sqlite-delete/\n",
    "# https://stackoverflow.com/questions/2279706/select-random-row-from-a-sqlite-table\n",
    "\n",
    "read_db = 'train_no_dup.db'\n",
    "write_db = 'Titlemoreweight.db'\n",
    "train_datasize = 400000\n",
    "if os.path.isfile(read_db):\n",
    "    conn_r = create_connection(read_db)\n",
    "    if conn_r is not None:\n",
    "        reader =conn_r.cursor()\n",
    "        # for selecting first 0.5M rows\n",
    "        reader.execute(\"SELECT Title, Body, Tags From no_dup_train LIMIT 500001;\")\n",
    "        # for selecting random points\n",
    "        #reader.execute(\"SELECT Title, Body, Tags From no_dup_train ORDER BY RANDOM() LIMIT 500001;\")\n",
    "\n",
    "if os.path.isfile(write_db):\n",
    "    conn_w = create_connection(write_db)\n",
    "    if conn_w is not None:\n",
    "        tables = checkTableExists(conn_w)\n",
    "        writer =conn_w.cursor()\n",
    "        if tables != 0:\n",
    "            writer.execute(\"DELETE FROM QuestionsProcessed WHERE 1\")\n",
    "            print(\"Cleared All the rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jvi2298wU986"
   },
   "source": [
    "<h3> 4.5.1 Preprocessing of questions </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JNhcD1LYU987"
   },
   "source": [
    "<ol> \n",
    "    <li> Separate Code from Body </li>\n",
    "    <li> Remove Spcial characters from Question title and description (not in code)</li>\n",
    "    <li> <b> Give more weightage to title : Add title three times to the question </b> </li>\n",
    "   \n",
    "    <li> Remove stop words (Except 'C') </li>\n",
    "    <li> Remove HTML Tags </li>\n",
    "    <li> Convert all the characters into small letters </li>\n",
    "    <li> Use SnowballStemmer to stem the words </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ifSmL0M-U98-",
    "outputId": "78019f2f-3a06-4b6d-a761-c8cae342ac42"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x54WQvZAU99B"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of questions completed= 100000\n",
      "number of questions completed= 200000\n",
      "number of questions completed= 300000\n",
      "number of questions completed= 400000\n",
      "number of questions completed= 500000\n",
      "Avg. length of questions(Title+Body) before processing: 1239\n",
      "Avg. length of questions(Title+Body) after processing: 424\n",
      "Percent of questions containing code: 57\n",
      "Time taken to run this cell : 0:21:37.730850\n"
     ]
    }
   ],
   "source": [
    "#http://www.bernzilla.com/2008/05/13/selecting-a-random-row-from-an-sqlite-table/\n",
    "start = datetime.now()\n",
    "preprocessed_data_list=[]\n",
    "reader.fetchone()\n",
    "questions_with_code=0\n",
    "len_pre=0\n",
    "len_post=0\n",
    "questions_proccesed = 0\n",
    "\n",
    "for row in reader:\n",
    "\n",
    "    \n",
    "    is_code = 0\n",
    "    \n",
    "    title, question, tags = row[0], row[1], str(row[2])\n",
    "    \n",
    "    if '<code>' in question:\n",
    "        questions_with_code+=1\n",
    "        is_code = 1\n",
    "    x = len(question)+len(title)\n",
    "    len_pre+=x\n",
    "    \n",
    "    code = str(re.findall(r'<code>(.*?)</code>', question, flags=re.DOTALL))\n",
    "    \n",
    "    question=re.sub('<code>(.*?)</code>', '', question, flags=re.MULTILINE|re.DOTALL)\n",
    "    question=striphtml(question.encode('utf-8'))\n",
    "    \n",
    "    title=title.encode('utf-8')\n",
    "    \n",
    "    # adding title three time to the data to increase its weight\n",
    "    # add tags string to the training data\n",
    "    \n",
    "    question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question\n",
    "    \n",
    "#     if questions_proccesed<=train_datasize:\n",
    "#         question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question+\" \"+str(tags)\n",
    "#     else:\n",
    "#         question=str(title)+\" \"+str(title)+\" \"+str(title)+\" \"+question\n",
    "\n",
    "    question=re.sub(r'[^A-Za-z0-9#+.\\-]+',' ',question)\n",
    "    words=word_tokenize(str(question.lower()))\n",
    "    \n",
    "    #Removing all single letter and and stopwords from question exceptt for the letter 'c'\n",
    "    question=' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))\n",
    "    \n",
    "    len_post+=len(question)\n",
    "    tup = (question,code,tags,x,len(question),is_code)\n",
    "    questions_proccesed += 1\n",
    "    writer.execute(\"insert into QuestionsProcessed(question,code,tags,words_pre,words_post,is_code) values (?,?,?,?,?,?)\",tup)\n",
    "    if (questions_proccesed%100000==0):\n",
    "        print(\"number of questions completed=\",questions_proccesed)\n",
    " \n",
    "\n",
    "no_dup_avg_len_pre=(len_pre*1.0)/questions_proccesed\n",
    "no_dup_avg_len_post=(len_post*1.0)/questions_proccesed\n",
    "\n",
    "print( \"Avg. length of questions(Title+Body) before processing: %d\"%no_dup_avg_len_pre)\n",
    "print( \"Avg. length of questions(Title+Body) after processing: %d\"%no_dup_avg_len_post)\n",
    "print (\"Percent of questions containing code: %d\"%((questions_with_code*100.0)/questions_proccesed))\n",
    "\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# never forget to close the conections or else we will end up with database locks\n",
    "conn_r.commit()\n",
    "conn_w.commit()\n",
    "conn_r.close()\n",
    "conn_w.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gpN1ZM2bU99F"
   },
   "source": [
    "__ Sample quesitons after preprocessing of data __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ytEecnCtU99H",
    "outputId": "f3c4992d-41e4-4e4a-9ae9-c40bb79dc43a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Questions after preprocessed\n",
      "====================================================================================================\n",
      "('dynam datagrid bind silverlight dynam datagrid bind silverlight dynam datagrid bind silverlight bind datagrid dynam code wrote code debug code block seem bind correct grid come column form come grid column although necessari bind nthank repli advance..',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('java.lang.noclassdeffounderror javax servlet jsp tagext taglibraryvalid java.lang.noclassdeffounderror javax servlet jsp tagext taglibraryvalid java.lang.noclassdeffounderror javax servlet jsp tagext taglibraryvalid follow guid link instal jstl got follow error tri launch jsp page java.lang.noclassdeffounderror javax servlet jsp tagext taglibraryvalid taglib declar instal jstl 1.1 tomcat webapp tri project work also tri version 1.2 jstl still messag caus solv',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('java.sql.sqlexcept microsoft odbc driver manag invalid descriptor index java.sql.sqlexcept microsoft odbc driver manag invalid descriptor index java.sql.sqlexcept microsoft odbc driver manag invalid descriptor index use follow code display caus solv',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('better way updat feed fb php sdk better way updat feed fb php sdk better way updat feed fb php sdk novic facebook api read mani tutori still confused.i find post feed api method like correct second way use curl someth like way better',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('btnadd click event open two window record ad btnadd click event open two window record ad btnadd click event open two window record ad open window search.aspx use code hav add button search.aspx nwhen insert record btnadd click event open anoth window nafter insert record close window',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('sql inject issu prevent correct form submiss php sql inject issu prevent correct form submiss php sql inject issu prevent correct form submiss php check everyth think make sure input field safe type sql inject good news safe bad news one tag mess form submiss place even touch life figur exact html use templat file forgiv okay entir php script get execut see data post none forum field post problem use someth titl field none data get post current use print post see submit noth work flawless statement though also mention script work flawless local machin use host come across problem state list input test mess',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('countabl subaddit lebesgu measur countabl subaddit lebesgu measur countabl subaddit lebesgu measur let lbrace rbrace sequenc set sigma -algebra mathcal want show left bigcup right leq sum left right countabl addit measur defin set sigma algebra mathcal think use monoton properti somewher proof start appreci littl help nthank ad han answer make follow addit construct given han answer clear bigcup bigcup cap emptyset neq left bigcup right left bigcup right sum left right also construct subset monoton left right leq left right final would sum leq sum result follow',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('hql equival sql queri hql equival sql queri hql equival sql queri hql queri replac name class properti name error occur hql error',)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "('undefin symbol architectur i386 objc class skpsmtpmessag referenc error undefin symbol architectur i386 objc class skpsmtpmessag referenc error undefin symbol architectur i386 objc class skpsmtpmessag referenc error import framework send email applic background import framework i.e skpsmtpmessag somebodi suggest get error collect2 ld return exit status import framework correct sorc taken framework follow mfmailcomposeviewcontrol question lock field updat answer drag drop folder project click copi nthat',)\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(write_db):\n",
    "    conn_r = create_connection(write_db)\n",
    "    if conn_r is not None:\n",
    "        reader =conn_r.cursor()\n",
    "        reader.execute(\"SELECT question From QuestionsProcessed LIMIT 10\")\n",
    "        print(\"Questions after preprocessed\")\n",
    "        print('='*100)\n",
    "        reader.fetchone()\n",
    "        for row in reader:\n",
    "            print(row)\n",
    "            print('-'*100)\n",
    "conn_r.commit()\n",
    "conn_r.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IspyyegoU99N"
   },
   "source": [
    "__ Saving Preprocessed data to a Database __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F_x-ETQJU99P"
   },
   "outputs": [],
   "source": [
    "#Taking 0.5 Million entries to a dataframe.\n",
    "write_db = 'Titlemoreweight.db'\n",
    "if os.path.isfile(write_db):\n",
    "    conn_r = create_connection(write_db)\n",
    "    if conn_r is not None:\n",
    "        preprocessed_data = pd.read_sql_query(\"\"\"SELECT question, Tags FROM QuestionsProcessed\"\"\", conn_r)\n",
    "conn_r.commit()\n",
    "conn_r.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bc7hwHjBU99U",
    "outputId": "7a90802a-0630-44da-853d-987e6ece8c4b"
   },
   "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>question</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dynam datagrid bind silverlight dynam datagrid...</td>\n",
       "      <td>c# silverlight data-binding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dynam datagrid bind silverlight dynam datagrid...</td>\n",
       "      <td>c# silverlight data-binding columns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>java.lang.noclassdeffounderror javax servlet j...</td>\n",
       "      <td>jsp jstl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>java.sql.sqlexcept microsoft odbc driver manag...</td>\n",
       "      <td>java jdbc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>better way updat feed fb php sdk better way up...</td>\n",
       "      <td>facebook api facebook-php-sdk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question  \\\n",
       "0  dynam datagrid bind silverlight dynam datagrid...   \n",
       "1  dynam datagrid bind silverlight dynam datagrid...   \n",
       "2  java.lang.noclassdeffounderror javax servlet j...   \n",
       "3  java.sql.sqlexcept microsoft odbc driver manag...   \n",
       "4  better way updat feed fb php sdk better way up...   \n",
       "\n",
       "                                  tags  \n",
       "0          c# silverlight data-binding  \n",
       "1  c# silverlight data-binding columns  \n",
       "2                             jsp jstl  \n",
       "3                            java jdbc  \n",
       "4        facebook api facebook-php-sdk  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xk9V0azqU99X",
    "outputId": "c767de50-461e-4454-c634-5487aa543b82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of data points in sample : 500000\n",
      "number of dimensions : 2\n"
     ]
    }
   ],
   "source": [
    "print(\"number of data points in sample :\", preprocessed_data.shape[0])\n",
    "print(\"number of dimensions :\", preprocessed_data.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oUpccCSkU99Z"
   },
   "source": [
    "__ Converting string Tags to multilable output variables __ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SWg_g1lNU99a"
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(tokenizer = lambda x: x.split(), binary='true')\n",
    "multilabel_y = vectorizer.fit_transform(preprocessed_data['tags'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pbtD0Hx8U99c"
   },
   "source": [
    "__ Selecting 500 Tags __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def tags_to_choose(n):\n",
    "    t = multilabel_y.sum(axis=0).tolist()[0]\n",
    "    sorted_tags_i = sorted(range(len(t)), key=lambda i: t[i], reverse=True)\n",
    "    multilabel_yn=multilabel_y[:,sorted_tags_i[:n]]\n",
    "    return multilabel_yn\n",
    "\n",
    "def questions_explained_fn(n):\n",
    "    multilabel_yn = tags_to_choose(n)\n",
    "    x= multilabel_yn.sum(axis=1)\n",
    "    return (np.count_nonzero(x==0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h_nMDxAIU99d"
   },
   "outputs": [],
   "source": [
    "questions_explained = []\n",
    "total_tags=multilabel_y.shape[1]\n",
    "total_qs=preprocessed_data.shape[0]\n",
    "for i in range(500, total_tags, 100):\n",
    "    questions_explained.append(np.round(((total_qs-questions_explained_fn(i))/total_qs)*100,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fggMk2IJU99f",
    "outputId": "7c443492-b0c4-492d-afc2-16c59b8b954e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with  5500 tags we are covering  99.157 % of questions\n",
      "with  500 tags we are covering  90.956 % of questions\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(questions_explained)\n",
    "xlabel = list(500+np.array(range(-50,450,50))*50)\n",
    "ax.set_xticklabels(xlabel)\n",
    "plt.xlabel(\"Number of tags\")\n",
    "plt.ylabel(\"Number Questions coverd partially\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "# you can choose any number of tags based on your computing power, minimun is 500(it covers 90% of the tags)\n",
    "print(\"with \",5500,\"tags we are covering \",questions_explained[50],\"% of questions\")\n",
    "print(\"with \",500,\"tags we are covering \",questions_explained[0],\"% of questions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VuJzfmNrU99i",
    "outputId": "2c34ec8f-ee38-451d-f477-89f880a98b7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of questions that are not covered : 45221 out of  500000\n"
     ]
    }
   ],
   "source": [
    "# we will be taking 500 tags\n",
    "multilabel_yx = tags_to_choose(500)\n",
    "print(\"number of questions that are not covered :\", questions_explained_fn(500),\"out of \", total_qs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['preprocessed_data.pkl']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(preprocessed_data, 'preprocessed_data.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WsduwXTeU99k"
   },
   "outputs": [],
   "source": [
    "x_train=preprocessed_data.head(train_datasize)\n",
    "x_test=preprocessed_data.tail(preprocessed_data.shape[0] - 400000)\n",
    "\n",
    "y_train = multilabel_yx[0:train_datasize,:]\n",
    "y_test = multilabel_yx[train_datasize:preprocessed_data.shape[0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iZZDSH_VU99m",
    "outputId": "15b74fe3-29ac-40d2-c393-82260f78ce19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data : (400000, 500)\n",
      "Number of data points in test data : (100000, 500)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of data points in train data :\", y_train.shape)\n",
    "print(\"Number of data points in test data :\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gDJ2PvnzU99o"
   },
   "source": [
    "<h3> 4.5.2 Featurizing data with TfIdf vectorizer </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "530e8tW9U99o",
    "outputId": "a73463fb-694d-44bf-dc16-97c9b161f0a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 0:07:18.075098\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm=\"l2\", \\\n",
    "                             tokenizer = lambda x: x.split(), sublinear_tf=False,\n",
    "                             ngram_range=(1,4))\n",
    "x_train_multilabel = vectorizer.fit_transform(x_train['question'])\n",
    "x_test_multilabel = vectorizer.transform(x_test['question'])\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r9iDfzXIU99t",
    "outputId": "23c4dfde-72c4-40c3-9339-29e56fc18b59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of train data X: (400000, 95585) Y : (400000, 500)\n",
      "Dimensions of test data X: (100000, 95585) Y: (100000, 500)\n"
     ]
    }
   ],
   "source": [
    "print(\"Dimensions of train data X:\",x_train_multilabel.shape, \"Y :\",y_train.shape)\n",
    "print(\"Dimensions of test data X:\",x_test_multilabel.shape,\"Y:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Kmmnoy4XU99v"
   },
   "source": [
    "<h3> 4.5.3  OneVsRest Classifier with SGDClassifier using TFIDF  </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GnHoxl5DU99w",
    "outputId": "fea313da-ed92-469d-d34b-149d3fc5e01e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.23625\n",
      "Hamming loss  0.00278104\n",
      "Micro-average quality numbers\n",
      "Precision: 0.7216, Recall: 0.3256, F1-measure: 0.4488\n",
      "Macro-average quality numbers\n",
      "Precision: 0.5490, Recall: 0.2571, F1-measure: 0.3342\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      0.64      0.76      5519\n",
      "          1       0.68      0.26      0.38      8190\n",
      "          2       0.81      0.38      0.52      6529\n",
      "          3       0.81      0.43      0.56      3231\n",
      "          4       0.81      0.41      0.54      6430\n",
      "          5       0.82      0.34      0.48      2879\n",
      "          6       0.87      0.49      0.63      5086\n",
      "          7       0.88      0.54      0.67      4533\n",
      "          8       0.61      0.13      0.21      3000\n",
      "          9       0.81      0.53      0.64      2765\n",
      "         10       0.59      0.17      0.26      3051\n",
      "         11       0.70      0.33      0.45      3009\n",
      "         12       0.65      0.25      0.36      2630\n",
      "         13       0.71      0.23      0.35      1426\n",
      "         14       0.90      0.53      0.67      2548\n",
      "         15       0.68      0.18      0.29      2371\n",
      "         16       0.64      0.23      0.34       873\n",
      "         17       0.89      0.60      0.72      2151\n",
      "         18       0.63      0.23      0.34      2204\n",
      "         19       0.72      0.40      0.51       831\n",
      "         20       0.77      0.40      0.53      1860\n",
      "         21       0.27      0.08      0.12      2023\n",
      "         22       0.50      0.22      0.31      1513\n",
      "         23       0.91      0.49      0.64      1207\n",
      "         24       0.56      0.29      0.38       506\n",
      "         25       0.68      0.30      0.41       425\n",
      "         26       0.65      0.40      0.50       793\n",
      "         27       0.60      0.33      0.42      1291\n",
      "         28       0.75      0.36      0.48      1208\n",
      "         29       0.41      0.09      0.14       406\n",
      "         30       0.76      0.17      0.28       504\n",
      "         31       0.30      0.11      0.16       732\n",
      "         32       0.57      0.22      0.32       441\n",
      "         33       0.57      0.18      0.27      1645\n",
      "         34       0.72      0.25      0.37      1058\n",
      "         35       0.83      0.55      0.66       946\n",
