Stack_Overflow_Auto_Tagging.ipynb

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        "<a href=\"https://colab.research.google.com/github/PHMark/ML-Personal-Projects/blob/master/Stack_Overflow_Auto_Tagging/Stack_Overflow_Auto_Tagging.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOd-Nwzxglej",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://drive.google.com/uc?id=18DLmzIUv3EzOYCcGjIEa2mk07NyRpnPL)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TksF7WqDjDFb",
        "colab_type": "text"
      },
      "source": [
        "# Stack Overflow: Tag Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2VA63BMK4MO",
        "colab_type": "text"
      },
      "source": [
        "# 0. Importing Modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Fd2oPZdAZRe",
        "colab_type": "code",
        "outputId": "42144f93-440e-4813-c3c4-f489b1db933f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import sqlite3\n",
        "import os\n",
        "import csv\n",
        "import re\n",
        "import time\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import SnowballStemmer\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "\n",
        "STACK_OVERFLOW_DIR = r'/content/drive/My Drive/ML Projects/Stack Overflow Tag Prediction/'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPKl--uLjMPk",
        "colab_type": "text"
      },
      "source": [
        "# 1. Business Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeVA7pF1jStk",
        "colab_type": "text"
      },
      "source": [
        "## 1.1 Description"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQYdJWHBjaGg",
        "colab_type": "text"
      },
      "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": {
        "id": "iDOBRJ_3jc5V",
        "colab_type": "text"
      },
      "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": {
        "id": "JUDrrfvljhh0",
        "colab_type": "text"
      },
      "source": [
        "## 1.2 Source"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-96hrA7jmzu",
        "colab_type": "text"
      },
      "source": [
        "Data Source : https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data <br>\n",
        "Research paper : https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tagging-1.pdf <br>\n",
        "Research paper : https://dl.acm.org/citation.cfm?id=2660970&dl=ACM&coll=DL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5FNIfpXjttW",
        "colab_type": "text"
      },
      "source": [
        "## 1.3 Real World / Business Objectives and Constraints"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLZw1DGLjxwc",
        "colab_type": "text"
      },
      "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": {
        "id": "4CwCZEtTj0Bs",
        "colab_type": "text"
      },
      "source": [
        "# 2. Machine Learning problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GO9ax0bj6kE",
        "colab_type": "text"
      },
      "source": [
        "## 2.1 Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Vy77gvQj_m-",
        "colab_type": "text"
      },
      "source": [
        "### 2.1.1 Data Overview\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XACjGkbV--P2",
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      },
      "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": {
        "id": "aslsFOOK_N0m",
        "colab_type": "text"
      },
      "source": [
        "<b>Data Field Explaination</b>\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": {
        "id": "pMPt46pg_hlk",
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      },
      "source": [
        "### 2.1.2 Example Data point"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbBSy-Kk_dPV",
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      "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>"
      ]
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    {
      "cell_type": "markdown",
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        "id": "BFWEww5zAZg9",
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      "source": [
        "## 2.2 Mapping the real-world problem to a Machine Learning Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUw9mfx3Adzs",
        "colab_type": "text"
      },
      "source": [
        "### 2.2.1 Type of Machine Learning Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1_6hSRMAgx1",
        "colab_type": "text"
      },
      "source": [
        "<p> It is a multi-label classification problem  <br>\n",
        "<b>Multi-label Classification</b>: Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A question on Stackoverflow might be about any of C, Pointers, FileIO and/or memory-management at the same time or none of these. <br>\n",
        "<b>Credit</b>: http://scikit-learn.org/stable/modules/multiclass.html\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHkKFg6XAqn6",
        "colab_type": "text"
      },
      "source": [
        "### 2.2.2 Performance metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVbMQEydAtS1",
        "colab_type": "text"
      },
      "source": [
        "<b>Micro-Averaged F1-Score (Mean F Score) </b>: \n",
        "The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:\n",
        "\n",
        "<i>F1 = 2 * (precision * recall) / (precision + recall)</i><br>\n",
        "\n",
        "In the multi-class and multi-label case, this is the weighted average of the F1 score of each class. <br>\n",
        "\n",
        "<b>'Micro f1 score': </b><br>\n",
        "Calculate metrics globally by counting the total true positives, false negatives and false positives. This is a better metric when we have class imbalance.\n",
        "<br>\n",
        "\n",
        "<b>'Macro f1 score': </b><br>\n",
        "Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n",
        "<br>\n",
        "\n",
        "https://www.kaggle.com/wiki/MeanFScore <br>\n",
        "http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html <br>\n",
        "<br>\n",
        "<b> Hamming loss </b>: The Hamming loss is the fraction of labels that are incorrectly predicted. <br>\n",
        "https://www.kaggle.com/wiki/HammingLoss <br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTkGaynzBOsy",
        "colab_type": "text"
      },
      "source": [
        "# 3. Exploratory Data Analysis\n",
        "Because the dataset contains huge amount of data (~7gb). As a workaround to save a space in my google drive, I've already done deduplication into it on my own computer and save the reduced file into database.sqlite file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IevKO0K0BRtD",
        "colab_type": "text"
      },
      "source": [
        "### 3.1.1 Using Pandas with SQLite to Load the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_480BtvkBkKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function for pulling data from the database\n",
        "def get_df_from_db(query):\n",
        "  connected = False\n",
        "  while not connected:\n",
        "    try:\n",
        "      con = sqlite3.connect(STACK_OVERFLOW_DIR + 'database.sqlite')\n",
        "      con.text_factory = lambda b: b.decode(errors = 'ignore')\n",
        "      connected = True\n",
        "    except Exception as e:\n",
        "      print('Connection Failed, retrying connection...')\n",
        "      connected = False\n",
        "    \n",
        "  try:\n",
        "    _df = pd.read_sql_query(query, con)\n",
        "  except:\n",
        "    print('An error occured while running the query')\n",
        "    con.close()\n",
        "    return\n",
        "\n",
        "  con.close()\n",
        "  return _df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxmhWupTCpzd",
        "colab_type": "code",
        "outputId": "94a317ff-ca4b-49e2-c27e-9befd2a3c1ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "df_questions = get_df_from_db('SELECT id, title, body FROM Questions')\n",
        "df_tags = get_df_from_db('SELECT Id, GROUP_CONCAT(LOWER(Tag), \" \") AS Tags FROM Tags GROUP BY Id')\n",
        "df_all = pd.merge(left=df_questions, right=df_tags, how='inner', on='Id')\n",
        "print('Rows: {}\\nColumns: {}'.format(*df_all.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows: 85085\n",
            "Columns: 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlBvUqZ25nGq",
        "colab_type": "code",
        "outputId": "a1c5f878-b0ab-4c62-80fe-922d54374f7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_all.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Title</th>\n",
              "      <th>Body</th>\n",
              "      <th>Tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>The Two Cultures: statistics vs. machine learn...</td>\n",
              "      <td>&lt;p&gt;Last year, I read a blog post from &lt;a href=...</td>\n",
              "      <td>machine-learning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21</td>\n",
              "      <td>Forecasting demographic census</td>\n",
              "      <td>&lt;p&gt;What are some of the ways to forecast demog...</td>\n",
              "      <td>forecasting population census</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>Bayesian and frequentist reasoning in plain En...</td>\n",
              "      <td>&lt;p&gt;How would you describe in plain English the...</td>\n",
              "      <td>bayesian frequentist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>What is the meaning of p values and t values i...</td>\n",
              "      <td>&lt;p&gt;After taking a statistics course and then t...</td>\n",