      "         36       0.66      0.20      0.30       644\n",
      "         37       0.98      0.67      0.79       136\n",
      "         38       0.63      0.35      0.45       570\n",
      "         39       0.85      0.28      0.43       766\n",
      "         40       0.62      0.28      0.38      1132\n",
      "         41       0.46      0.19      0.27       174\n",
      "         42       0.80      0.53      0.64       210\n",
      "         43       0.80      0.41      0.54       433\n",
      "         44       0.66      0.49      0.57       626\n",
      "         45       0.74      0.31      0.44       852\n",
      "         46       0.75      0.43      0.54       534\n",
      "         47       0.32      0.13      0.18       350\n",
      "         48       0.74      0.51      0.60       496\n",
      "         49       0.80      0.61      0.69       785\n",
      "         50       0.16      0.03      0.06       475\n",
      "         51       0.28      0.08      0.13       305\n",
      "         52       0.47      0.04      0.07       251\n",
      "         53       0.68      0.40      0.50       914\n",
      "         54       0.46      0.16      0.23       728\n",
      "         55       0.29      0.02      0.03       258\n",
      "         56       0.47      0.19      0.27       821\n",
      "         57       0.50      0.09      0.15       541\n",
      "         58       0.78      0.28      0.41       748\n",
      "         59       0.94      0.62      0.75       724\n",
      "         60       0.33      0.06      0.11       660\n",
      "         61       0.85      0.19      0.31       235\n",
      "         62       0.91      0.71      0.80       718\n",
      "         63       0.83      0.63      0.71       468\n",
      "         64       0.54      0.32      0.40       191\n",
      "         65       0.36      0.13      0.19       429\n",
      "         66       0.27      0.05      0.08       415\n",
      "         67       0.76      0.47      0.58       274\n",
      "         68       0.82      0.52      0.63       510\n",
      "         69       0.67      0.45      0.54       466\n",
      "         70       0.27      0.06      0.10       305\n",
      "         71       0.46      0.14      0.22       247\n",
      "         72       0.78      0.48      0.59       401\n",
      "         73       0.98      0.73      0.84        86\n",
      "         74       0.73      0.37      0.49       120\n",
      "         75       0.89      0.67      0.77       129\n",
      "         76       0.50      0.00      0.01       473\n",
      "         77       0.35      0.25      0.29       143\n",
      "         78       0.80      0.45      0.57       347\n",
      "         79       0.73      0.23      0.35       479\n",
      "         80       0.54      0.31      0.40       279\n",
      "         81       0.78      0.17      0.28       461\n",
      "         82       0.19      0.01      0.03       298\n",
      "         83       0.77      0.45      0.57       396\n",
      "         84       0.55      0.34      0.42       184\n",
      "         85       0.67      0.20      0.31       573\n",
      "         86       0.47      0.05      0.08       325\n",
      "         87       0.49      0.27      0.35       273\n",
      "         88       0.42      0.21      0.28       135\n",
      "         89       0.30      0.07      0.12       232\n",
      "         90       0.57      0.31      0.40       409\n",
      "         91       0.64      0.25      0.36       420\n",
      "         92       0.75      0.53      0.62       408\n",
      "         93       0.69      0.47      0.56       241\n",
      "         94       0.33      0.04      0.08       211\n",
      "         95       0.33      0.07      0.12       277\n",
      "         96       0.28      0.04      0.07       410\n",
      "         97       0.89      0.32      0.47       501\n",
      "         98       0.78      0.59      0.67       136\n",
      "         99       0.55      0.33      0.41       239\n",
      "        100       0.58      0.14      0.22       324\n",
      "        101       0.93      0.61      0.73       277\n",
      "        102       0.92      0.70      0.79       613\n",
      "        103       0.51      0.17      0.25       157\n",
      "        104       0.23      0.06      0.10       295\n",
      "        105       0.85      0.34      0.49       334\n",
      "        106       0.81      0.14      0.24       335\n",
      "        107       0.76      0.48      0.59       389\n",
      "        108       0.56      0.24      0.33       251\n",
      "        109       0.54      0.41      0.46       317\n",
      "        110       0.68      0.08      0.14       187\n",
      "        111       0.48      0.07      0.12       140\n",
      "        112       0.61      0.28      0.38       154\n",
      "        113       0.63      0.18      0.28       332\n",
      "        114       0.46      0.27      0.34       323\n",
      "        115       0.48      0.21      0.29       344\n",
      "        116       0.76      0.49      0.60       370\n",
      "        117       0.57      0.22      0.32       313\n",
      "        118       0.78      0.68      0.72       874\n",
      "        119       0.47      0.19      0.27       293\n",
      "        120       0.00      0.00      0.00       200\n",
      "        121       0.76      0.48      0.59       463\n",
      "        122       0.38      0.09      0.15       119\n",
      "        123       0.75      0.01      0.02       256\n",
      "        124       0.91      0.69      0.79       195\n",
      "        125       0.41      0.11      0.17       138\n",
      "        126       0.81      0.49      0.61       376\n",
      "        127       0.15      0.03      0.05       122\n",
      "        128       0.15      0.03      0.05       252\n",
      "        129       0.41      0.10      0.16       144\n",
      "        130       0.41      0.08      0.13       150\n",
      "        131       0.17      0.01      0.02       210\n",
      "        132       0.66      0.25      0.37       361\n",
      "        133       0.94      0.54      0.68       453\n",
      "        134       0.89      0.73      0.80       124\n",
      "        135       0.27      0.03      0.06        91\n",
      "        136       0.68      0.27      0.38       128\n",
      "        137       0.58      0.34      0.43       218\n",
      "        138       0.79      0.16      0.26       243\n",
      "        139       0.38      0.19      0.25       149\n",
      "        140       0.76      0.44      0.55       318\n",
      "        141       0.29      0.11      0.16       159\n",
      "        142       0.66      0.35      0.46       274\n",
      "        143       0.87      0.72      0.79       362\n",
      "        144       0.58      0.15      0.24       118\n",
      "        145       0.67      0.37      0.48       164\n",
      "        146       0.59      0.28      0.38       461\n",
      "        147       0.66      0.39      0.49       159\n",
      "        148       0.34      0.14      0.20       166\n",
      "        149       0.99      0.45      0.62       346\n",
      "        150       0.65      0.09      0.15       350\n",
      "        151       0.90      0.64      0.74        55\n",
      "        152       0.79      0.46      0.58       387\n",
      "        153       0.48      0.09      0.16       150\n",
      "        154       0.60      0.12      0.20       281\n",
      "        155       0.27      0.06      0.10       202\n",
      "        156       0.76      0.62      0.68       130\n",
      "        157       0.27      0.07      0.12       245\n",
      "        158       0.88      0.58      0.70       177\n",
      "        159       0.47      0.26      0.34       130\n",
      "        160       0.48      0.12      0.20       336\n",
      "        161       0.91      0.57      0.70       220\n",
      "        162       0.19      0.03      0.06       229\n",
      "        163       0.89      0.40      0.55       316\n",
      "        164       0.75      0.35      0.47       283\n",
      "        165       0.64      0.32      0.43       197\n",
      "        166       0.48      0.25      0.33       101\n",
      "        167       0.47      0.19      0.27       231\n",
      "        168       0.61      0.22      0.32       370\n",
      "        169       0.41      0.17      0.24       258\n",
      "        170       0.30      0.06      0.10       101\n",
      "        171       0.37      0.21      0.27        89\n",
      "        172       0.52      0.37      0.43       193\n",
      "        173       0.41      0.21      0.27       309\n",
      "        174       0.52      0.13      0.21       172\n",
      "        175       0.93      0.72      0.81        95\n",
      "        176       0.94      0.59      0.73       346\n",
      "        177       0.94      0.43      0.59       322\n",
      "        178       0.64      0.46      0.53       232\n",
      "        179       0.35      0.06      0.11       125\n",
      "        180       0.55      0.27      0.36       145\n",
      "        181       0.40      0.10      0.16        77\n",
      "        182       0.20      0.03      0.05       182\n",
      "        183       0.61      0.31      0.41       257\n",
      "        184       0.08      0.01      0.02       216\n",
      "        185       0.35      0.06      0.11       242\n",
      "        186       0.41      0.16      0.23       165\n",
      "        187       0.76      0.56      0.64       263\n",
      "        188       0.34      0.11      0.17       174\n",
      "        189       0.71      0.29      0.42       136\n",
      "        190       0.88      0.49      0.63       202\n",
      "        191       0.42      0.15      0.22       134\n",
      "        192       0.73      0.40      0.52       230\n",
      "        193       0.43      0.18      0.25        90\n",
      "        194       0.58      0.48      0.53       185\n",
      "        195       0.18      0.04      0.06       156\n",
      "        196       0.38      0.07      0.12       160\n",
      "        197       0.61      0.06      0.12       266\n",
      "        198       0.43      0.06      0.11       284\n",
      "        199       0.43      0.06      0.11       145\n",
      "        200       0.94      0.68      0.79       212\n",
      "        201       0.68      0.22      0.33       317\n",
      "        202       0.79      0.54      0.64       427\n",
      "        203       0.31      0.09      0.14       232\n",
      "        204       0.50      0.22      0.31       217\n",
      "        205       0.48      0.42      0.45       527\n",
      "        206       0.13      0.02      0.03       124\n",
      "        207       0.50      0.09      0.15       103\n",
      "        208       0.89      0.48      0.63       287\n",
      "        209       0.28      0.07      0.11       193\n",
      "        210       0.71      0.31      0.44       220\n",
      "        211       0.78      0.18      0.29       140\n",
      "        212       0.17      0.02      0.03       161\n",
      "        213       0.55      0.25      0.34        72\n",
      "        214       0.61      0.45      0.52       396\n",
      "        215       0.86      0.32      0.47       134\n",
      "        216       0.50      0.06      0.10       400\n",
      "        217       0.56      0.25      0.35        75\n",
      "        218       0.96      0.75      0.85       219\n",
      "        219       0.75      0.36      0.48       210\n",
      "        220       0.90      0.59      0.71       298\n",
      "        221       0.97      0.60      0.74       266\n",
      "        222       0.78      0.41      0.54       290\n",
      "        223       0.08      0.01      0.01       128\n",
      "        224       0.78      0.38      0.51       159\n",
      "        225       0.58      0.30      0.39       164\n",
      "        226       0.62      0.35      0.45       144\n",
      "        227       0.58      0.32      0.41       276\n",
      "        228       0.17      0.02      0.03       235\n",
      "        229       0.33      0.02      0.04       216\n",
      "        230       0.35      0.17      0.23       228\n",
      "        231       0.71      0.47      0.57        64\n",
      "        232       0.44      0.07      0.12       103\n",
      "        233       0.69      0.29      0.41       216\n",
      "        234       0.75      0.08      0.14       116\n",
      "        235       0.55      0.36      0.44        77\n",
      "        236       0.96      0.64      0.77        67\n",
      "        237       0.52      0.06      0.10       218\n",
      "        238       0.35      0.09      0.14       139\n",
      "        239       0.17      0.01      0.02        94\n",
      "        240       0.55      0.27      0.37        77\n",
      "        241       0.52      0.09      0.15       167\n",
      "        242       0.83      0.29      0.43        86\n",
      "        243       0.45      0.16      0.23        58\n",
      "        244       0.57      0.17      0.26       269\n",
      "        245       0.18      0.06      0.09       112\n",
      "        246       0.95      0.73      0.83       255\n",
      "        247       0.44      0.19      0.27        58\n",
      "        248       0.25      0.02      0.04        81\n",
      "        249       0.00      0.00      0.00       131\n",
      "        250       0.43      0.22      0.29        93\n",
      "        251       0.66      0.29      0.40       154\n",
      "        252       0.33      0.04      0.07       129\n",
      "        253       0.63      0.33      0.43        83\n",
      "        254       0.36      0.09      0.14       191\n",
      "        255       0.16      0.03      0.05       219\n",
      "        256       0.25      0.03      0.05       130\n",
      "        257       0.46      0.29      0.36        93\n",
      "        258       0.69      0.43      0.53       217\n",
      "        259       0.33      0.11      0.16       141\n",
      "        260       0.95      0.13      0.23       143\n",
      "        261       0.56      0.12      0.20       219\n",
      "        262       0.54      0.27      0.36       107\n",
      "        263       0.40      0.23      0.29       236\n",
      "        264       0.29      0.17      0.21       119\n",
      "        265       0.31      0.11      0.16        72\n",
      "        266       0.00      0.00      0.00        70\n",
      "        267       0.32      0.14      0.19       107\n",
      "        268       0.66      0.41      0.51       169\n",
      "        269       0.30      0.10      0.15       129\n",
      "        270       0.74      0.53      0.62       159\n",
      "        271       0.81      0.30      0.44       190\n",
      "        272       0.62      0.22      0.33       248\n",
      "        273       0.91      0.70      0.79       264\n",
      "        274       0.90      0.66      0.76       105\n",
      "        275       0.57      0.08      0.14       104\n",
      "        276       0.14      0.02      0.03       115\n",
      "        277       0.83      0.59      0.69       170\n",
      "        278       0.65      0.23      0.34       145\n",
      "        279       0.92      0.57      0.71       230\n",
      "        280       0.57      0.42      0.49        80\n",
      "        281       0.68      0.55      0.61       217\n",
      "        282       0.75      0.47      0.58       175\n",
      "        283       0.34      0.05      0.09       269\n",
      "        284       0.65      0.27      0.38        74\n",
      "        285       0.86      0.49      0.62       206\n",
      "        286       0.90      0.60      0.72       227\n",
      "        287       0.85      0.31      0.45       130\n",
      "        288       0.39      0.07      0.12       129\n",
      "        289       0.50      0.03      0.05        80\n",
      "        290       0.14      0.06      0.08        99\n",
      "        291       0.78      0.32      0.45       208\n",
      "        292       0.17      0.01      0.03        67\n",
      "        293       0.82      0.42      0.56       109\n",
      "        294       0.40      0.24      0.30       140\n",
      "        295       0.24      0.08      0.12       241\n",
      "        296       0.24      0.10      0.14        72\n",
      "        297       0.22      0.04      0.06       107\n",
      "        298       0.80      0.39      0.53        61\n",
      "        299       0.93      0.36      0.52        77\n",
      "        300       0.19      0.06      0.10       111\n",
      "        301       0.00      0.00      0.00       126\n",
      "        302       0.00      0.00      0.00        73\n",
      "        303       0.56      0.35      0.43       176\n",
      "        304       0.96      0.70      0.81       230\n",
      "        305       0.97      0.59      0.73       156\n",
      "        306       0.51      0.36      0.42       146\n",
      "        307       0.29      0.08      0.13        98\n",
      "        308       0.00      0.00      0.00        78\n",
      "        309       0.71      0.05      0.10        94\n",
      "        310       0.76      0.35      0.48       162\n",
      "        311       0.81      0.53      0.64       116\n",
      "        312       0.48      0.26      0.34        57\n",
      "        313       0.80      0.06      0.11        65\n",
      "        314       0.51      0.36      0.42       138\n",
      "        315       0.53      0.21      0.30       195\n",
      "        316       0.46      0.26      0.33        69\n",
      "        317       0.34      0.10      0.15       134\n",
      "        318       0.49      0.33      0.40       148\n",
      "        319       0.85      0.44      0.58       161\n",
      "        320       0.22      0.14      0.17       104\n",
      "        321       0.85      0.53      0.65       156\n",
      "        322       0.60      0.31      0.41       134\n",
      "        323       0.57      0.38      0.45       232\n",
      "        324       0.44      0.18      0.26        92\n",
      "        325       0.47      0.28      0.35       197\n",
      "        326       0.12      0.02      0.04       126\n",
      "        327       0.50      0.04      0.08       115\n",
      "        328       0.98      0.64      0.78       198\n",
      "        329       0.63      0.31      0.42       125\n",
      "        330       0.83      0.19      0.30        81\n",
      "        331       0.50      0.09      0.15        94\n",
      "        332       1.00      0.02      0.04        56\n",
      "        333       0.13      0.03      0.04       260\n",
      "        334       0.18      0.03      0.06        60\n",
      "        335       0.32      0.09      0.14       110\n",
      "        336       0.63      0.41      0.50        71\n",
      "        337       0.13      0.03      0.05        66\n",
      "        338       0.44      0.31      0.36       150\n",
      "        339       0.00      0.00      0.00        54\n",
      "        340       0.85      0.54      0.66       195\n",
      "        341       0.89      0.20      0.33        79\n",
      "        342       0.38      0.16      0.22        38\n",
      "        343       0.67      0.37      0.48        43\n",
      "        344       0.53      0.24      0.33        68\n",
      "        345       0.67      0.38      0.49        73\n",
      "        346       0.27      0.03      0.05       116\n",
      "        347       0.88      0.34      0.49       111\n",
      "        348       0.29      0.10      0.14        63\n",
      "        349       0.82      0.59      0.69       104\n",
      "        350       0.64      0.48      0.55        44\n",
      "        351       0.73      0.20      0.31        40\n",
      "        352       0.98      0.40      0.57       136\n",
      "        353       0.42      0.20      0.27        54\n",
      "        354       0.36      0.04      0.07       134\n",
      "        355       0.51      0.28      0.36       120\n",
      "        356       0.55      0.25      0.34       228\n",
      "        357       0.66      0.28      0.39       269\n",
      "        358       0.69      0.36      0.48        80\n",
      "        359       0.86      0.43      0.57       140\n",
      "        360       0.40      0.15      0.22       125\n",
      "        361       0.89      0.63      0.74       169\n",
      "        362       0.11      0.04      0.05        56\n",
      "        363       0.94      0.66      0.77       154\n",
      "        364       0.33      0.05      0.09        58\n",
      "        365       0.26      0.13      0.17        71\n",
      "        366       1.00      0.65      0.79        54\n",
      "        367       0.29      0.03      0.06       116\n",
      "        368       0.00      0.00      0.00        54\n",
      "        369       0.00      0.00      0.00        71\n",
      "        370       0.20      0.03      0.06        61\n",
      "        371       0.55      0.08      0.15        71\n",
      "        372       0.65      0.46      0.54        52\n",
      "        373       0.78      0.36      0.49       150\n",
      "        374       0.34      0.13      0.19        93\n",
      "        375       0.19      0.04      0.07        67\n",
      "        376       0.00      0.00      0.00        76\n",
      "        377       0.74      0.16      0.26       106\n",
      "        378       0.27      0.03      0.06        86\n",
      "        379       0.33      0.07      0.12        14\n",
      "        380       1.00      0.40      0.57       122\n",
      "        381       0.19      0.03      0.05       104\n",
      "        382       0.32      0.09      0.14        66\n",
      "        383       0.46      0.27      0.34       110\n",
      "        384       0.00      0.00      0.00       155\n",
      "        385       0.40      0.08      0.13        50\n",
      "        386       0.24      0.11      0.15        64\n",
      "        387       0.43      0.06      0.11        93\n",
      "        388       0.61      0.27      0.38       102\n",
      "        389       0.07      0.01      0.02       108\n",
      "        390       0.96      0.66      0.78       178\n",
      "        391       0.62      0.17      0.27       115\n",
      "        392       0.77      0.40      0.53        42\n",
      "        393       0.00      0.00      0.00       134\n",
      "        394       0.50      0.02      0.03       112\n",
      "        395       0.42      0.12      0.19       176\n",
      "        396       0.50      0.08      0.14       125\n",
      "        397       0.70      0.23      0.35       224\n",
      "        398       0.88      0.56      0.68        63\n",
      "        399       0.00      0.00      0.00        59\n",
      "        400       0.48      0.35      0.40        63\n",
      "        401       0.50      0.18      0.27        98\n",
      "        402       0.57      0.16      0.25       162\n",
      "        403       0.41      0.14      0.21        83\n",
      "        404       0.73      0.84      0.78        19\n",
      "        405       0.29      0.07      0.11        92\n",
      "        406       0.86      0.15      0.25        41\n",
      "        407       0.62      0.30      0.41        43\n",
      "        408       0.80      0.32      0.46       160\n",
      "        409       0.17      0.10      0.13        50\n",
      "        410       0.00      0.00      0.00        19\n",
      "        411       0.39      0.10      0.16       175\n",
      "        412       0.29      0.06      0.09        72\n",
      "        413       0.56      0.05      0.10        95\n",
      "        414       0.16      0.03      0.05        97\n",
      "        415       0.30      0.15      0.20        48\n",
      "        416       0.44      0.28      0.34        83\n",
      "        417       0.50      0.07      0.13        40\n",
      "        418       0.37      0.08      0.13        91\n",
      "        419       0.52      0.28      0.36        90\n",
      "        420       0.29      0.22      0.25        37\n",
      "        421       0.00      0.00      0.00        66\n",
      "        422       0.61      0.34      0.44        73\n",
      "        423       0.48      0.25      0.33        56\n",
      "        424       0.93      0.82      0.87        33\n",
      "        425       0.00      0.00      0.00        76\n",
      "        426       0.25      0.05      0.08        81\n",
      "        427       0.99      0.68      0.81       150\n",
      "        428       0.95      0.66      0.78        29\n",
      "        429       0.99      0.65      0.78       389\n",
      "        430       0.64      0.36      0.46       167\n",
      "        431       0.48      0.08      0.14       123\n",
      "        432       0.45      0.33      0.38        39\n",
      "        433       0.29      0.16      0.20        82\n",
      "        434       1.00      0.65      0.79        66\n",
      "        435       0.63      0.45      0.53        93\n",
      "        436       0.52      0.25      0.34        87\n",
      "        437       0.26      0.06      0.10        86\n",
      "        438       0.73      0.47      0.57       104\n",
      "        439       0.62      0.13      0.21       100\n",
      "        440       0.25      0.01      0.01       141\n",
      "        441       0.42      0.25      0.31       110\n",
      "        442       0.40      0.13      0.20       123\n",
      "        443       0.50      0.13      0.20        71\n",
      "        444       0.44      0.06      0.11       109\n",
      "        445       0.42      0.21      0.28        48\n",
      "        446       0.43      0.25      0.32        76\n",
      "        447       0.26      0.13      0.18        38\n",
      "        448       0.69      0.54      0.61        81\n",
      "        449       0.57      0.16      0.25       132\n",
      "        450       0.46      0.26      0.33        81\n",
      "        451       0.88      0.29      0.44        76\n",
      "        452       0.00      0.00      0.00        44\n",
      "        453       0.00      0.00      0.00        44\n",
      "        454       0.94      0.41      0.57        70\n",
      "        455       0.48      0.07      0.12       155\n",
      "        456       0.43      0.14      0.21        43\n",
      "        457       0.52      0.21      0.30        72\n",
      "        458       0.29      0.08      0.13        62\n",
      "        459       0.64      0.13      0.22        69\n",
      "        460       0.07      0.01      0.01       119\n",
      "        461       0.77      0.13      0.22        79\n",
      "        462       0.69      0.23      0.35        47\n",
      "        463       0.26      0.05      0.08       104\n",
      "        464       0.65      0.34      0.45       106\n",
      "        465       0.54      0.11      0.18        64\n",
      "        466       0.57      0.28      0.38       173\n",
      "        467       0.79      0.35      0.48       107\n",
      "        468       0.82      0.11      0.20       126\n",
      "        469       0.00      0.00      0.00       114\n",
      "        470       0.94      0.79      0.86       140\n",
      "        471       0.91      0.27      0.41        79\n",
      "        472       0.39      0.28      0.33       143\n",
      "        473       0.68      0.30      0.41       158\n",
      "        474       0.38      0.07      0.11       138\n",
      "        475       0.00      0.00      0.00        59\n",
      "        476       0.57      0.32      0.41        88\n",
      "        477       0.86      0.57      0.68       176\n",
      "        478       0.94      0.71      0.81        24\n",
      "        479       0.09      0.01      0.02        92\n",
      "        480       0.82      0.50      0.62       100\n",
      "        481       0.49      0.17      0.26       103\n",
      "        482       0.52      0.23      0.32        74\n",
      "        483       0.83      0.57      0.68       105\n",
      "        484       0.29      0.02      0.04        83\n",
      "        485       0.25      0.02      0.04        82\n",
      "        486       0.38      0.11      0.17        71\n",
      "        487       0.43      0.18      0.26       120\n",
      "        488       0.20      0.01      0.02       105\n",
      "        489       0.72      0.30      0.42        87\n",
      "        490       1.00      0.81      0.90        32\n",
      "        491       0.00      0.00      0.00        69\n",
      "        492       0.00      0.00      0.00        49\n",
      "        493       0.00      0.00      0.00       117\n",
      "        494       0.50      0.16      0.25        61\n",
      "        495       0.99      0.52      0.68       344\n",
      "        496       0.37      0.19      0.25        52\n",
      "        497       0.62      0.19      0.29       137\n",
      "        498       0.29      0.04      0.07        98\n",
      "        499       0.72      0.16      0.27        79\n",
      "\n",
      "avg / total       0.67      0.33      0.43    173812\n",
      "\n",
      "Time taken to run this cell : 0:05:30.994191\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "classifier = OneVsRestClassifier(SGDClassifier(loss='log',\n",
    "                                               alpha=0.00001,\n",
    "                                               penalty='l1'), n_jobs=-1)\n",
    "classifier.fit(x_train_multilabel, y_train)\n",
    "predictions = classifier.predict (x_test_multilabel)\n",
    "\n",
    "\n",
    "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
    "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
    "\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='micro')\n",
    "recall = recall_score(y_test, predictions, average='micro')\n",
    "f1 = f1_score(y_test, predictions, average='micro')\n",
    " \n",
    "print(\"Micro-average quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='macro')\n",
    "recall = recall_score(y_test, predictions, average='macro')\n",
    "f1 = f1_score(y_test, predictions, average='macro')\n",
    " \n",
    "print(\"Macro-average quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "print (metrics.classification_report(y_test, predictions))\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6qY1LzZPU991",
    "outputId": "3b3750c1-8e13-478c-cb8a-ff91de626236"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lr_with_more_title_weight.pkl']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "joblib.dump(classifier, 'lr_with_more_title_weight.pkl') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ASSIGNMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kWQKiSJxU994",
    "outputId": "7728dac3-cda2-495c-8a6f-dd08cdc1f5fc"
   },
   "source": [
    "<ol>\n",
    "    <li>  bag of words upto 4 grams and compute the micro f1 score with Logistic regression(OvR) </li>\n",
    "    <li> Perform hyperparam tuning on alpha (or lambda) for Logistic regression to improve the performance using GridSearch  </li>\n",
    "    <li>  OneVsRestClassifier  with Linear-SVM (SGDClassifier with loss-hinge)</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Featurizing Using Bag of Words  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha=[10**-3,10**-2,10**-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.now()\n",
    "vectorizer = CountVectorizer(min_df=0.00009, max_features=200000,  \\\n",
    "                             tokenizer = lambda x: x.split(),  ngram_range=(1,4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 0:07:24.935906\n"
     ]
    }
   ],
   "source": [
    "x_train_multilabel = vectorizer.fit_transform(x_train['question'])\n",
    "x_test_multilabel = vectorizer.transform(x_test['question'])\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of train data X: (400000, 95585) Y : (400000, 500)\n",
      "Dimensions of test data X: (100000, 95585) Y: (100000, 500)\n"
     ]
    }
   ],
   "source": [
    "print(\"Dimensions of train data X:\",x_train_multilabel.shape, \"Y :\",y_train.shape)\n",
    "print(\"Dimensions of test data X:\",x_test_multilabel.shape,\"Y:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Dump and load train and test data into joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['y_test.pkl']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(x_train_multilabel, 'x_train_BOW.pkl') \n",
    "joblib.dump(x_test_multilabel, 'x_test_BOW.pkl')\n",
    "joblib.dump(y_train, 'y_train.pkl')\n",
    "joblib.dump(y_test, 'y_test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_multilabel = joblib.load('x_train_BOW.pkl')\n",
    "y_train = joblib.load('y_train.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_multilabel = joblib.load('x_test_BOW.pkl')\n",
    "y_test = joblib.load('y_test.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OneVsRestClassifier with  Logistic regression \n",
    "#### (alpha tuning using Gridsearch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsRestClassifier with SGDClassifier( penalty=l2, loss=log )==> {Logistic regression}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n",
      "{'estimator__alpha': [0.001], 'estimator__loss': ['log'], 'estimator__penalty': ['l2']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "0.01\n",
      "{'estimator__alpha': [0.01], 'estimator__loss': ['log'], 'estimator__penalty': ['l2']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "0.1\n",
      "{'estimator__alpha': [0.1], 'estimator__loss': ['log'], 'estimator__penalty': ['l2']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "\n",
      " The optimal value of alpha with penalty=l1 and loss= log is 0.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 1:59:14.455889\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# hp1={'estimator__C':alpha}\n",
    "\n",
    "cv_scores = []\n",
    "for i in alpha: \n",
    "    print(i)\n",
    "    hp1={'estimator__alpha':[i],\n",
    "         'estimator__loss':['log'],\n",
    "         'estimator__penalty':['l2']}\n",
    "    print(hp1)\n",
    "    classifier = OneVsRestClassifier(SGDClassifier())\n",
    "\n",
    "    model11 =GridSearchCV(classifier,hp1,\n",
    "                          cv=3, scoring='f1_micro',n_jobs=-1)\n",
    "    print(\"Gridsearchcv\")\n",
    "    best_model1=model11.fit(x_train_multilabel, y_train)\n",
    "    print('fit model')\n",
    "    Train_model_score=best_model1.score(x_train_multilabel, \n",
    "                                        y_train)\n",
    "#print(\"best_model1\")\n",
    "    cv_scores.append(Train_model_score.mean())\n",
    "\n",
    "fscore = [x for x in cv_scores]\n",
    "\n",
    "# determining best alpha\n",
    "optimal_alpha21 = alpha[fscore.index(max(fscore))]\n",
    "print('\\n The optimal value of alpha with penalty=l2 and loss= log is %d.' % optimal_alpha21)\n",
    "\n",
    "# Plots\n",
    "fig4 = plt.figure( facecolor='c', edgecolor='k')\n",
    "plt.plot(alpha, fscore,color='green', marker='o', linestyle='dashed',\n",
    "linewidth=2, markersize=12)\n",
    "\n",
    "for xy in zip(alpha, np.round(fscore,3)):\n",
    "    plt.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')\n",
    "\n",
    "plt.xlabel('Hyper parameter Alpha')\n",
    "plt.ylabel('F1_Score value ')\n",
    "plt.show()\n",
    "\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n"
     ]
    }
   ],
   "source": [
    "print(optimal_alpha21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='log', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False),\n",
       "          n_jobs=-1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "best_model1 = OneVsRestClassifier(SGDClassifier(loss='log', alpha=optimal_alpha21,\n",
    "                                               penalty='l2'), n_jobs=-1)\n",
    "best_model1.fit(x_train_multilabel, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_model1_LR.pkl']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(best_model1, 'best_model1_LR.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model1=joblib.load('best_model1_LR.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.2117\n",
      "Hamming loss  0.00296836\n",
      "Micro-averasge quality numbers\n",
      "Precision: 0.6491, Recall: 0.3179, F1-measure: 0.4268\n",
      "Macro-average quality numbers\n",
      "Precision: 0.4948, Recall: 0.2353, F1-measure: 0.3058\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.64      0.76      5519\n",
      "          1       0.68      0.27      0.39      8190\n",
      "          2       0.80      0.37      0.51      6529\n",
      "          3       0.82      0.42      0.55      3231\n",
      "          4       0.80      0.43      0.56      6430\n",
      "          5       0.80      0.35      0.49      2879\n",
      "          6       0.88      0.47      0.62      5086\n",
      "          7       0.87      0.56      0.68      4533\n",
      "          8       0.60      0.14      0.23      3000\n",
      "          9       0.81      0.57      0.67      2765\n",
      "         10       0.59      0.21      0.31      3051\n",
      "         11       0.71      0.33      0.45      3009\n",
      "         12       0.63      0.27      0.38      2630\n",
      "         13       0.73      0.27      0.39      1426\n",
      "         14       0.90      0.49      0.63      2548\n",
      "         15       0.63      0.13      0.22      2371\n",
      "         16       0.63      0.25      0.36       873\n",
      "         17       0.85      0.62      0.72      2151\n",
      "         18       0.63      0.26      0.37      2204\n",
      "         19       0.72      0.41      0.53       831\n",
      "         20       0.78      0.40      0.53      1860\n",
      "         21       0.28      0.14      0.18      2023\n",
      "         22       0.44      0.31      0.37      1513\n",
      "         23       0.91      0.47      0.62      1207\n",
      "         24       0.49      0.36      0.41       506\n",
      "         25       0.60      0.29      0.40       425\n",
      "         26       0.59      0.42      0.49       793\n",
      "         27       0.57      0.38      0.46      1291\n",
      "         28       0.70      0.32      0.44      1208\n",
      "         29       0.36      0.09      0.14       406\n",
      "         30       0.58      0.14      0.23       504\n",
      "         31       0.28      0.15      0.20       732\n",
      "         32       0.57      0.27      0.37       441\n",
      "         33       0.51      0.30      0.37      1645\n",
      "         34       0.71      0.23      0.35      1058\n",
      "         35       0.83      0.58      0.68       946\n",
      "         36       0.60      0.22      0.32       644\n",
      "         37       0.98      0.63      0.77       136\n",
      "         38       0.60      0.45      0.51       570\n",
      "         39       0.85      0.22      0.34       766\n",
      "         40       0.60      0.31      0.40      1132\n",
      "         41       0.46      0.22      0.30       174\n",
      "         42       0.69      0.43      0.53       210\n",
      "         43       0.76      0.39      0.52       433\n",
      "         44       0.65      0.47      0.55       626\n",
      "         45       0.65      0.31      0.42       852\n",
      "         46       0.71      0.43      0.53       534\n",
      "         47       0.27      0.23      0.25       350\n",
      "         48       0.72      0.50      0.59       496\n",
      "         49       0.79      0.64      0.71       785\n",
      "         50       0.20      0.13      0.16       475\n",
      "         51       0.28      0.15      0.19       305\n",
      "         52       0.34      0.06      0.11       251\n",
      "         53       0.67      0.38      0.49       914\n",
      "         54       0.43      0.22      0.29       728\n",
      "         55       0.00      0.00      0.00       258\n",
      "         56       0.38      0.27      0.32       821\n",
      "         57       0.39      0.12      0.19       541\n",
      "         58       0.80      0.24      0.37       748\n",
      "         59       0.95      0.57      0.71       724\n",
      "         60       0.27      0.07      0.11       660\n",
      "         61       0.85      0.19      0.31       235\n",
      "         62       0.88      0.69      0.78       718\n",
      "         63       0.83      0.55      0.66       468\n",
      "         64       0.49      0.44      0.47       191\n",
      "         65       0.25      0.18      0.21       429\n",
      "         66       0.26      0.14      0.19       415\n",
      "         67       0.68      0.46      0.55       274\n",
      "         68       0.84      0.47      0.61       510\n",
      "         69       0.65      0.42      0.51       466\n",