              "      <td>hypothesis-testing t-test p-value interpretati...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36</td>\n",
              "      <td>Examples for teaching: Correlation does not me...</td>\n",
              "      <td>&lt;p&gt;There is an old saying: \"Correlation does n...</td>\n",
              "      <td>correlation teaching</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  ...                                               Tags\n",
              "0   6  ...                                   machine-learning\n",
              "1  21  ...                      forecasting population census\n",
              "2  22  ...                               bayesian frequentist\n",
              "3  31  ...  hypothesis-testing t-test p-value interpretati...\n",
              "4  36  ...                               correlation teaching\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XfC_sK7GNtsi",
        "colab_type": "code",
        "outputId": "cc8a6ed6-4b64-47ea-ed88-d2cca363a617",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "df_all.isna().sum(axis=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id       0\n",
              "Title    0\n",
              "Body     0\n",
              "Tags     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEVkRnDXEs4N",
        "colab_type": "text"
      },
      "source": [
        "## 3.2 Analysis of Tags "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1EB-1k4wExAJ",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.1 Total number of unique tags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaEegwd3DL_p",
        "colab_type": "code",
        "outputId": "4417f09b-5752-4a06-8dbe-16f280379e14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Get all tags \n",
        "tags = df_all[['Tags']]\n",
        "tags.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>machine-learning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>forecasting population census</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>bayesian frequentist</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hypothesis-testing t-test p-value interpretati...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>correlation teaching</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Tags\n",
              "0                                   machine-learning\n",
              "1                      forecasting population census\n",
              "2                               bayesian frequentist\n",
              "3  hypothesis-testing t-test p-value interpretati...\n",
              "4                               correlation teaching"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8MGzKBIFe5x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer = CountVectorizer(tokenizer=lambda x: x.split())\n",
        "vectorized_tags = vectorizer.fit_transform(tags['Tags'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qOmI1eKF1Rm",
        "colab_type": "code",
        "outputId": "f6f58d02-879e-404b-82d7-0d099ea6597b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('Total Number of Unique Tags: ', vectorized_tags.shape[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Number of Unique Tags:  1315\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDVAUkaqF8b3",
        "colab_type": "code",
        "outputId": "a3e95c93-b276-455d-9e05-d0271cfd3c85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "tag_names = vectorizer.get_feature_names()\n",
        "print('Sample Tags (First 10): ', tag_names[:10])\n",
        "print('Sample Tags (Last 10): ', tag_names[-10:-1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample Tags (First 10):  ['2d', '2sls', 'ab-test', 'abbreviation', 'abc', 'absolute-deviation', 'absolute-risk', 'academia', 'accuracy', 'active-learning']\n",
            "Sample Tags (Last 10):  ['word2vec', 'xgboost', 'xorshift', 'yates-correction', 'z-score', 'z-statistic', 'z-test', 'zero-inflated', 'zero-inflation']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dvp4CHWaGgmB",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.3 Number of times a tag appeared"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9YtKsdkGPbo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Accessing sparse matrix's property\n",
        "freqs = vectorized_tags.sum(axis=0).A1\n",
        "result = dict(zip(tag_names, freqs))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THDt7MzVGh43",
        "colab_type": "code",
        "outputId": "e6f4676a-5a37-4243-f2d3-8110790de406",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Saving the result into a csv file to avoid memory error.\n",
        "if not os.path.isfile(STACK_OVERFLOW_DIR + 'tag_counts_dict_dtm.csv'):\n",
        "  with open(STACK_OVERFLOW_DIR + '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(STACK_OVERFLOW_DIR + 'tag_counts_dict_dtm.csv', names=['Tags', 'Counts'])\n",
        "tag_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2d</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2sls</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ab-test</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>abbreviation</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>abc</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Tags  Counts\n",
              "0            2d       1\n",
              "1          2sls      66\n",
              "2       ab-test     107\n",
              "3  abbreviation       4\n",
              "4           abc      19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btHCf_WNHUHo",
        "colab_type": "code",
        "outputId": "e5294246-d927-4c92-8fdb-5cd7eaf824f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "tag_df_sorted = tag_df.sort_values(by='Counts', ascending=False)\n",
        "tag_counts = tag_df_sorted['Counts'].values\n",
        "tag_df_sorted.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>986</th>\n",
              "      <td>r</td>\n",
              "      <td>13236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1020</th>\n",
              "      <td>regression</td>\n",
              "      <td>10959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>669</th>\n",
              "      <td>machine-learning</td>\n",
              "      <td>6089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1220</th>\n",
              "      <td>time-series</td>\n",
              "      <td>5559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>946</th>\n",
              "      <td>probability</td>\n",
              "      <td>4217</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Tags  Counts\n",
              "986                  r   13236\n",
              "1020        regression   10959\n",
              "669   machine-learning    6089\n",
              "1220       time-series    5559\n",
              "946        probability    4217"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhJLuGOkz3Ir",
        "colab_type": "text"
      },
      "source": [
        "Let's plot these tags to see their distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIKmSepQujN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Plotting the distribution of tag counts\n",
        "def plot_distribution(counts, title, log_scale=False):\n",
        "  plt.figure(figsize=(9, 4))\n",
        "  plt.title(title)\n",
        "  if log_scale:\n",
        "    plt.yscale('log')\n",
        "  plt.grid(True)\n",
        "  plt.plot(counts)\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndJoAXxrx-RP",
        "colab_type": "code",
        "outputId": "2ab225ef-fda4-4b2a-cbd2-a3f6775737dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "plot_distribution(tag_counts, 'Distribution of number of times a tag appeared on questions')\n",
        "print('Values: ', tag_counts[:10000:25])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Values:  [13236  1533   937   654   496   375   317   277   253   228   198   174\n",
            "   154   137   124   114   102    91    84    75    70    62    57    50\n",
            "    45    41    37    33    30    28    25    23    21    20    18    16\n",
            "    14    13    12    10    10     9     8     7     6     5     4     4\n",
            "     3     2     2     1     1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_oPuK0jwurF",
        "colab_type": "code",
        "outputId": "d901cc3c-8790-48d9-aa68-cb16defa9d95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        }
      },
      "source": [
        "# Zooming in, plotting the Top 1000 tags\n",
        "plot_distribution(tag_counts[:1000], '[First 1k tags] Distribution of number of times a tag appeared on questions')\n",
        "print('Values: ', tag_counts[:1000:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Values:  [13236  3869  2871  2054  1776  1533  1344  1209  1057   966   937   898\n",
            "   860   837   745   654   635   617   569   514   496   480   450   431\n",
            "   407   375   370   358   340   332   317   310   302   295   288   277\n",
            "   270   266   260   256   253   245   240   236   232   228   226   217\n",
            "   206   200   198   191   185   180   177   174   172   165   162   156\n",
            "   154   151   148   144   139   137   134   131   128   127   124   122\n",
            "   120   118   116   114   111   109   108   106   102    98    97    94\n",
            "    92    91    90    88    87    85    84    81    79    77    76    75\n",
            "    74    73    72    71    70    68    67    65    63    62    61    60\n",
            "    60    58    57    56    55    53    51    50    49    48    47    46\n",