      "         70       0.26      0.13      0.18       305\n",
      "         71       0.37      0.17      0.23       247\n",
      "         72       0.75      0.41      0.53       401\n",
      "         73       0.90      0.65      0.76        86\n",
      "         74       0.71      0.34      0.46       120\n",
      "         75       0.90      0.62      0.73       129\n",
      "         76       0.46      0.01      0.02       473\n",
      "         77       0.36      0.35      0.35       143\n",
      "         78       0.75      0.38      0.51       347\n",
      "         79       0.69      0.21      0.32       479\n",
      "         80       0.49      0.39      0.44       279\n",
      "         81       0.75      0.11      0.19       461\n",
      "         82       0.20      0.08      0.12       298\n",
      "         83       0.71      0.41      0.52       396\n",
      "         84       0.46      0.37      0.41       184\n",
      "         85       0.45      0.27      0.34       573\n",
      "         86       0.24      0.09      0.13       325\n",
      "         87       0.46      0.24      0.32       273\n",
      "         88       0.32      0.25      0.28       135\n",
      "         89       0.25      0.16      0.20       232\n",
      "         90       0.49      0.40      0.44       409\n",
      "         91       0.62      0.34      0.44       420\n",
      "         92       0.75      0.46      0.57       408\n",
      "         93       0.51      0.48      0.49       241\n",
      "         94       0.31      0.10      0.16       211\n",
      "         95       0.27      0.18      0.22       277\n",
      "         96       0.29      0.07      0.11       410\n",
      "         97       0.88      0.16      0.27       501\n",
      "         98       0.79      0.57      0.66       136\n",
      "         99       0.49      0.29      0.37       239\n",
      "        100       0.47      0.18      0.26       324\n",
      "        101       0.90      0.50      0.64       277\n",
      "        102       0.90      0.64      0.75       613\n",
      "        103       0.44      0.20      0.27       157\n",
      "        104       0.21      0.15      0.17       295\n",
      "        105       0.67      0.36      0.47       334\n",
      "        106       0.78      0.05      0.10       335\n",
      "        107       0.75      0.49      0.59       389\n",
      "        108       0.53      0.34      0.41       251\n",
      "        109       0.48      0.40      0.43       317\n",
      "        110       0.47      0.09      0.14       187\n",
      "        111       0.35      0.06      0.10       140\n",
      "        112       0.43      0.25      0.32       154\n",
      "        113       0.58      0.14      0.22       332\n",
      "        114       0.42      0.29      0.35       323\n",
      "        115       0.41      0.19      0.26       344\n",
      "        116       0.72      0.45      0.55       370\n",
      "        117       0.54      0.19      0.29       313\n",
      "        118       0.80      0.46      0.58       874\n",
      "        119       0.34      0.24      0.28       293\n",
      "        120       0.13      0.04      0.05       200\n",
      "        121       0.75      0.42      0.54       463\n",
      "        122       0.36      0.24      0.29       119\n",
      "        123       0.25      0.00      0.01       256\n",
      "        124       0.91      0.62      0.74       195\n",
      "        125       0.39      0.20      0.26       138\n",
      "        126       0.79      0.51      0.62       376\n",
      "        127       0.17      0.06      0.09       122\n",
      "        128       0.20      0.08      0.11       252\n",
      "        129       0.39      0.10      0.16       144\n",
      "        130       0.41      0.07      0.12       150\n",
      "        131       0.16      0.03      0.06       210\n",
      "        132       0.58      0.22      0.32       361\n",
      "        133       0.94      0.39      0.55       453\n",
      "        134       0.89      0.66      0.76       124\n",
      "        135       0.25      0.01      0.02        91\n",
      "        136       0.53      0.30      0.39       128\n",
      "        137       0.46      0.33      0.39       218\n",
      "        138       0.38      0.08      0.13       243\n",
      "        139       0.33      0.24      0.28       149\n",
      "        140       0.68      0.32      0.44       318\n",
      "        141       0.18      0.15      0.17       159\n",
      "        142       0.65      0.39      0.49       274\n",
      "        143       0.85      0.61      0.71       362\n",
      "        144       0.48      0.20      0.29       118\n",
      "        145       0.58      0.37      0.45       164\n",
      "        146       0.57      0.29      0.38       461\n",
      "        147       0.66      0.45      0.53       159\n",
      "        148       0.35      0.16      0.22       166\n",
      "        149       0.97      0.31      0.47       346\n",
      "        150       0.61      0.07      0.12       350\n",
      "        151       0.88      0.42      0.57        55\n",
      "        152       0.72      0.46      0.56       387\n",
      "        153       0.39      0.06      0.10       150\n",
      "        154       0.52      0.06      0.11       281\n",
      "        155       0.29      0.16      0.21       202\n",
      "        156       0.73      0.55      0.63       130\n",
      "        157       0.28      0.11      0.15       245\n",
      "        158       0.89      0.47      0.62       177\n",
      "        159       0.43      0.28      0.34       130\n",
      "        160       0.49      0.25      0.33       336\n",
      "        161       0.85      0.50      0.63       220\n",
      "        162       0.18      0.10      0.13       229\n",
      "        163       0.90      0.28      0.43       316\n",
      "        164       0.71      0.28      0.41       283\n",
      "        165       0.54      0.28      0.37       197\n",
      "        166       0.31      0.20      0.24       101\n",
      "        167       0.39      0.24      0.30       231\n",
      "        168       0.44      0.21      0.28       370\n",
      "        169       0.42      0.28      0.33       258\n",
      "        170       0.23      0.09      0.13       101\n",
      "        171       0.46      0.25      0.32        89\n",
      "        172       0.39      0.34      0.36       193\n",
      "        173       0.41      0.28      0.34       309\n",
      "        174       0.50      0.12      0.19       172\n",
      "        175       0.90      0.75      0.82        95\n",
      "        176       0.93      0.43      0.59       346\n",
      "        177       0.95      0.24      0.39       322\n",
      "        178       0.57      0.43      0.49       232\n",
      "        179       0.54      0.06      0.10       125\n",
      "        180       0.43      0.21      0.28       145\n",
      "        181       0.47      0.19      0.28        77\n",
      "        182       0.13      0.07      0.09       182\n",
      "        183       0.55      0.35      0.43       257\n",
      "        184       0.13      0.06      0.08       216\n",
      "        185       0.29      0.14      0.19       242\n",
      "        186       0.28      0.19      0.23       165\n",
      "        187       0.77      0.46      0.58       263\n",
      "        188       0.31      0.16      0.21       174\n",
      "        189       0.78      0.33      0.46       136\n",
      "        190       0.94      0.36      0.52       202\n",
      "        191       0.40      0.15      0.22       134\n",
      "        192       0.63      0.31      0.41       230\n",
      "        193       0.31      0.18      0.23        90\n",
      "        194       0.59      0.52      0.56       185\n",
      "        195       0.08      0.04      0.05       156\n",
      "        196       0.23      0.07      0.11       160\n",
      "        197       0.10      0.02      0.03       266\n",
      "        198       0.38      0.10      0.16       284\n",
      "        199       0.15      0.03      0.06       145\n",
      "        200       0.93      0.52      0.67       212\n",
      "        201       0.49      0.23      0.31       317\n",
      "        202       0.73      0.43      0.54       427\n",
      "        203       0.25      0.14      0.18       232\n",
      "        204       0.40      0.25      0.31       217\n",
      "        205       0.48      0.38      0.42       527\n",
      "        206       0.10      0.04      0.06       124\n",
      "        207       0.34      0.16      0.21       103\n",
      "        208       0.81      0.34      0.48       287\n",
      "        209       0.25      0.11      0.15       193\n",
      "        210       0.69      0.25      0.37       220\n",
      "        211       0.64      0.06      0.12       140\n",
      "        212       0.08      0.05      0.06       161\n",
      "        213       0.55      0.29      0.38        72\n",
      "        214       0.60      0.43      0.50       396\n",
      "        215       0.77      0.17      0.28       134\n",
      "        216       0.36      0.07      0.12       400\n",
      "        217       0.44      0.25      0.32        75\n",
      "        218       0.97      0.50      0.66       219\n",
      "        219       0.79      0.28      0.41       210\n",
      "        220       0.93      0.37      0.53       298\n",
      "        221       0.96      0.41      0.58       266\n",
      "        222       0.70      0.29      0.41       290\n",
      "        223       0.22      0.05      0.09       128\n",
      "        224       0.75      0.36      0.49       159\n",
      "        225       0.36      0.22      0.27       164\n",
      "        226       0.56      0.34      0.42       144\n",
      "        227       0.54      0.41      0.46       276\n",
      "        228       0.07      0.02      0.03       235\n",
      "        229       0.23      0.03      0.05       216\n",
      "        230       0.36      0.25      0.30       228\n",
      "        231       0.67      0.45      0.54        64\n",
      "        232       0.15      0.07      0.09       103\n",
      "        233       0.72      0.20      0.31       216\n",
      "        234       0.60      0.13      0.21       116\n",
      "        235       0.57      0.43      0.49        77\n",
      "        236       0.91      0.60      0.72        67\n",
      "        237       0.56      0.05      0.08       218\n",
      "        238       0.15      0.09      0.12       139\n",
      "        239       0.19      0.03      0.05        94\n",
      "        240       0.39      0.16      0.22        77\n",
      "        241       0.47      0.10      0.17       167\n",
      "        242       0.77      0.23      0.36        86\n",
      "        243       0.48      0.19      0.27        58\n",
      "        244       0.45      0.22      0.29       269\n",
      "        245       0.17      0.06      0.09       112\n",
      "        246       0.96      0.54      0.69       255\n",
      "        247       0.39      0.21      0.27        58\n",
      "        248       0.36      0.06      0.11        81\n",
      "        249       0.03      0.01      0.01       131\n",
      "        250       0.30      0.23      0.26        93\n",
      "        251       0.57      0.28      0.38       154\n",
      "        252       0.20      0.05      0.09       129\n",
      "        253       0.55      0.35      0.43        83\n",
      "        254       0.22      0.10      0.14       191\n",
      "        255       0.14      0.07      0.09       219\n",
      "        256       0.07      0.02      0.03       130\n",
      "        257       0.41      0.31      0.35        93\n",
      "        258       0.63      0.35      0.45       217\n",
      "        259       0.24      0.11      0.15       141\n",
      "        260       0.89      0.12      0.21       143\n",
      "        261       0.53      0.11      0.18       219\n",
      "        262       0.42      0.32      0.36       107\n",
      "        263       0.32      0.32      0.32       236\n",
      "        264       0.21      0.19      0.20       119\n",
      "        265       0.32      0.24      0.27        72\n",
      "        266       0.18      0.09      0.12        70\n",
      "        267       0.26      0.13      0.17       107\n",
      "        268       0.61      0.33      0.43       169\n",
      "        269       0.22      0.15      0.18       129\n",
      "        270       0.70      0.50      0.58       159\n",
      "        271       0.48      0.17      0.25       190\n",
      "        272       0.57      0.21      0.31       248\n",
      "        273       0.93      0.43      0.59       264\n",
      "        274       0.88      0.50      0.64       105\n",
      "        275       0.09      0.03      0.04       104\n",
      "        276       0.09      0.02      0.03       115\n",
      "        277       0.86      0.51      0.64       170\n",
      "        278       0.63      0.19      0.29       145\n",
      "        279       0.88      0.30      0.45       230\n",
      "        280       0.54      0.33      0.41        80\n",
      "        281       0.68      0.47      0.56       217\n",
      "        282       0.74      0.38      0.50       175\n",
      "        283       0.37      0.11      0.17       269\n",
      "        284       0.61      0.30      0.40        74\n",
      "        285       0.86      0.36      0.51       206\n",
      "        286       0.92      0.43      0.58       227\n",
      "        287       0.77      0.25      0.38       130\n",
      "        288       0.28      0.06      0.10       129\n",
      "        289       0.17      0.06      0.09        80\n",
      "        290       0.15      0.12      0.14        99\n",
      "        291       0.83      0.21      0.34       208\n",
      "        292       0.37      0.10      0.16        67\n",
      "        293       0.78      0.33      0.46       109\n",
      "        294       0.32      0.33      0.33       140\n",
      "        295       0.17      0.14      0.15       241\n",
      "        296       0.23      0.19      0.21        72\n",
      "        297       0.28      0.12      0.17       107\n",
      "        298       0.67      0.43      0.52        61\n",
      "        299       0.86      0.39      0.54        77\n",
      "        300       0.18      0.09      0.12       111\n",
      "        301       0.00      0.00      0.00       126\n",
      "        302       0.33      0.01      0.03        73\n",
      "        303       0.53      0.40      0.46       176\n",
      "        304       0.96      0.46      0.62       230\n",
      "        305       0.94      0.40      0.57       156\n",
      "        306       0.43      0.36      0.39       146\n",
      "        307       0.28      0.11      0.16        98\n",
      "        308       0.08      0.04      0.05        78\n",
      "        309       0.33      0.02      0.04        94\n",
      "        310       0.56      0.31      0.40       162\n",
      "        311       0.67      0.37      0.48       116\n",
      "        312       0.47      0.25      0.32        57\n",
      "        313       0.67      0.03      0.06        65\n",
      "        314       0.46      0.30      0.37       138\n",
      "        315       0.48      0.24      0.32       195\n",
      "        316       0.41      0.33      0.37        69\n",
      "        317       0.19      0.08      0.11       134\n",
      "        318       0.41      0.30      0.35       148\n",
      "        319       0.70      0.29      0.41       161\n",
      "        320       0.18      0.22      0.20       104\n",
      "        321       0.81      0.43      0.56       156\n",
      "        322       0.56      0.31      0.40       134\n",
      "        323       0.49      0.41      0.45       232\n",
      "        324       0.37      0.18      0.25        92\n",
      "        325       0.34      0.26      0.30       197\n",
      "        326       0.09      0.02      0.04       126\n",
      "        327       0.29      0.04      0.08       115\n",
      "        328       0.97      0.31      0.47       198\n",
      "        329       0.53      0.32      0.40       125\n",
      "        330       0.57      0.10      0.17        81\n",
      "        331       0.22      0.06      0.10        94\n",
      "        332       0.33      0.02      0.03        56\n",
      "        333       0.12      0.09      0.10       260\n",
      "        334       0.67      0.07      0.12        60\n",
      "        335       0.28      0.17      0.21       110\n",
      "        336       0.65      0.42      0.51        71\n",
      "        337       0.11      0.06      0.08        66\n",
      "        338       0.46      0.33      0.38       150\n",
      "        339       0.00      0.00      0.00        54\n",
      "        340       0.89      0.33      0.49       195\n",
      "        341       0.75      0.19      0.30        79\n",
      "        342       0.33      0.32      0.32        38\n",
      "        343       0.57      0.30      0.39        43\n",
      "        344       0.50      0.21      0.29        68\n",
      "        345       0.60      0.38      0.47        73\n",
      "        346       0.07      0.03      0.04       116\n",
      "        347       0.93      0.23      0.36       111\n",
      "        348       0.23      0.08      0.12        63\n",
      "        349       0.89      0.39      0.55       104\n",
      "        350       0.54      0.30      0.38        44\n",
      "        351       0.50      0.15      0.23        40\n",
      "        352       1.00      0.18      0.31       136\n",
      "        353       0.48      0.28      0.35        54\n",
      "        354       0.27      0.04      0.08       134\n",
      "        355       0.48      0.26      0.34       120\n",
      "        356       0.42      0.23      0.30       228\n",
      "        357       0.53      0.22      0.31       269\n",
      "        358       0.69      0.30      0.42        80\n",
      "        359       0.65      0.25      0.36       140\n",
      "        360       0.37      0.18      0.24       125\n",
      "        361       0.88      0.33      0.48       169\n",
      "        362       0.12      0.05      0.07        56\n",
      "        363       0.95      0.47      0.63       154\n",
      "        364       0.33      0.05      0.09        58\n",
      "        365       0.22      0.20      0.21        71\n",
      "        366       1.00      0.37      0.54        54\n",
      "        367       0.19      0.05      0.08       116\n",
      "        368       0.25      0.02      0.03        54\n",
      "        369       0.12      0.04      0.06        71\n",
      "        370       0.10      0.03      0.05        61\n",
      "        371       0.40      0.06      0.10        71\n",
      "        372       0.61      0.33      0.42        52\n",
      "        373       0.60      0.17      0.27       150\n",
      "        374       0.39      0.23      0.29        93\n",
      "        375       0.33      0.06      0.10        67\n",
      "        376       0.00      0.00      0.00        76\n",
      "        377       0.66      0.18      0.28       106\n",
      "        378       0.17      0.01      0.02        86\n",
      "        379       0.20      0.07      0.11        14\n",
      "        380       0.94      0.14      0.24       122\n",
      "        381       0.11      0.05      0.07       104\n",
      "        382       0.19      0.08      0.11        66\n",
      "        383       0.49      0.26      0.34       110\n",
      "        384       0.20      0.01      0.02       155\n",
      "        385       0.22      0.04      0.07        50\n",
      "        386       0.22      0.17      0.19        64\n",
      "        387       0.19      0.03      0.06        93\n",
      "        388       0.54      0.20      0.29       102\n",
      "        389       0.10      0.02      0.03       108\n",
      "        390       0.95      0.32      0.48       178\n",
      "        391       0.58      0.16      0.25       115\n",
      "        392       0.50      0.21      0.30        42\n",
      "        393       0.00      0.00      0.00       134\n",
      "        394       0.06      0.01      0.02       112\n",