            "    45    44    44    43    42    41    40    40    39    38    37    37\n",
            "    35    34    33    33    32    31    31    31    30    30    29    29\n",
            "    28    28    28    27    26    26    25    25    24    24    24    23\n",
            "    23    23    22    22    21    21    21    20    20    20    19    19\n",
            "    19    19    18    18    17    17    17    16    16    15    15    15\n",
            "    14    14    14    14    13    13    13    12    12    12    12    11\n",
            "    11    11    11    10    10    10    10    10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Fn33kwxya6f",
        "colab_type": "code",
        "outputId": "bf66260f-aac4-446f-c6a9-a6eccba2a520",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "source": [
        "# Zooming in, plotting the First 500 tags\n",
        "plot_distribution(tag_counts[:500], '[First 500 tags] Distribution of number of times a tag appeared on questions')\n",
        "print('Values: ', tag_counts[:500:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Values:  [13236  3869  2871  2054  1776  1533  1344  1209  1057   966   937   898\n",
            "   860   837   745   654   635   617   569   514   496   480   450   431\n",
            "   407   375   370   358   340   332   317   310   302   295   288   277\n",
            "   270   266   260   256   253   245   240   236   232   228   226   217\n",
            "   206   200   198   191   185   180   177   174   172   165   162   156\n",
            "   154   151   148   144   139   137   134   131   128   127   124   122\n",
            "   120   118   116   114   111   109   108   106   102    98    97    94\n",
            "    92    91    90    88    87    85    84    81    79    77    76    75\n",
            "    74    73    72    71]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UxqXlkQzXky",
        "colab_type": "code",
        "outputId": "db590c54-42d9-409a-e781-d101d6b6a703",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.figure(figsize=(9, 4))\n",
        "plt.title('[First 100 tags] Distribution of number of times a tag appeared on questions')\n",
        "plt.grid(True)\n",
        "plt.plot(tag_counts[:100], color='darkblue')\n",
        "plt.scatter(x=list(range(0, 100, 5)), y=tag_counts[:100:5], color='orange', label='quantiles with 0.05 intervals')\n",
        "plt.scatter(x=list(range(0, 100, 25)), y=tag_counts[:100:25], color='magenta', label='quantiles with 0.25 intervals')\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",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRHRj-1m3SMH",
        "colab_type": "code",
        "outputId": "e98e1af8-ea60-45c3-d5a2-5cca30c1a49a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "list_tags_gt_10k = tag_df.loc[(tag_df['Counts'] > 1000), 'Tags']\n",
        "list_tags_gt_1k = tag_df.loc[(tag_df['Counts'] > 500), 'Tags']\n",
        "\n",
        "print('Tags that are used in more than 10k questions', len(list_tags_gt_10k))\n",
        "print('Tags that are used in more than 1k questions', len(list_tags_gt_1k))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tags that are used in more than 10k questions 43\n",
            "Tags that are used in more than 1k questions 99\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "STAI46VO5nfj",
        "colab_type": "text"
      },
      "source": [
        "<b>Observation(s):</b>\n",
        "\n",
        "1.) There are 43 tags which are used in more than 10k questions.\n",
        "\n",
        "2.) There are 99 tags which are used in more than 1k questions.\n",
        "\n",
        "3.) The distribution of number of times a tag appeared on questions was highly skewed to the left.\n",
        "\n",
        "4.) r was the most used tag (13,236 times) across all of the questions.\n",
        "\n",
        "5.) Since some tags occur more than the others, Micro F1 Score is an appropriate metric to use to gauge the model's precision and recall effectively."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9BL4ClaEjSL",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.4 Tags per Question"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2axxI9cNEnPl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tag_quest_count = list(vectorized_tags.sum(axis=1).A.reshape(-1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqPTK_lrE0ct",
        "colab_type": "code",
        "outputId": "4798d897-74c5-409d-dc94-d70a97ad8acb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(tag_quest_count[:5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 3, 2, 5, 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPQ4zjUtGSw-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tag_quest_count_freq = np.unique(tag_quest_count, return_counts=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6Y1xdo6MVeY",