      "        395       0.45      0.21      0.29       176\n",
      "        396       0.19      0.02      0.04       125\n",
      "        397       0.69      0.21      0.32       224\n",
      "        398       0.85      0.27      0.41        63\n",
      "        399       0.00      0.00      0.00        59\n",
      "        400       0.42      0.29      0.34        63\n",
      "        401       0.23      0.16      0.19        98\n",
      "        402       0.35      0.07      0.12       162\n",
      "        403       0.33      0.20      0.25        83\n",
      "        404       0.76      0.68      0.72        19\n",
      "        405       0.19      0.12      0.15        92\n",
      "        406       0.60      0.22      0.32        41\n",
      "        407       0.74      0.33      0.45        43\n",
      "        408       0.66      0.18      0.28       160\n",
      "        409       0.28      0.22      0.25        50\n",
      "        410       0.00      0.00      0.00        19\n",
      "        411       0.28      0.15      0.20       175\n",
      "        412       0.29      0.06      0.09        72\n",
      "        413       0.40      0.04      0.08        95\n",
      "        414       0.17      0.10      0.13        97\n",
      "        415       0.20      0.10      0.14        48\n",
      "        416       0.43      0.29      0.35        83\n",
      "        417       0.11      0.03      0.04        40\n",
      "        418       0.25      0.10      0.14        91\n",
      "        419       0.42      0.28      0.34        90\n",
      "        420       0.18      0.11      0.14        37\n",
      "        421       0.10      0.05      0.06        66\n",
      "        422       0.54      0.37      0.44        73\n",
      "        423       0.41      0.20      0.27        56\n",
      "        424       0.95      0.58      0.72        33\n",
      "        425       0.05      0.01      0.02        76\n",
      "        426       0.19      0.06      0.09        81\n",
      "        427       1.00      0.32      0.48       150\n",
      "        428       1.00      0.52      0.68        29\n",
      "        429       1.00      0.07      0.13       389\n",
      "        430       0.62      0.20      0.31       167\n",
      "        431       0.32      0.06      0.10       123\n",
      "        432       0.37      0.26      0.30        39\n",
      "        433       0.42      0.29      0.35        82\n",
      "        434       1.00      0.42      0.60        66\n",
      "        435       0.60      0.39      0.47        93\n",
      "        436       0.55      0.20      0.29        87\n",
      "        437       0.24      0.05      0.08        86\n",
      "        438       0.81      0.34      0.48       104\n",
      "        439       0.55      0.11      0.18       100\n",
      "        440       0.36      0.04      0.06       141\n",
      "        441       0.36      0.32      0.34       110\n",
      "        442       0.26      0.17      0.21       123\n",
      "        443       0.00      0.00      0.00        71\n",
      "        444       0.21      0.03      0.05       109\n",
      "        445       0.22      0.12      0.16        48\n",
      "        446       0.33      0.20      0.25        76\n",
      "        447       0.17      0.13      0.15        38\n",
      "        448       0.69      0.49      0.58        81\n",
      "        449       0.51      0.20      0.29       132\n",
      "        450       0.49      0.28      0.36        81\n",
      "        451       0.80      0.16      0.26        76\n",
      "        452       0.00      0.00      0.00        44\n",
      "        453       0.12      0.02      0.04        44\n",
      "        454       0.73      0.31      0.44        70\n",
      "        455       0.22      0.10      0.14       155\n",
      "        456       0.33      0.21      0.26        43\n",
      "        457       0.40      0.22      0.29        72\n",
      "        458       0.17      0.06      0.09        62\n",
      "        459       0.50      0.12      0.19        69\n",
      "        460       0.05      0.03      0.03       119\n",
      "        461       0.72      0.23      0.35        79\n",
      "        462       0.31      0.11      0.16        47\n",
      "        463       0.21      0.07      0.10       104\n",
      "        464       0.59      0.36      0.45       106\n",
      "        465       0.62      0.12      0.21        64\n",
      "        466       0.58      0.24      0.34       173\n",
      "        467       0.66      0.23      0.34       107\n",
      "        468       0.48      0.08      0.14       126\n",
      "        469       0.00      0.00      0.00       114\n",
      "        470       0.95      0.51      0.67       140\n",
      "        471       0.62      0.06      0.11        79\n",
      "        472       0.30      0.22      0.26       143\n",
      "        473       0.50      0.18      0.27       158\n",
      "        474       0.27      0.05      0.09       138\n",
      "        475       0.07      0.03      0.04        59\n",
      "        476       0.62      0.28      0.39        88\n",
      "        477       0.85      0.42      0.56       176\n",
      "        478       0.93      0.54      0.68        24\n",
      "        479       0.18      0.04      0.07        92\n",
      "        480       0.83      0.30      0.44       100\n",
      "        481       0.41      0.19      0.26       103\n",
      "        482       0.30      0.27      0.28        74\n",
      "        483       0.82      0.30      0.44       105\n",
      "        484       0.03      0.01      0.02        83\n",
      "        485       0.10      0.02      0.04        82\n",
      "        486       0.35      0.15      0.22        71\n",
      "        487       0.36      0.20      0.26       120\n",
      "        488       0.20      0.02      0.03       105\n",
      "        489       0.62      0.23      0.34        87\n",
      "        490       0.95      0.59      0.73        32\n",
      "        491       0.00      0.00      0.00        69\n",
      "        492       0.25      0.02      0.04        49\n",
      "        493       0.06      0.01      0.01       117\n",
      "        494       0.43      0.05      0.09        61\n",
      "        495       1.00      0.08      0.15       344\n",
      "        496       0.31      0.15      0.21        52\n",
      "        497       0.57      0.12      0.19       137\n",
      "        498       0.42      0.05      0.09        98\n",
      "        499       0.71      0.06      0.12        79\n",
      "\n",
      "avg / total       0.64      0.32      0.41    173812\n",
      "\n",
      "Time taken to run this cell : 0:15:15.119457\n"
     ]
    }
   ],
   "source": [
    "predictions = best_model1.predict (x_test_multilabel)\n",
    "\n",
    "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
    "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='micro')\n",
    "recall = recall_score(y_test, predictions, average='micro')\n",
    "f1 = f1_score(y_test, predictions, average='micro')\n",
    " \n",
    "print(\"Micro-averasge quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='macro')\n",
    "recall = recall_score(y_test, predictions, average='macro')\n",
    "f1 = f1_score(y_test, predictions, average='macro')\n",
    " \n",
    "print(\"Macro-average quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "print (metrics.classification_report(y_test, predictions)) #printing classification report for all 500 labels\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsRestClassifier with Logistic regression( penalty=l1 ) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n",
      "{'estimator__alpha': [0.001], 'estimator__loss': ['log'], 'estimator__penalty': ['l1']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "0.01\n",
      "{'estimator__alpha': [0.01], 'estimator__loss': ['log'], 'estimator__penalty': ['l1']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "0.1\n",
      "{'estimator__alpha': [0.1], 'estimator__loss': ['log'], 'estimator__penalty': ['l1']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "\n",
      " The optimal value of alpha with penalty=l1 and loss= log is 0.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 2:56:17.727412\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# hp1={'estimator__C':alpha}\n",
    "\n",
    "cv_scores = []\n",
    "for i in alpha: \n",
    "    print(i)\n",
    "    hp1={'estimator__alpha':[i],\n",
    "         'estimator__loss':['log'],\n",
    "         'estimator__penalty':['l1']}\n",
    "    print(hp1)\n",
    "    classifier = OneVsRestClassifier(SGDClassifier())\n",
    "\n",
    "    model11 =GridSearchCV(classifier,hp1,\n",
    "                          cv=3, scoring='f1_micro',n_jobs=-1)\n",
    "    print(\"Gridsearchcv\")\n",
    "    best_model1=model11.fit(x_train_multilabel, y_train)\n",
    "    print('fit model')\n",
    "    Train_model_score=best_model1.score(x_train_multilabel, \n",
    "                                        y_train)\n",
    "#print(\"best_model1\")\n",
    "    cv_scores.append(Train_model_score.mean())\n",
    "\n",
    "fscore = [x for x in cv_scores]\n",
    "\n",
    "# determining best alpha\n",
    "optimal_alpha22 = alpha[fscore.index(max(fscore))]\n",
    "print('\\n The optimal value of alpha with penalty=l1 and loss= log is %d.' % optimal_alpha22)\n",
    "\n",
    "# Plots\n",
    "fig4 = plt.figure( facecolor='c', edgecolor='k')\n",
    "plt.plot(alpha, fscore,color='green', marker='o', linestyle='dashed',\n",
    "linewidth=2, markersize=12)\n",
    "\n",
    "for xy in zip(alpha, np.round(fscore,3)):\n",
    "    plt.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')\n",
    "\n",
    "plt.xlabel('Hyper parameter Alpha')\n",
    "plt.ylabel('F1_Score value ')\n",
    "plt.show()\n",
    "\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='log', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l1', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False),\n",
       "          n_jobs=-1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "best_model2 = OneVsRestClassifier(SGDClassifier(loss='log', alpha=optimal_alpha22,\n",
    "                                               penalty='l1'), n_jobs=-1)\n",
    "best_model2.fit(x_train_multilabel, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_model2_LR.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(best_model2, 'best_model2_LR.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model2=joblib.load('best_model2_LR.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression with l1 penalty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.1879\n",
      "Hamming loss  0.00319694\n",
      "Micro-average quality numbers\n",
      "Precision: 0.5718, Recall: 0.3201, F1-measure: 0.4104\n",
      "Macro-average quality numbers\n",
      "Precision: 0.4113, Recall: 0.2385, F1-measure: 0.2830\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.68      0.68      0.68      5519\n",
      "          1       0.57      0.20      0.29      8190\n",
      "          2       0.75      0.33      0.46      6529\n",
      "          3       0.76      0.40      0.52      3231\n",
      "          4       0.70      0.42      0.53      6430\n",
      "          5       0.62      0.39      0.48      2879\n",
      "          6       0.72      0.55      0.62      5086\n",
      "          7       0.83      0.60      0.69      4533\n",
      "          8       0.48      0.14      0.22      3000\n",
      "          9       0.75      0.48      0.59      2765\n",
      "         10       0.57      0.14      0.23      3051\n",
      "         11       0.66      0.37      0.48      3009\n",
      "         12       0.61      0.22      0.32      2630\n",
      "         13       0.54      0.14      0.22      1426\n",
      "         14       0.81      0.61      0.70      2548\n",
      "         15       0.64      0.12      0.20      2371\n",
      "         16       0.49      0.28      0.35       873\n",
      "         17       0.74      0.68      0.71      2151\n",
      "         18       0.63      0.22      0.33      2204\n",
      "         19       0.62      0.42      0.50       831\n",
      "         20       0.70      0.51      0.59      1860\n",
      "         21       0.24      0.11      0.15      2023\n",
      "         22       0.34      0.25      0.28      1513\n",
      "         23       0.90      0.45      0.60      1207\n",
      "         24       0.47      0.33      0.39       506\n",
      "         25       0.67      0.32      0.43       425\n",
      "         26       0.46      0.41      0.44       793\n",
      "         27       0.54      0.31      0.39      1291\n",
      "         28       0.62      0.32      0.42      1208\n",
      "         29       0.26      0.09      0.14       406\n",
      "         30       0.50      0.26      0.35       504\n",
      "         31       0.26      0.14      0.18       732\n",
      "         32       0.47      0.35      0.40       441\n",
      "         33       0.35      0.11      0.17      1645\n",
      "         34       0.51      0.34      0.41      1058\n",
      "         35       0.72      0.59      0.65       946\n",
      "         36       0.48      0.29      0.36       644\n",
      "         37       0.61      0.77      0.68       136\n",
      "         38       0.56      0.43      0.49       570\n",
      "         39       0.76      0.36      0.48       766\n",
      "         40       0.53      0.27      0.35      1132\n",
      "         41       0.33      0.22      0.27       174\n",
      "         42       0.47      0.51      0.49       210\n",
      "         43       0.62      0.51      0.56       433\n",
      "         44       0.57      0.47      0.52       626\n",
      "         45       0.39      0.28      0.33       852\n",
      "         46       0.66      0.38      0.48       534\n",
      "         47       0.20      0.24      0.22       350\n",
      "         48       0.52      0.60      0.55       496\n",
      "         49       0.79      0.59      0.67       785\n",
      "         50       0.16      0.15      0.16       475\n",
      "         51       0.24      0.12      0.16       305\n",
      "         52       0.16      0.09      0.11       251\n",
      "         53       0.59      0.39      0.47       914\n",
      "         54       0.43      0.18      0.25       728\n",
      "         55       0.00      0.00      0.00       258\n",
      "         56       0.37      0.14      0.20       821\n",
      "         57       0.38      0.14      0.20       541\n",
      "         58       0.54      0.33      0.41       748\n",
      "         59       0.87      0.67      0.76       724\n",
      "         60       0.23      0.09      0.13       660\n",
      "         61       0.63      0.29      0.39       235\n",
      "         62       0.89      0.68      0.77       718\n",
      "         63       0.84      0.49      0.62       468\n",
      "         64       0.49      0.46      0.47       191\n",
      "         65       0.19      0.16      0.17       429\n",
      "         66       0.17      0.10      0.12       415\n",
      "         67       0.66      0.51      0.58       274\n",
      "         68       0.84      0.50      0.63       510\n",
      "         69       0.63      0.44      0.52       466\n",
      "         70       0.20      0.18      0.19       305\n",
      "         71       0.38      0.17      0.23       247\n",
      "         72       0.71      0.41      0.52       401\n",
      "         73       0.93      0.78      0.85        86\n",
      "         74       0.69      0.31      0.43       120\n",
      "         75       0.77      0.79      0.78       129\n",
      "         76       0.04      0.01      0.02       473\n",
      "         77       0.30      0.31      0.31       143\n",
      "         78       0.77      0.41      0.54       347\n",
      "         79       0.55      0.23      0.33       479\n",
      "         80       0.35      0.32      0.33       279\n",
      "         81       0.80      0.11      0.20       461\n",
      "         82       0.13      0.04      0.07       298\n",
      "         83       0.70      0.40      0.51       396\n",
      "         84       0.37      0.33      0.35       184\n",
      "         85       0.30      0.18      0.23       573\n",
      "         86       0.11      0.01      0.02       325\n",
      "         87       0.51      0.23      0.32       273\n",
      "         88       0.27      0.21      0.24       135\n",
      "         89       0.19      0.15      0.17       232\n",
      "         90       0.48      0.35      0.40       409\n",
      "         91       0.51      0.36      0.42       420\n",
      "         92       0.63      0.60      0.62       408\n",
      "         93       0.58      0.47      0.52       241\n",
      "         94       0.23      0.09      0.12       211\n",
      "         95       0.14      0.19      0.16       277\n",
      "         96       0.14      0.13      0.13       410\n",
      "         97       0.82      0.15      0.25       501\n",
      "         98       0.69      0.63      0.66       136\n",
      "         99       0.49      0.25      0.33       239\n",
      "        100       0.34      0.09      0.14       324\n",
      "        101       0.54      0.50      0.52       277\n",
      "        102       0.82      0.75      0.78       613\n",
      "        103       0.45      0.18      0.26       157\n",
      "        104       0.17      0.09      0.12       295\n",
      "        105       0.60      0.40      0.48       334\n",
      "        106       0.07      0.01      0.01       335\n",
      "        107       0.77      0.47      0.59       389\n",
      "        108       0.18      0.25      0.21       251\n",
      "        109       0.42      0.29      0.34       317\n",
      "        110       0.52      0.06      0.11       187\n",
      "        111       0.24      0.15      0.18       140\n",
      "        112       0.12      0.03      0.04       154\n",
      "        113       0.40      0.39      0.40       332\n",
      "        114       0.40      0.20      0.27       323\n",
      "        115       0.35      0.09      0.14       344\n",
      "        116       0.59      0.48      0.53       370\n",
      "        117       0.50      0.17      0.26       313\n",
      "        118       0.79      0.53      0.63       874\n",
      "        119       0.36      0.16      0.22       293\n",
      "        120       0.01      0.01      0.01       200\n",
      "        121       0.75      0.42      0.54       463\n",
      "        122       0.27      0.32      0.29       119\n",
      "        123       0.00      0.00      0.00       256\n",
      "        124       0.87      0.73      0.79       195\n",
      "        125       0.34      0.17      0.22       138\n",
      "        126       0.62      0.50      0.55       376\n",
      "        127       0.20      0.07      0.11       122\n",
      "        128       0.17      0.06      0.09       252\n",
      "        129       0.50      0.02      0.04       144\n",
      "        130       0.09      0.02      0.03       150\n",
      "        131       0.10      0.01      0.02       210\n",
      "        132       0.43      0.08      0.14       361\n",
      "        133       0.89      0.55      0.68       453\n",
      "        134       0.83      0.70      0.76       124\n",
      "        135       0.00      0.00      0.00        91\n",
      "        136       0.18      0.30      0.23       128\n",
      "        137       0.44      0.29      0.35       218\n",
      "        138       0.09      0.00      0.01       243\n",
      "        139       0.32      0.21      0.25       149\n",
      "        140       0.68      0.29      0.41       318\n",
      "        141       0.10      0.14      0.12       159\n",
      "        142       0.69      0.30      0.42       274\n",
      "        143       0.78      0.79      0.79       362\n",
      "        144       0.50      0.24      0.32       118\n",
      "        145       0.61      0.39      0.48       164\n",
      "        146       0.57      0.25      0.35       461\n",
      "        147       0.61      0.42      0.50       159\n",
      "        148       0.35      0.13      0.19       166\n",
      "        149       0.92      0.56      0.70       346\n",
      "        150       0.42      0.01      0.03       350\n",
      "        151       0.79      0.62      0.69        55\n",
      "        152       0.75      0.44      0.56       387\n",
      "        153       0.00      0.00      0.00       150\n",
      "        154       0.40      0.19      0.26       281\n",
      "        155       0.25      0.12      0.17       202\n",
      "        156       0.62      0.65      0.64       130\n",
      "        157       0.30      0.11      0.16       245\n",
      "        158       0.83      0.48      0.61       177\n",
      "        159       0.42      0.24      0.31       130\n",
      "        160       0.46      0.17      0.25       336\n",
      "        161       0.81      0.60      0.69       220\n",
      "        162       0.10      0.03      0.05       229\n",
      "        163       0.85      0.46      0.59       316\n",
      "        164       0.38      0.23      0.29       283\n",
      "        165       0.56      0.28      0.37       197\n",
      "        166       0.12      0.08      0.10       101\n",
      "        167       0.34      0.22      0.27       231\n",
      "        168       0.31      0.12      0.17       370\n",
      "        169       0.27      0.28      0.27       258\n",
      "        170       0.12      0.05      0.07       101\n",
      "        171       0.37      0.21      0.27        89\n",
      "        172       0.28      0.45      0.35       193\n",
      "        173       0.38      0.31      0.34       309\n",
      "        174       0.46      0.15      0.23       172\n",
      "        175       0.87      0.73      0.79        95\n",
      "        176       0.72      0.71      0.71       346\n",
      "        177       0.92      0.34      0.50       322\n",
      "        178       0.52      0.43      0.47       232\n",
      "        179       0.57      0.03      0.06       125\n",
      "        180       0.42      0.19      0.26       145\n",
      "        181       0.10      0.22      0.13        77\n",
      "        182       0.15      0.04      0.07       182\n",
      "        183       0.53      0.35      0.42       257\n",
      "        184       0.13      0.04      0.06       216\n",
      "        185       0.26      0.08      0.12       242\n",
      "        186       0.29      0.17      0.21       165\n",
      "        187       0.72      0.53      0.61       263\n",
      "        188       0.28      0.11      0.16       174\n",
      "        189       0.63      0.09      0.15       136\n",
      "        190       0.94      0.51      0.66       202\n",
      "        191       0.31      0.23      0.26       134\n",
      "        192       0.79      0.36      0.49       230\n",
      "        193       0.21      0.16      0.18        90\n",
      "        194       0.55      0.51      0.53       185\n",
      "        195       0.09      0.04      0.06       156\n",
      "        196       0.00      0.00      0.00       160\n",
      "        197       0.00      0.00      0.00       266\n",
      "        198       0.44      0.07      0.12       284\n",
      "        199       0.14      0.07      0.09       145\n",
      "        200       0.91      0.59      0.72       212\n",
      "        201       0.25      0.04      0.07       317\n",
      "        202       0.57      0.65      0.61       427\n",
      "        203       0.16      0.17      0.16       232\n",
      "        204       0.26      0.17      0.20       217\n",
      "        205       0.45      0.35      0.39       527\n",
      "        206       0.07      0.02      0.03       124\n",
      "        207       0.00      0.00      0.00       103\n",
      "        208       0.77      0.59      0.67       287\n",
      "        209       0.15      0.09      0.11       193\n",
      "        210       0.46      0.21      0.29       220\n",
      "        211       0.00      0.00      0.00       140\n",
      "        212       0.08      0.18      0.11       161\n",
      "        213       0.50      0.18      0.27        72\n",
      "        214       0.60      0.50      0.54       396\n",
      "        215       0.87      0.25      0.39       134\n",
      "        216       0.00      0.00      0.00       400\n",
      "        217       0.43      0.33      0.38        75\n",
      "        218       0.90      0.80      0.85       219\n",
      "        219       0.70      0.38      0.49       210\n",
      "        220       0.90      0.32      0.47       298\n",
      "        221       0.96      0.52      0.67       266\n",
      "        222       0.82      0.29      0.43       290\n",
      "        223       0.19      0.04      0.06       128\n",
      "        224       0.77      0.32      0.45       159\n",
      "        225       0.43      0.29      0.34       164\n",
      "        226       0.51      0.36      0.42       144\n",
      "        227       0.44      0.40      0.42       276\n",
      "        228       0.02      0.00      0.01       235\n",
      "        229       0.12      0.00      0.01       216\n",
      "        230       0.32      0.20      0.25       228\n",
      "        231       0.66      0.45      0.54        64\n",
      "        232       0.08      0.04      0.05       103\n",
      "        233       0.74      0.27      0.40       216\n",
      "        234       0.00      0.00      0.00       116\n",
      "        235       0.46      0.35      0.40        77\n",
      "        236       0.94      0.67      0.78        67\n",
      "        237       0.00      0.00      0.00       218\n",
      "        238       0.09      0.04      0.05       139\n",
      "        239       0.24      0.04      0.07        94\n",
      "        240       0.45      0.32      0.38        77\n",
      "        241       0.33      0.01      0.01       167\n",
      "        242       0.07      0.19      0.10        86\n",
      "        243       0.12      0.14      0.13        58\n",
      "        244       0.25      0.13      0.18       269\n",
      "        245       0.11      0.04      0.05       112\n",
      "        246       0.96      0.61      0.74       255\n",
      "        247       0.25      0.24      0.25        58\n",
      "        248       0.09      0.05      0.06        81\n",
      "        249       0.00      0.00      0.00       131\n",
      "        250       0.12      0.14      0.13        93\n",
      "        251       0.30      0.32      0.31       154\n",
      "        252       0.07      0.02      0.03       129\n",
      "        253       0.41      0.35      0.38        83\n",
      "        254       0.23      0.09      0.13       191\n",
      "        255       0.12      0.01      0.02       219\n",
      "        256       0.07      0.01      0.01       130\n",
      "        257       0.37      0.31      0.34        93\n",
      "        258       0.44      0.65      0.53       217\n",
      "        259       0.18      0.06      0.09       141\n",
      "        260       0.94      0.10      0.19       143\n",
      "        261       0.47      0.08      0.14       219\n",
      "        262       0.38      0.34      0.36       107\n",
      "        263       0.30      0.35      0.32       236\n",
      "        264       0.22      0.23      0.22       119\n",
      "        265       0.15      0.14      0.14        72\n",
      "        266       0.20      0.07      0.11        70\n",
      "        267       0.13      0.21      0.16       107\n",
      "        268       0.67      0.34      0.45       169\n",
      "        269       0.22      0.16      0.18       129\n",
      "        270       0.49      0.65      0.56       159\n",
      "        271       0.42      0.13      0.19       190\n",
      "        272       0.45      0.10      0.16       248\n",
      "        273       0.89      0.74      0.81       264\n",
      "        274       0.86      0.56      0.68       105\n",
      "        275       0.13      0.05      0.07       104\n",
      "        276       0.03      0.04      0.04       115\n",
      "        277       0.85      0.50      0.63       170\n",
      "        278       0.43      0.16      0.23       145\n",
      "        279       0.60      0.40      0.48       230\n",
      "        280       0.57      0.42      0.49        80\n",
      "        281       0.60      0.71      0.65       217\n",
      "        282       0.77      0.49      0.60       175\n",
      "        283       0.00      0.00      0.00       269\n",
      "        284       0.53      0.22      0.31        74\n",
      "        285       0.67      0.66      0.66       206\n",
      "        286       0.84      0.52      0.64       227\n",
      "        287       0.83      0.27      0.41       130\n",
      "        288       0.28      0.12      0.17       129\n",
      "        289       0.20      0.01      0.02        80\n",
      "        290       0.15      0.09      0.11        99\n",
      "        291       0.76      0.23      0.35       208\n",
      "        292       0.26      0.12      0.16        67\n",
      "        293       0.50      0.26      0.34       109\n",
      "        294       0.24      0.24      0.24       140\n",
      "        295       0.16      0.13      0.14       241\n",
      "        296       0.17      0.12      0.14        72\n",
      "        297       0.29      0.11      0.16       107\n",
      "        298       0.71      0.20      0.31        61\n",
      "        299       0.53      0.35      0.42        77\n",
      "        300       0.16      0.05      0.08       111\n",
      "        301       0.00      0.00      0.00       126\n",
      "        302       0.06      0.01      0.02        73\n",
      "        303       0.50      0.43      0.46       176\n",
      "        304       0.82      0.66      0.73       230\n",
      "        305       0.84      0.73      0.78       156\n",
      "        306       0.41      0.34      0.37       146\n",
      "        307       0.16      0.05      0.08        98\n",
      "        308       0.25      0.01      0.02        78\n",
      "        309       0.40      0.02      0.04        94\n",
      "        310       0.67      0.25      0.37       162\n",
      "        311       0.59      0.65      0.62       116\n",
      "        312       0.47      0.26      0.34        57\n",
      "        313       0.00      0.00      0.00        65\n",
      "        314       0.49      0.35      0.41       138\n",
      "        315       0.36      0.26      0.30       195\n",
      "        316       0.25      0.42      0.32        69\n",
      "        317       0.00      0.00      0.00       134\n",
      "        318       0.33      0.26      0.29       148\n",
      "        319       0.70      0.20      0.32       161\n",
      "        320       0.13      0.14      0.14       104\n",
      "        321       0.73      0.47      0.58       156\n",
      "        322       0.45      0.23      0.31       134\n",
      "        323       0.57      0.30      0.39       232\n",
      "        324       0.06      0.17      0.09        92\n",
      "        325       0.25      0.09      0.13       197\n",
      "        326       0.00      0.00      0.00       126\n",
      "        327       0.33      0.01      0.02       115\n",
      "        328       0.99      0.45      0.62       198\n",
      "        329       0.49      0.26      0.34       125\n",
      "        330       0.60      0.04      0.07        81\n",
      "        331       0.12      0.02      0.04        94\n",
      "        332       0.00      0.00      0.00        56\n",
      "        333       0.03      0.00      0.01       260\n",
      "        334       0.00      0.00      0.00        60\n",
      "        335       0.21      0.14      0.17       110\n",
      "        336       0.49      0.46      0.48        71\n",
      "        337       0.12      0.06      0.08        66\n",
      "        338       0.44      0.33      0.37       150\n",
      "        339       0.00      0.00      0.00        54\n",
      "        340       0.86      0.48      0.62       195\n",
      "        341       0.00      0.00      0.00        79\n",
      "        342       0.25      0.34      0.29        38\n",
      "        343       0.37      0.23      0.29        43\n",
      "        344       0.33      0.01      0.03        68\n",
      "        345       0.54      0.44      0.48        73\n",
      "        346       0.00      0.00      0.00       116\n",
      "        347       0.71      0.48      0.57       111\n",
      "        348       0.12      0.05      0.07        63\n",
      "        349       0.89      0.49      0.63       104\n",
      "        350       0.71      0.34      0.46        44\n",
      "        351       0.00      0.00      0.00        40\n",
      "        352       0.93      0.40      0.56       136\n",
      "        353       0.40      0.39      0.40        54\n",
      "        354       0.14      0.07      0.10       134\n",
      "        355       0.28      0.11      0.16       120\n",
      "        356       0.28      0.16      0.20       228\n",
      "        357       0.57      0.09      0.15       269\n",
      "        358       0.66      0.34      0.45        80\n",
      "        359       0.75      0.15      0.25       140\n",
      "        360       0.10      0.19      0.13       125\n",
      "        361       0.88      0.43      0.57       169\n",
      "        362       0.10      0.05      0.07        56\n",
      "        363       0.86      0.59      0.70       154\n",
      "        364       0.00      0.00      0.00        58\n",
      "        365       0.12      0.11      0.12        71\n",
      "        366       0.97      0.54      0.69        54\n",
      "        367       0.14      0.07      0.09       116\n",
      "        368       0.00      0.00      0.00        54\n",
      "        369       0.00      0.00      0.00        71\n",
      "        370       0.03      0.07      0.04        61\n",
      "        371       0.00      0.00      0.00        71\n",
      "        372       0.72      0.44      0.55        52\n",
      "        373       0.67      0.36      0.47       150\n",
      "        374       0.38      0.19      0.26        93\n",
      "        375       0.25      0.01      0.03        67\n",
      "        376       0.00      0.00      0.00        76\n",
      "        377       0.91      0.09      0.17       106\n",
      "        378       0.50      0.01      0.02        86\n",
      "        379       0.14      0.07      0.10        14\n",
      "        380       1.00      0.25      0.39       122\n",
      "        381       0.03      0.01      0.01       104\n",
      "        382       0.24      0.18      0.21        66\n",
      "        383       0.44      0.24      0.31       110\n",
      "        384       0.00      0.00      0.00       155\n",
      "        385       0.08      0.02      0.03        50\n",
      "        386       0.22      0.19      0.20        64\n",
      "        387       0.00      0.00      0.00        93\n",
      "        388       0.53      0.21      0.30       102\n",
      "        389       0.33      0.01      0.02       108\n",
      "        390       0.83      0.70      0.76       178\n",
      "        391       0.53      0.14      0.22       115\n",
      "        392       0.92      0.29      0.44        42\n",
      "        393       0.00      0.00      0.00       134\n",
      "        394       0.00      0.00      0.00       112\n",
      "        395       0.25      0.03      0.06       176\n",
      "        396       0.00      0.00      0.00       125\n",
      "        397       0.44      0.24      0.31       224\n",
      "        398       0.64      0.48      0.55        63\n",
      "        399       0.00      0.00      0.00        59\n",
      "        400       0.33      0.25      0.29        63\n",
      "        401       0.10      0.02      0.03        98\n",
      "        402       0.36      0.06      0.10       162\n",
      "        403       0.29      0.14      0.19        83\n",
      "        404       0.63      0.89      0.74        19\n",
      "        405       0.13      0.08      0.10        92\n",
      "        406       0.33      0.15      0.20        41\n",
      "        407       0.56      0.23      0.33        43\n",
      "        408       0.80      0.05      0.09       160\n",
      "        409       0.22      0.16      0.18        50\n",
      "        410       0.00      0.00      0.00        19\n",
      "        411       0.32      0.14      0.20       175\n",
      "        412       0.08      0.01      0.02        72\n",
      "        413       0.50      0.02      0.04        95\n",
      "        414       0.08      0.06      0.07        97\n",
      "        415       0.18      0.25      0.21        48\n",
      "        416       0.38      0.25      0.30        83\n",
      "        417       0.00      0.00      0.00        40\n",
      "        418       0.19      0.07      0.10        91\n",
      "        419       0.38      0.26      0.31        90\n",
      "        420       0.27      0.24      0.26        37\n",
      "        421       0.04      0.03      0.03        66\n",
      "        422       0.57      0.27      0.37        73\n",
      "        423       0.34      0.20      0.25        56\n",
      "        424       0.65      0.85      0.74        33\n",
      "        425       0.00      0.00      0.00        76\n",
      "        426       0.00      0.00      0.00        81\n",
      "        427       0.99      0.50      0.66       150\n",
      "        428       0.95      0.66      0.78        29\n",
      "        429       0.00      0.00      0.00       389\n",
      "        430       0.65      0.22      0.32       167\n",
      "        431       0.00      0.00      0.00       123\n",
      "        432       0.38      0.23      0.29        39\n",
      "        433       0.35      0.22      0.27        82\n",
      "        434       0.18      0.47      0.26        66\n",
      "        435       0.51      0.29      0.37        93\n",
      "        436       0.14      0.01      0.02        87\n",
      "        437       0.25      0.03      0.06        86\n",
      "        438       0.66      0.37      0.47       104\n",
      "        439       0.02      0.01      0.01       100\n",
      "        440       0.33      0.01      0.01       141\n",
      "        441       0.29      0.23      0.26       110\n",
      "        442       0.22      0.09      0.13       123\n",
      "        443       0.00      0.00      0.00        71\n",
      "        444       0.36      0.05      0.08       109\n",
      "        445       0.23      0.12      0.16        48\n",
      "        446       0.36      0.18      0.24        76\n",
      "        447       0.04      0.03      0.03        38\n",
      "        448       0.66      0.43      0.52        81\n",
      "        449       0.47      0.06      0.11       132\n",
      "        450       0.39      0.30      0.34        81\n",
      "        451       0.89      0.11      0.19        76\n",
      "        452       0.00      0.00      0.00        44\n",
      "        453       0.00      0.00      0.00        44\n",
      "        454       0.88      0.30      0.45        70\n",
      "        455       0.11      0.01      0.01       155\n",
      "        456       0.22      0.16      0.19        43\n",
      "        457       0.31      0.15      0.21        72\n",
      "        458       0.23      0.11      0.15        62\n",
      "        459       1.00      0.09      0.16        69\n",
      "        460       0.25      0.03      0.06       119\n",
      "        461       0.68      0.16      0.27        79\n",
      "        462       0.17      0.02      0.04        47\n",
      "        463       0.11      0.01      0.02       104\n",
      "        464       0.37      0.33      0.35       106\n",
      "        465       0.00      0.00      0.00        64\n",
      "        466       0.55      0.20      0.29       173\n",
      "        467       0.66      0.48      0.55       107\n",
      "        468       0.50      0.01      0.02       126\n",
      "        469       0.00      0.00      0.00       114\n",
      "        470       0.94      0.72      0.81       140\n",
      "        471       0.00      0.00      0.00        79\n",
      "        472       0.32      0.27      0.29       143\n",
      "        473       0.56      0.23      0.32       158\n",
      "        474       1.00      0.01      0.01       138\n",
      "        475       0.04      0.05      0.05        59\n",