        "colab_type": "code",
        "outputId": "3168b680-6140-4387-e70d-ab954632bb6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print('Minimum Number of Tags per Question: ', tag_quest_count_freq[0].min())\n",
        "print('Maximum Number of Tags per Question: ', tag_quest_count_freq[0].max())\n",
        "print('Average Number of Tags per Question: ', tag_quest_count_freq[0].mean())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Minimum Number of Tags per Question:  1\n",
            "Maximum Number of Tags per Question:  5\n",
            "Average Number of Tags per Question:  3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxkTmvTgHJqH",
        "colab_type": "code",
        "outputId": "abf2854f-f820-4bec-d296-46d76f41f002",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "sns.FacetGrid('whitegrid', height=4.5, aspect=1.7)\n",
        "bar = sns.barplot(x=tag_quest_count_freq[0], y=tag_quest_count_freq[1], color='slateblue')\n",
        "plt.title('')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 550.8x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQ0pyB5yM-SB",
        "colab_type": "text"
      },
      "source": [
        "<b>Observation(s):</b>\n",
        "\n",
        "1.) The minimum number of tags per question is 1.\n",
        "\n",
        "2.) The maximum number of tags per question is 5.\n",
        "\n",
        "3.) The mean number of tags per question is 3.\n",
        "\n",
        "4.) Most of the questions has 2 or 3 number of tags associated with them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ednep3taTjFW",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.5 Most Frequent Tags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPcshPZiOPlV",
        "colab_type": "code",
        "outputId": "e5e2ab11-a22c-4672-817e-3c1cf885e32c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "wordcloud = WordCloud(background_color='black', width=1280,\n",
        "                      height=720).generate_from_frequencies(result)\n",
        "plt.figure(figsize=(30, 10))\n",
        "plt.imshow(wordcloud)\n",
        "plt.axis(False)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mFoL_2EGUJQe",
        "colab_type": "text"
      },
      "source": [
        "<b>Observation(s):</b>\n",
        "The most dominant tags (in terms of font-size) that we can observe are  r, regression, probability, & time-series."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSJRG0YxAVdq",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.5.1 Shared Word Ratio of Tag to Body/Title"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNH_AvazA2ez",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f1_get_total_common_words(row):\n",
        "  '''Get the intersection between tag and title'''\n",
        "  w1 = set(map(lambda s: s.lower().strip(), row['Title'].split()))\n",
        "  w2 = set(map(lambda s: s.lower().strip(), row['Tags'].split()))\n",
        "  return 1.0 * len(w1&w2)\n",
        "\n",
        "def f1_get_total_words(row):\n",
        "  '''Get the union of tag and title'''\n",
        "  w1 = set(map(lambda s: s.lower().strip(), row['Title'].split()))\n",
        "  w2 = set(map(lambda s: s.lower().strip(), row['Tags'].split()))\n",
        "  return 1.0 * len(w1)+len(w2)\n",
        "\n",
        "def f2_get_total_common_words(row):\n",
        "  '''Get the intersection between tag and body'''\n",
        "  w1 = set(map(lambda s: s.lower().strip(), row['Body'].split()))\n",
        "  w2 = set(map(lambda s: s.lower().strip(), row['Tags'].split()))\n",
        "  return 1.0 * len(w1&w2)\n",
        "\n",
        "def f2_get_total_words(row):\n",
        "  '''Get the union of tag and body'''\n",
        "  w1 = set(map(lambda s: s.lower().strip(), row['Body'].split()))\n",
        "  w2 = set(map(lambda s: s.lower().strip(), row['Tags'].split()))\n",
        "  return 1.0 * len(w1)+len(w2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUJcwngkBKzx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_all['f1_word_common'] = df_all.apply(f1_get_total_common_words, axis=1)\n",
        "df_all['f1_word_total'] = df_all.apply(f1_get_total_words, axis=1)\n",
        "df_all['f1_word_share'] = df_all['f1_word_common']/df_all['f1_word_total']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9NFi7kIB1Ls",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_all['f2_word_common'] = df_all.apply(f2_get_total_common_words, axis=1)\n",
        "df_all['f2_word_total'] = df_all.apply(f2_get_total_words, axis=1)\n",
        "df_all['f2_word_share'] = df_all['f2_word_common']/df_all['f2_word_total']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9MxM0QnChRT",
        "colab_type": "code",