      "        476       0.58      0.39      0.46        88\n",
      "        477       0.81      0.45      0.58       176\n",
      "        478       0.92      0.50      0.65        24\n",
      "        479       0.00      0.00      0.00        92\n",
      "        480       0.78      0.28      0.41       100\n",
      "        481       0.44      0.04      0.07       103\n",
      "        482       0.22      0.22      0.22        74\n",
      "        483       0.76      0.45      0.56       105\n",
      "        484       0.05      0.01      0.02        83\n",
      "        485       0.11      0.01      0.02        82\n",
      "        486       0.33      0.03      0.05        71\n",
      "        487       0.39      0.21      0.27       120\n",
      "        488       0.00      0.00      0.00       105\n",
      "        489       0.60      0.17      0.27        87\n",
      "        490       1.00      0.75      0.86        32\n",
      "        491       0.00      0.00      0.00        69\n",
      "        492       0.00      0.00      0.00        49\n",
      "        493       0.00      0.00      0.00       117\n",
      "        494       0.80      0.07      0.12        61\n",
      "        495       0.98      0.62      0.76       344\n",
      "        496       0.16      0.10      0.12        52\n",
      "        497       0.71      0.04      0.07       137\n",
      "        498       0.00      0.00      0.00        98\n",
      "        499       0.35      0.23      0.28        79\n",
      "\n",
      "avg / total       0.55      0.32      0.39    173812\n",
      "\n",
      "Time taken to run this cell : 0:00:19.236854\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "#classifier = OneVsRestClassifier(LogisticRegression(penalty='l1'), n_jobs=-1)\n",
    "#classifier.fit(x_train_multilabel, y_train)\n",
    "predictions = best_model2.predict(x_test_multilabel)\n",
    "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
    "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
    "precision = precision_score(y_test, predictions, average='micro')\n",
    "recall = recall_score(y_test, predictions, average='micro')\n",
    "f1 = f1_score(y_test, predictions, average='micro')\n",
    " \n",
    "print(\"Micro-average quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='macro')\n",
    "recall = recall_score(y_test, predictions, average='macro')\n",
    "f1 = f1_score(y_test, predictions, average='macro')\n",
    " \n",
    "print(\"Macro-average quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "print (metrics.classification_report(y_test, predictions))\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  OneVsRestClassifier with Linear-SVM (SGDClassifier with loss-hinge)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.001\n",
      "{'estimator__alpha': [0.001], 'estimator__loss': ['hinge'], 'estimator__penalty': ['l1']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "0.01\n",
      "{'estimator__alpha': [0.01], 'estimator__loss': ['hinge'], 'estimator__penalty': ['l1']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "0.1\n",
      "{'estimator__alpha': [0.1], 'estimator__loss': ['hinge'], 'estimator__penalty': ['l1']}\n",
      "Gridsearchcv\n",
      "fit model\n",
      "\n",
      " The optimal value of alpha with penalty=l1 and loss= log is 0.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to run this cell : 2:18:49.138029\n"
     ]
    }
   ],
   "source": [
    "start = datetime.now()\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# hp1={'estimator__C':alpha}\n",
    "\n",
    "cv_scores = []\n",
    "for i in alpha: \n",
    "    print(i)\n",
    "    hp1={'estimator__alpha':[i],\n",
    "         'estimator__loss':['hinge'],\n",
    "         'estimator__penalty':['l1']}\n",
    "    print(hp1)\n",
    "    classifier = OneVsRestClassifier(SGDClassifier())\n",
    "\n",
    "    model11 =GridSearchCV(classifier,hp1,\n",
    "                          cv=3, scoring='f1_micro',n_jobs=-1)\n",
    "    print(\"Gridsearchcv\")\n",
    "    best_model1=model11.fit(x_train_multilabel, y_train)\n",
    "    print('fit model')\n",
    "    Train_model_score=best_model1.score(x_train_multilabel, \n",
    "                                        y_train)\n",
    "#print(\"best_model1\")\n",
    "    cv_scores.append(Train_model_score.mean())\n",
    "\n",
    "fscore = [x for x in cv_scores]\n",
    "\n",
    "# determining best alpha\n",
    "optimal_alpha23 = alpha[fscore.index(max(fscore))]\n",
    "print('\\n The optimal value of alpha with penalty=l1 and loss= log is %d.' % optimal_alpha23)\n",
    "\n",
    "# Plots\n",
    "fig4 = plt.figure( facecolor='c', edgecolor='k')\n",
    "plt.plot(alpha, fscore,color='green', marker='o', linestyle='dashed',\n",
    "linewidth=2, markersize=12)\n",
    "\n",
    "for xy in zip(alpha, np.round(fscore,3)):\n",
    "    plt.annotate('(%s, %s)' % xy, xy=xy, textcoords='data')\n",
    "\n",
    "plt.xlabel('Hyper parameter Alpha')\n",
    "plt.ylabel('F1_Score value ')\n",
    "plt.show()\n",
    "\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsRestClassifier with SGDClassifier for optimal alpha with hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.now()\n",
    "classifier2 = OneVsRestClassifier(SGDClassifier(loss='hinge',\n",
    "                                               alpha=optimal_alpha23,\n",
    "                                               penalty='l1'))\n",
    "classifier2=classifier2.fit(x_train_multilabel, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['classifier2.pkl']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(classifier2, 'classifier2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier2=joblib.load('classifier2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.17585\n",
      "Hamming loss  0.00330166\n",
      "Micro-averasge quality numbers\n",
      "Precision: 0.5428, Recall: 0.3186, F1-measure: 0.4015\n",
      "Macro-average quality numbers\n",
      "Precision: 0.3193, Recall: 0.2399, F1-measure: 0.2547\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.67      0.68      0.68      5519\n",
      "          1       0.45      0.21      0.29      8190\n",
      "          2       0.70      0.38      0.49      6529\n",
      "          3       0.65      0.43      0.52      3231\n",
      "          4       0.83      0.33      0.47      6430\n",
      "          5       0.58      0.41      0.48      2879\n",
      "          6       0.78      0.57      0.65      5086\n",
      "          7       0.82      0.59      0.68      4533\n",
      "          8       0.44      0.16      0.24      3000\n",
      "          9       0.60      0.59      0.59      2765\n",
      "         10       0.20      0.01      0.02      3051\n",
      "         11       0.65      0.37      0.47      3009\n",
      "         12       0.54      0.29      0.37      2630\n",
      "         13       0.27      0.20      0.23      1426\n",
      "         14       0.77      0.64      0.70      2548\n",
      "         15       0.59      0.14      0.22      2371\n",
      "         16       0.38      0.32      0.35       873\n",
      "         17       0.73      0.69      0.71      2151\n",
      "         18       0.49      0.27      0.35      2204\n",
      "         19       0.55      0.43      0.48       831\n",
      "         20       0.74      0.47      0.57      1860\n",
      "         21       0.27      0.01      0.02      2023\n",
      "         22       0.34      0.02      0.03      1513\n",
      "         23       0.73      0.62      0.67      1207\n",
      "         24       0.00      0.00      0.00       506\n",
      "         25       0.52      0.33      0.41       425\n",
      "         26       0.52      0.36      0.42       793\n",
      "         27       0.52      0.37      0.43      1291\n",
      "         28       0.49      0.40      0.44      1208\n",
      "         29       0.14      0.18      0.16       406\n",
      "         30       0.69      0.25      0.37       504\n",
      "         31       0.00      0.00      0.00       732\n",
      "         32       0.37      0.39      0.38       441\n",
      "         33       0.02      0.00      0.00      1645\n",
      "         34       0.58      0.32      0.41      1058\n",
      "         35       0.66      0.57      0.61       946\n",
      "         36       0.52      0.29      0.37       644\n",
      "         37       0.59      0.82      0.68       136\n",
      "         38       0.48      0.41      0.44       570\n",
      "         39       0.70      0.31      0.43       766\n",
      "         40       0.56      0.08      0.14      1132\n",
      "         41       0.29      0.25      0.27       174\n",
      "         42       0.58      0.63      0.60       210\n",
      "         43       0.61      0.53      0.57       433\n",
      "         44       0.47      0.53      0.50       626\n",
      "         45       0.45      0.28      0.35       852\n",
      "         46       0.61      0.39      0.48       534\n",
      "         47       0.00      0.00      0.00       350\n",
      "         48       0.56      0.62      0.59       496\n",
      "         49       0.71      0.69      0.70       785\n",
      "         50       0.05      0.00      0.00       475\n",
      "         51       0.00      0.00      0.00       305\n",
      "         52       0.06      0.01      0.01       251\n",
      "         53       0.44      0.54      0.48       914\n",
      "         54       0.00      0.00      0.00       728\n",
      "         55       0.03      0.00      0.01       258\n",
      "         56       0.00      0.00      0.00       821\n",
      "         57       0.36      0.06      0.11       541\n",
      "         58       0.68      0.24      0.35       748\n",
      "         59       0.80      0.74      0.77       724\n",
      "         60       0.22      0.09      0.13       660\n",
      "         61       0.62      0.28      0.38       235\n",
      "         62       0.84      0.83      0.83       718\n",
      "         63       0.63      0.68      0.65       468\n",
      "         64       0.47      0.44      0.45       191\n",
      "         65       0.12      0.19      0.14       429\n",
      "         66       0.00      0.00      0.00       415\n",
      "         67       0.63      0.65      0.64       274\n",
      "         68       0.74      0.63      0.68       510\n",
      "         69       0.51      0.49      0.50       466\n",
      "         70       0.00      0.00      0.00       305\n",
      "         71       0.14      0.26      0.18       247\n",
      "         72       0.62      0.52      0.56       401\n",
      "         73       0.88      0.78      0.83        86\n",
      "         74       0.26      0.41      0.32       120\n",
      "         75       0.84      0.75      0.79       129\n",
      "         76       0.00      0.00      0.00       473\n",
      "         77       0.23      0.43      0.30       143\n",
      "         78       0.73      0.51      0.60       347\n",
      "         79       0.57      0.36      0.44       479\n",
      "         80       0.23      0.41      0.30       279\n",
      "         81       0.62      0.13      0.22       461\n",
      "         82       0.03      0.04      0.03       298\n",
      "         83       0.63      0.50      0.56       396\n",
      "         84       0.36      0.33      0.34       184\n",
      "         85       0.30      0.11      0.16       573\n",
      "         86       0.37      0.04      0.07       325\n",
      "         87       0.53      0.21      0.30       273\n",
      "         88       0.30      0.35      0.32       135\n",
      "         89       0.00      0.00      0.00       232\n",
      "         90       0.30      0.42      0.35       409\n",
      "         91       0.60      0.29      0.39       420\n",
      "         92       0.64      0.58      0.61       408\n",
      "         93       0.42      0.59      0.49       241\n",
      "         94       0.00      0.00      0.00       211\n",
      "         95       0.00      0.00      0.00       277\n",
      "         96       0.00      0.00      0.00       410\n",
      "         97       0.84      0.15      0.25       501\n",
      "         98       0.56      0.68      0.62       136\n",
      "         99       0.44      0.24      0.31       239\n",
      "        100       0.08      0.15      0.11       324\n",
      "        101       0.67      0.61      0.64       277\n",
      "        102       0.85      0.69      0.76       613\n",
      "        103       0.25      0.20      0.22       157\n",
      "        104       0.00      0.00      0.00       295\n",
      "        105       0.72      0.37      0.49       334\n",
      "        106       0.00      0.00      0.00       335\n",
      "        107       0.54      0.60      0.57       389\n",
      "        108       0.33      0.21      0.26       251\n",
      "        109       0.39      0.42      0.40       317\n",
      "        110       0.00      0.00      0.00       187\n",
      "        111       0.17      0.15      0.16       140\n",
      "        112       0.09      0.05      0.07       154\n",
      "        113       0.49      0.31      0.38       332\n",
      "        114       0.00      0.00      0.00       323\n",
      "        115       0.19      0.16      0.17       344\n",
      "        116       0.58      0.61      0.59       370\n",
      "        117       0.42      0.15      0.22       313\n",
      "        118       0.69      0.73      0.71       874\n",
      "        119       0.41      0.16      0.23       293\n",
      "        120       0.00      0.00      0.00       200\n",
      "        121       0.60      0.49      0.54       463\n",
      "        122       0.00      0.00      0.00       119\n",
      "        123       0.00      0.00      0.00       256\n",
      "        124       0.80      0.82      0.81       195\n",
      "        125       0.30      0.05      0.09       138\n",
      "        126       0.56      0.57      0.56       376\n",
      "        127       0.00      0.00      0.00       122\n",
      "        128       0.02      0.00      0.01       252\n",
      "        129       0.00      0.00      0.00       144\n",
      "        130       0.42      0.18      0.25       150\n",
      "        131       0.00      0.00      0.00       210\n",
      "        132       0.62      0.02      0.04       361\n",
      "        133       0.80      0.64      0.71       453\n",
      "        134       0.68      0.76      0.71       124\n",
      "        135       0.00      0.00      0.00        91\n",
      "        136       0.51      0.14      0.22       128\n",
      "        137       0.36      0.36      0.36       218\n",
      "        138       0.60      0.10      0.17       243\n",
      "        139       0.00      0.00      0.00       149\n",
      "        140       0.61      0.31      0.41       318\n",
      "        141       0.07      0.18      0.10       159\n",
      "        142       0.58      0.30      0.39       274\n",
      "        143       0.76      0.66      0.70       362\n",
      "        144       0.32      0.31      0.32       118\n",
      "        145       0.41      0.49      0.45       164\n",
      "        146       0.41      0.46      0.44       461\n",
      "        147       0.57      0.60      0.59       159\n",
      "        148       0.18      0.05      0.08       166\n",
      "        149       0.94      0.51      0.66       346\n",
      "        150       0.30      0.05      0.08       350\n",
      "        151       0.81      0.64      0.71        55\n",
      "        152       0.59      0.53      0.56       387\n",
      "        153       0.58      0.05      0.09       150\n",
      "        154       0.36      0.11      0.17       281\n",
      "        155       0.11      0.07      0.09       202\n",
      "        156       0.50      0.72      0.59       130\n",
      "        157       0.00      0.00      0.00       245\n",
      "        158       0.64      0.49      0.55       177\n",
      "        159       0.40      0.29      0.34       130\n",
      "        160       0.25      0.25      0.25       336\n",
      "        161       0.60      0.69      0.64       220\n",
      "        162       0.00      0.00      0.00       229\n",
      "        163       0.79      0.46      0.58       316\n",
      "        164       0.69      0.27      0.39       283\n",
      "        165       0.30      0.47      0.37       197\n",
      "        166       0.38      0.05      0.09       101\n",
      "        167       0.00      0.00      0.00       231\n",
      "        168       0.26      0.23      0.24       370\n",
      "        169       0.30      0.26      0.28       258\n",
      "        170       0.05      0.01      0.02       101\n",
      "        171       0.31      0.18      0.23        89\n",
      "        172       0.21      0.30      0.24       193\n",
      "        173       0.36      0.38      0.37       309\n",
      "        174       0.18      0.19      0.18       172\n",
      "        175       0.66      0.75      0.70        95\n",
      "        176       0.68      0.60      0.64       346\n",
      "        177       0.86      0.39      0.54       322\n",
      "        178       0.51      0.54      0.53       232\n",
      "        179       0.00      0.00      0.00       125\n",
      "        180       0.37      0.34      0.36       145\n",
      "        181       0.19      0.21      0.20        77\n",
      "        182       0.00      0.00      0.00       182\n",
      "        183       0.39      0.49      0.43       257\n",
      "        184       0.07      0.08      0.08       216\n",
      "        185       0.00      0.00      0.00       242\n",
      "        186       0.00      0.00      0.00       165\n",
      "        187       0.60      0.58      0.59       263\n",
      "        188       0.17      0.20      0.18       174\n",
      "        189       0.00      0.00      0.00       136\n",
      "        190       0.80      0.57      0.66       202\n",
      "        191       0.00      0.00      0.00       134\n",
      "        192       0.68      0.43      0.53       230\n",
      "        193       0.30      0.23      0.26        90\n",
      "        194       0.37      0.54      0.44       185\n",
      "        195       0.00      0.00      0.00       156\n",
      "        196       0.00      0.00      0.00       160\n",
      "        197       0.00      0.00      0.00       266\n",
      "        198       0.00      0.00      0.00       284\n",
      "        199       0.07      0.03      0.04       145\n",
      "        200       0.82      0.76      0.79       212\n",
      "        201       0.00      0.00      0.00       317\n",
      "        202       0.55      0.55      0.55       427\n",
      "        203       0.09      0.02      0.03       232\n",
      "        204       0.00      0.00      0.00       217\n",
      "        205       0.43      0.42      0.42       527\n",
      "        206       0.00      0.00      0.00       124\n",
      "        207       0.24      0.15      0.18       103\n",
      "        208       0.51      0.43      0.47       287\n",
      "        209       0.00      0.00      0.00       193\n",
      "        210       0.48      0.19      0.27       220\n",
      "        211       0.67      0.21      0.32       140\n",
      "        212       0.00      0.00      0.00       161\n",
      "        213       0.37      0.14      0.20        72\n",
      "        214       0.56      0.43      0.48       396\n",
      "        215       0.67      0.29      0.41       134\n",
      "        216       0.06      0.01      0.02       400\n",
      "        217       0.32      0.36      0.34        75\n",
      "        218       0.87      0.74      0.80       219\n",
      "        219       0.79      0.30      0.44       210\n",
      "        220       0.91      0.36      0.51       298\n",
      "        221       0.46      0.69      0.55       266\n",
      "        222       0.44      0.34      0.38       290\n",
      "        223       0.12      0.12      0.12       128\n",
      "        224       0.46      0.48      0.47       159\n",
      "        225       0.53      0.29      0.38       164\n",
      "        226       0.34      0.44      0.38       144\n",
      "        227       0.45      0.25      0.32       276\n",
      "        228       0.00      0.00      0.00       235\n",
      "        229       0.00      0.00      0.00       216\n",
      "        230       0.00      0.00      0.00       228\n",
      "        231       0.69      0.64      0.67        64\n",
      "        232       0.07      0.12      0.09       103\n",
      "        233       0.46      0.34      0.39       216\n",
      "        234       0.33      0.02      0.03       116\n",
      "        235       0.36      0.71      0.48        77\n",
      "        236       0.86      0.73      0.79        67\n",
      "        237       0.00      0.00      0.00       218\n",
      "        238       0.07      0.03      0.04       139\n",
      "        239       0.00      0.00      0.00        94\n",
      "        240       0.47      0.25      0.32        77\n",
      "        241       0.42      0.05      0.09       167\n",
      "        242       0.40      0.43      0.42        86\n",
      "        243       0.05      0.02      0.03        58\n",
      "        244       0.00      0.00      0.00       269\n",
      "        245       0.13      0.12      0.12       112\n",
      "        246       0.73      0.79      0.76       255\n",
      "        247       0.27      0.21      0.24        58\n",
      "        248       0.00      0.00      0.00        81\n",
      "        249       0.00      0.00      0.00       131\n",
      "        250       0.12      0.31      0.17        93\n",
      "        251       0.00      0.00      0.00       154\n",
      "        252       0.00      0.00      0.00       129\n",
      "        253       0.31      0.36      0.33        83\n",
      "        254       0.21      0.12      0.15       191\n",
      "        255       0.00      0.00      0.00       219\n",
      "        256       0.00      0.00      0.00       130\n",
      "        257       0.32      0.25      0.28        93\n",
      "        258       0.58      0.50      0.53       217\n",
      "        259       0.00      0.00      0.00       141\n",
      "        260       0.74      0.20      0.31       143\n",
      "        261       0.53      0.14      0.22       219\n",
      "        262       0.41      0.22      0.29       107\n",
      "        263       0.27      0.33      0.29       236\n",
      "        264       0.11      0.19      0.14       119\n",
      "        265       0.00      0.00      0.00        72\n",
      "        266       0.20      0.11      0.15        70\n",
      "        267       0.23      0.06      0.09       107\n",
      "        268       0.44      0.44      0.44       169\n",
      "        269       0.00      0.00      0.00       129\n",
      "        270       0.53      0.62      0.57       159\n",
      "        271       0.20      0.16      0.18       190\n",
      "        272       0.00      0.00      0.00       248\n",
      "        273       0.84      0.74      0.78       264\n",
      "        274       0.58      0.63      0.61       105\n",
      "        275       0.14      0.06      0.08       104\n",
      "        276       0.00      0.00      0.00       115\n",
      "        277       0.88      0.12      0.22       170\n",
      "        278       0.41      0.31      0.35       145\n",
      "        279       0.83      0.30      0.45       230\n",
      "        280       0.39      0.46      0.42        80\n",
      "        281       0.54      0.64      0.58       217\n",
      "        282       0.63      0.70      0.66       175\n",
      "        283       0.00      0.00      0.00       269\n",
      "        284       0.45      0.43      0.44        74\n",
      "        285       0.60      0.47      0.53       206\n",
      "        286       0.83      0.71      0.77       227\n",
      "        287       0.77      0.26      0.39       130\n",
      "        288       0.16      0.12      0.14       129\n",
      "        289       0.00      0.00      0.00        80\n",
      "        290       0.00      0.00      0.00        99\n",
      "        291       0.51      0.20      0.28       208\n",
      "        292       0.10      0.03      0.05        67\n",
      "        293       1.00      0.01      0.02       109\n",
      "        294       0.00      0.00      0.00       140\n",
      "        295       0.12      0.20      0.15       241\n",
      "        296       0.10      0.12      0.11        72\n",
      "        297       0.20      0.14      0.16       107\n",
      "        298       0.61      0.18      0.28        61\n",
      "        299       0.81      0.17      0.28        77\n",
      "        300       0.00      0.00      0.00       111\n",
      "        301       0.00      0.00      0.00       126\n",
      "        302       0.00      0.00      0.00        73\n",
      "        303       0.31      0.42      0.36       176\n",
      "        304       0.87      0.71      0.78       230\n",
      "        305       0.93      0.58      0.72       156\n",
      "        306       0.34      0.35      0.35       146\n",
      "        307       0.00      0.00      0.00        98\n",
      "        308       0.00      0.00      0.00        78\n",
      "        309       0.48      0.21      0.29        94\n",
      "        310       0.21      0.41      0.28       162\n",
      "        311       0.71      0.51      0.59       116\n",
      "        312       0.34      0.46      0.39        57\n",
      "        313       0.00      0.00      0.00        65\n",
      "        314       0.34      0.34      0.34       138\n",
      "        315       0.30      0.32      0.31       195\n",
      "        316       0.28      0.48      0.35        69\n",
      "        317       0.00      0.00      0.00       134\n",
      "        318       0.23      0.41      0.29       148\n",
      "        319       0.78      0.38      0.51       161\n",
      "        320       0.00      0.00      0.00       104\n",
      "        321       0.57      0.69      0.62       156\n",
      "        322       0.49      0.32      0.39       134\n",
      "        323       0.47      0.28      0.35       232\n",
      "        324       0.00      0.00      0.00        92\n",
      "        325       0.00      0.00      0.00       197\n",
      "        326       0.00      0.00      0.00       126\n",
      "        327       0.00      0.00      0.00       115\n",
      "        328       0.96      0.34      0.50       198\n",
      "        329       0.27      0.38      0.31       125\n",
      "        330       0.67      0.15      0.24        81\n",
      "        331       0.00      0.00      0.00        94\n",
      "        332       0.00      0.00      0.00        56\n",
      "        333       0.00      0.00      0.00       260\n",
      "        334       0.00      0.00      0.00        60\n",
      "        335       0.13      0.19      0.16       110\n",
      "        336       0.32      0.56      0.41        71\n",
      "        337       0.00      0.00      0.00        66\n",
      "        338       0.35      0.25      0.29       150\n",
      "        339       0.00      0.00      0.00        54\n",
      "        340       0.60      0.46      0.52       195\n",
      "        341       1.00      0.03      0.05        79\n",
      "        342       0.38      0.08      0.13        38\n",
      "        343       0.47      0.21      0.29        43\n",
      "        344       0.00      0.00      0.00        68\n",
      "        345       0.37      0.47      0.41        73\n",
      "        346       0.08      0.05      0.06       116\n",
      "        347       0.72      0.23      0.35       111\n",
      "        348       0.00      0.00      0.00        63\n",
      "        349       0.62      0.65      0.64       104\n",
      "        350       0.50      0.43      0.46        44\n",
      "        351       0.00      0.00      0.00        40\n",
      "        352       0.29      0.38      0.33       136\n",
      "        353       0.35      0.31      0.33        54\n",
      "        354       0.00      0.00      0.00       134\n",
      "        355       0.82      0.12      0.20       120\n",
      "        356       0.29      0.14      0.19       228\n",
      "        357       0.62      0.06      0.10       269\n",
      "        358       0.33      0.54      0.41        80\n",
      "        359       0.31      0.33      0.32       140\n",
      "        360       0.00      0.00      0.00       125\n",
      "        361       0.87      0.39      0.54       169\n",
      "        362       0.08      0.05      0.06        56\n",
      "        363       0.82      0.64      0.72       154\n",
      "        364       0.00      0.00      0.00        58\n",
      "        365       0.07      0.23      0.11        71\n",
      "        366       0.97      0.54      0.69        54\n",
      "        367       0.00      0.00      0.00       116\n",
      "        368       0.00      0.00      0.00        54\n",
      "        369       0.00      0.00      0.00        71\n",
      "        370       0.00      0.00      0.00        61\n",
      "        371       0.45      0.07      0.12        71\n",
      "        372       0.41      0.50      0.45        52\n",
      "        373       0.27      0.18      0.22       150\n",
      "        374       0.24      0.32      0.27        93\n",
      "        375       0.00      0.00      0.00        67\n",
      "        376       0.00      0.00      0.00        76\n",
      "        377       0.16      0.07      0.09       106\n",
      "        378       0.00      0.00      0.00        86\n",
      "        379       0.00      0.00      0.00        14\n",
      "        380       1.00      0.03      0.06       122\n",
      "        381       0.00      0.00      0.00       104\n",
      "        382       0.16      0.12      0.14        66\n",
      "        383       0.21      0.26      0.24       110\n",
      "        384       0.00      0.00      0.00       155\n",
      "        385       0.00      0.00      0.00        50\n",
      "        386       0.21      0.16      0.18        64\n",
      "        387       0.00      0.00      0.00        93\n",
      "        388       0.33      0.38      0.35       102\n",
      "        389       0.00      0.00      0.00       108\n",
      "        390       0.85      0.70      0.77       178\n",
      "        391       0.54      0.24      0.34       115\n",
      "        392       0.46      0.43      0.44        42\n",
      "        393       0.00      0.00      0.00       134\n",
      "        394       0.00      0.00      0.00       112\n",
      "        395       0.00      0.00      0.00       176\n",
      "        396       0.00      0.00      0.00       125\n",
      "        397       0.52      0.48      0.50       224\n",
      "        398       0.59      0.37      0.45        63\n",
      "        399       0.00      0.00      0.00        59\n",
      "        400       0.32      0.46      0.38        63\n",
      "        401       0.00      0.00      0.00        98\n",
      "        402       0.00      0.00      0.00       162\n",
      "        403       0.04      0.22      0.06        83\n",
      "        404       0.65      0.79      0.71        19\n",
      "        405       0.00      0.00      0.00        92\n",
      "        406       0.15      0.27      0.19        41\n",
      "        407       0.36      0.28      0.32        43\n",
      "        408       0.04      0.03      0.03       160\n",
      "        409       0.00      0.00      0.00        50\n",
      "        410       0.00      0.00      0.00        19\n",
      "        411       0.25      0.12      0.16       175\n",
      "        412       0.00      0.00      0.00        72\n",
      "        413       0.20      0.11      0.14        95\n",
      "        414       0.00      0.00      0.00        97\n",
      "        415       0.00      0.00      0.00        48\n",
      "        416       0.27      0.36      0.31        83\n",
      "        417       0.00      0.00      0.00        40\n",
      "        418       0.00      0.00      0.00        91\n",
      "        419       0.27      0.22      0.25        90\n",
      "        420       0.29      0.46      0.35        37\n",
      "        421       0.00      0.00      0.00        66\n",
      "        422       0.44      0.36      0.39        73\n",
      "        423       0.37      0.25      0.30        56\n",
      "        424       0.88      0.88      0.88        33\n",
      "        425       0.00      0.00      0.00        76\n",
      "        426       0.00      0.00      0.00        81\n",
      "        427       0.96      0.73      0.83       150\n",
      "        428       0.58      0.76      0.66        29\n",
      "        429       0.00      0.00      0.00       389\n",
      "        430       0.47      0.18      0.26       167\n",
      "        431       0.00      0.00      0.00       123\n",
      "        432       0.29      0.31      0.30        39\n",
      "        433       0.28      0.34      0.31        82\n",
      "        434       0.95      0.55      0.69        66\n",
      "        435       0.47      0.44      0.46        93\n",
      "        436       0.00      0.00      0.00        87\n",
      "        437       0.18      0.07      0.10        86\n",
      "        438       0.35      0.61      0.45       104\n",
      "        439       0.00      0.00      0.00       100\n",
      "        440       0.00      0.00      0.00       141\n",
      "        441       0.29      0.35      0.31       110\n",
      "        442       0.00      0.00      0.00       123\n",
      "        443       0.53      0.11      0.19        71\n",
      "        444       0.14      0.02      0.03       109\n",
      "        445       0.30      0.29      0.29        48\n",
      "        446       0.42      0.21      0.28        76\n",
      "        447       0.00      0.00      0.00        38\n",
      "        448       0.49      0.51      0.50        81\n",
      "        449       0.00      0.00      0.00       132\n",
      "        450       0.47      0.38      0.42        81\n",
      "        451       0.60      0.33      0.42        76\n",
      "        452       0.00      0.00      0.00        44\n",
      "        453       0.00      0.00      0.00        44\n",
      "        454       0.45      0.49      0.47        70\n",
      "        455       0.00      0.00      0.00       155\n",
      "        456       0.00      0.00      0.00        43\n",
      "        457       0.09      0.36      0.14        72\n",
      "        458       0.00      0.00      0.00        62\n",
      "        459       0.00      0.00      0.00        69\n",
      "        460       0.00      0.00      0.00       119\n",
      "        461       0.00      0.00      0.00        79\n",
      "        462       0.00      0.00      0.00        47\n",
      "        463       0.00      0.00      0.00       104\n",
      "        464       0.00      0.00      0.00       106\n",
      "        465       0.00      0.00      0.00        64\n",
      "        466       0.31      0.26      0.28       173\n",
      "        467       0.67      0.21      0.31       107\n",
      "        468       0.00      0.00      0.00       126\n",
      "        469       0.00      0.00      0.00       114\n",
      "        470       0.88      0.59      0.71       140\n",
      "        471       0.00      0.00      0.00        79\n",
      "        472       0.35      0.43      0.39       143\n",
      "        473       0.69      0.11      0.20       158\n",
      "        474       0.00      0.00      0.00       138\n",
      "        475       0.00      0.00      0.00        59\n",
      "        476       0.43      0.62      0.51        88\n",
      "        477       0.65      0.63      0.64       176\n",
      "        478       0.85      0.71      0.77        24\n",
      "        479       0.08      0.10      0.09        92\n",
      "        480       0.25      0.20      0.22       100\n",
      "        481       0.00      0.00      0.00       103\n",
      "        482       0.00      0.00      0.00        74\n",
      "        483       0.70      0.54      0.61       105\n",
      "        484       0.00      0.00      0.00        83\n",
      "        485       0.00      0.00      0.00        82\n",
      "        486       0.24      0.10      0.14        71\n",
      "        487       0.28      0.53      0.36       120\n",
      "        488       0.00      0.00      0.00       105\n",
      "        489       0.62      0.37      0.46        87\n",
      "        490       1.00      0.81      0.90        32\n",
      "        491       0.00      0.00      0.00        69\n",
      "        492       0.00      0.00      0.00        49\n",
      "        493       0.00      0.00      0.00       117\n",
      "        494       0.33      0.07      0.11        61\n",
      "        495       0.00      0.00      0.00       344\n",
      "        496       0.00      0.00      0.00        52\n",
      "        497       0.00      0.00      0.00       137\n",
      "        498       0.29      0.05      0.09        98\n",
      "        499       0.00      0.00      0.00        79\n",
      "\n",
      "avg / total       0.47      0.32      0.36    173812\n",
      "\n",
      "Time taken to run this cell : 0:13:46.246785\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = classifier2.predict (x_test_multilabel)\n",
    "\n",
    "\n",
    "print(\"Accuracy :\",metrics.accuracy_score(y_test, predictions))\n",
    "print(\"Hamming loss \",metrics.hamming_loss(y_test,predictions))\n",
    "\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='micro')\n",
    "recall = recall_score(y_test, predictions, average='micro')\n",
    "f1 = f1_score(y_test, predictions, average='micro')\n",
    " \n",
    "print(\"Micro-averasge quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "precision = precision_score(y_test, predictions, average='macro')\n",
    "recall = recall_score(y_test, predictions, average='macro')\n",
    "f1 = f1_score(y_test, predictions, average='macro')\n",
    " \n",
    "print(\"Macro-average quality numbers\")\n",
    "print(\"Precision: {:.4f}, Recall: {:.4f}, F1-measure: {:.4f}\".format(precision, recall, f1))\n",
    "\n",
    "print (metrics.classification_report(y_test, predictions)) #printing classification report for all 500 labels\n",
    "print(\"Time taken to run this cell :\", datetime.now() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prettytable import PrettyTable\n",
    "x = PrettyTable()\n",
    "x.field_names = [\"Sr.No\", \"MODEL\",\"FEATURIZATION\",\"PENALTY\" ,\"ALPHA\",'LOSS','MICRO_F1_SCORE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.add_row([\"1\", 'OneVsRest+SGD Classifier', \"Tf-idf\",\"l1\",0.0001,\"log\",0.4488])\n",
    "x.add_row([\"2\", 'OneVsRest+SGD(log)=LR', \"Bag-of-words\",\"l2\",0.001,\"log\",0.4268])\n",
    "x.add_row([\"3\", 'OneVsRest+SGD(log)=LR', \"Bag-of-words\",\"l1\",0.001,\"log\",0.4104])\n",
    "x.add_row([\"4\", 'OneVsRest+SGD Classifier', \"Bag-of-words\",\"l1\",0.001,\"Hinge\",0.4028])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------------+---------------+---------+--------+-------+----------------+\n",
      "| Sr.No |          MODEL           | FEATURIZATION | PENALTY | ALPHA  |  LOSS | MICRO_F1_SCORE |\n",
      "+-------+--------------------------+---------------+---------+--------+-------+----------------+\n",
      "|   1   | OneVsRest+SGD Classifier |     Tf-idf    |    l1   | 0.0001 |  log  |     0.4488     |\n",
      "|   2   |  OneVsRest+SGD(log)=LR   |  Bag-of-words |    l2   | 0.001  |  log  |     0.4268     |\n",
      "|   3   |  OneVsRest+SGD(log)=LR   |  Bag-of-words |    l1   | 0.001  |  log  |     0.4104     |\n",
      "|   4   | OneVsRest+SGD Classifier |  Bag-of-words |    l1   | 0.001  | Hinge |     0.4028     |\n",
      "+-------+--------------------------+---------------+---------+--------+-------+----------------+\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The objective's result is shown as above.\n",
    "* Model {bag of words upto 4 grams and computed the micro f1 score with Logistic regression(OvR)} performs 42.68% on tag prediction which is not higher than the result obtained with model{ TF-IDF with alpha=00.0001 ,n_grams=(1,3)}\n",
    "* The performance of model with various alpha value is shown in graph.\n"
   ]
  }
 ],
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  "colab": {
   "name": "SO_Tag_Predictor.ipynb",
   "provenance": [],
   "version": "0.3.2"
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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