        "outputId": "6ec78d2a-6f1b-4b1f-8788-71b87aa0355c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "plt.figure(figsize=(12, 5))\n",
        "plt.suptitle('Wordshare Distribution')\n",
        "plt.subplot(1,2,1)\n",
        "sns.distplot(a=df_all['f1_word_share'], )\n",
        "plt.xlabel('Title and Tag')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.distplot(a=df_all['f2_word_share'], color='darkorange')\n",
        "plt.xlabel('Body and Tag')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53I5QgENPspj",
        "colab_type": "code",
        "outputId": "0a380308-0af0-4a83-b038-89128694d992",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "print(df_all['f1_word_share'].describe())\n",
        "print()\n",
        "print(df_all['f2_word_share'].describe())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    85085.000000\n",
            "mean         0.051366\n",
            "std          0.070719\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.090909\n",
            "max          0.500000\n",
            "Name: f1_word_share, dtype: float64\n",
            "\n",
            "count    85085.000000\n",
            "mean         0.009174\n",
            "std          0.012351\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.005988\n",
            "75%          0.014085\n",
            "max          0.181818\n",
            "Name: f2_word_share, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XtNy8hup9CLb",
        "colab_type": "code",
        "outputId": "254454b4-35d6-42ab-e181-35d3309dabcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "print(df_all['f1_word_common'].describe())\n",
        "print()\n",
        "print(df_all['f2_word_common'].describe())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    85085.000000\n",
            "mean         0.536193\n",
            "std          0.695407\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          1.000000\n",
            "max          5.000000\n",
            "Name: f1_word_common, dtype: float64\n",
            "\n",
            "count    85085.000000\n",
            "mean         0.782112\n",
            "std          0.847607\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          1.000000\n",
            "75%          1.000000\n",
            "max          5.000000\n",
            "Name: f2_word_common, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyLIc_YHOfXI",
        "colab_type": "text"
      },
      "source": [
        "<b>Observation(s): </b>\n",
        "\n",
        "1.) The word share values for tag & title ranges between 0 and 0.50\n",
        "\n",
        "2.) The word share values for tag & body ranges between 0 and 0.18\n",
        "\n",
        "3.) We can see that there are many words in common between Tag and Title. Logically, as the number of words in a question increases, the number of chances that the Tag is present on that question is higher.\n",
        "\n",
        "4.) The word_share for Title and Tags is higher than Tags and Body. With this findings, we can try to add weights on the Title column. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWnFHWTLQnXQ",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.6 The Top 20 Tags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCP_ZTrYOv93",
        "colab_type": "code",
        "outputId": "b273beb2-917e-49e6-b23e-ea70241d9390",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "x = np.arange(20)\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.title('Frequency of Top 20 Tags')\n",
        "plt.bar(tag_df_sorted['Tags'].iloc[:20], \n",
        "        height=tag_df_sorted['Counts'].iloc[:20],\n",
        "        align='edge', width=0.4, color='slateblue')\n",
        "plt.xticks(rotation=60)\n",
        "plt.xlabel('Tags')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUiVzg1BUpOq",
        "colab_type": "text"
      },
      "source": [
        "<b>Observation(s):</b>\n",
        "\n",
        "1.) r is the most frequent tag across all of the questions.\n",
        "\n",
        "3.) Interestingly, there is a tag related to self-study.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4q9acElVmZj",
        "colab_type": "text"
      },
      "source": [
        "## 3.3 Cleaning and preprocessing of Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHoj_4qSVr0p",
        "colab_type": "text"
      },
      "source": [
        "### 3.3.1 Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1kRrnW9V9lo",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "1.) Separate out code-snippets from Body\n",
        "\n",
        "2.) Remove Special characters from Question title and description (not in code)\n",
        "\n",
        "3.) Remove stop words (Except 'C')\n",
        "\n",
        "4.) Remove HTML Tags\n",
        "\n",
        "5.) Convert all the characters into small letters\n",
        "\n",
        "6.) Use SnowballStemmer to stem the words\n",
        "\n",
        "We will also put 3x more weight to the title of the question"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SqwJZeFJpLf",
        "colab_type": "code",
        "outputId": "3ab911cb-579c-4796-8735-4ae23081a3d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "stopwords = set(stopwords.words('english'))\n",
        "stemmer = SnowballStemmer('english')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzY8W8odVqv_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "code_pattern = r'<code>(.*?)</code>'\n",
        "b_code_pattern = rb'<code>(.*?)</code>'\n",
        "\n",
        "def remove_html_tags(string):\n",
        "  return re.sub('<.*?>', ' ', string).strip()\n",
        "\n",
        "def remove_special_characters(string):\n",
        "  string = re.sub(r'[^a-zA-Z]+', ' ', string)\n",
        "  return string\n",
        "\n",
        "def remove_code_tags(string):\n",
        "  code = re.findall(r'<code>(.*?)</code>', string, flags=re.DOTALL)\n",
        "  if len(code):\n",
        "    return code[0]\n",
        "  else:\n",
        "    return 'no code'\n",
        "\n",
        "def parse_question(string):\n",
        "  question = re.sub(code_pattern, ' ', string, flags=re.DOTALL|re.MULTILINE)\n",
        "  question = remove_html_tags(question)\n",
        "  return question\n",
        "\n",
        "def to_lower(string):\n",
        "  return string.lower()\n",
        "\n",
        "def tokenize(string):\n",
        "  return word_tokenize(string)\n",
        "\n",
        "def remove_stopwords(string):\n",
        "  string_prime = []\n",
        "  for word in tokenize(string):\n",
        "    is_c = (len(word)!=1 or word=='c')\n",
        "    if word not in stopwords and is_c:\n",
        "      string_prime.append(stemmer.stem(word))\n",
        "  return ' '.join(string_prime)\n",
        "\n",
        "def preprocess(df):\n",
        "  df['words_pre'] = len(df['Body']) + len(df['Title'])\n",
        "  df['Body'] = to_lower(df['Body'])\n",
        "  df['is_code'] = 'code' in df['Body']\n",
        "  df['code'] = remove_code_tags(df['Body'])\n",
        "  df['question'] = parse_question(df['Body'])\n",
        "  # Putting 3x more weight on the title of the question\n",
        "  df['question'] = (df['Title'] + ' ')*3 + df['question']\n",
        "  df['question'] = remove_special_characters(df['question'])\n",
        "  df['question'] = remove_stopwords(df['question'])\n",
        "  df['words_post'] = len(df['question'])\n",
        "  \n",
        "  return df.loc[['Title', 'question', 'code', 'is_code', 'words_pre', 'words_post', 'Tags']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMfP0hYoM4mj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Since 4 million datapoints is too large, we will randomly sample 200k datapoints for our experimentation.\n",
        "# start = time.time()\n",
        "# df_no_dup = get_df_from_db('SELECT title, body, tags FROM no_dup_train')\n",
        "# end = time.time()\n",
        "# print('Time consumed to execute the query: ', round(end-start, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VE0m2YyOmtgv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df_no_dup = df_no_dup.sample(n=200000, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SmOviJzY1jo",
        "colab_type": "code",
        "outputId": "582a7624-ad2b-445d-96c3-348dfa7bbf87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "4509ba3f7d234989a1675aa4a54f9dc0",
            "4665b286db98462386a695b9ba9d319d",
            "2b73c0cfe9964a779dd62f9465d55eeb",
            "c7830fe0401648cf9cb4bcd80a66e170",
            "bb93b885f90e4852a303f9418c1a27f3",
            "1870215b5bd34f058455a21ab4b3ef70",
            "927d1a117a7c4d1ba3d996c2b21ac7e5",
            "36eb7f09f26548ca81309570fb41f71d"
          ]
        }
      },
      "source": [
        "tqdm.pandas()\n",
        "df_all = df_all.progress_apply(preprocess, axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4509ba3f7d234989a1675aa4a54f9dc0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=85085.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CC0N1u3ulwEP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conn = sqlite3.connect(STACK_OVERFLOW_DIR + 'Preprocessed.db')\n",
        "df_all.to_sql(index=False, name='preprocessed_questions', \n",
        "                 con=conn, if_exists='replace')\n",
        "conn.commit()\n",
        "conn.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJuZnYI3SO2w",
        "colab_type": "code",
        "outputId": "9f598956-e648-49db-9df7-8973dbaf369b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "conn = sqlite3.connect(STACK_OVERFLOW_DIR + 'Preprocessed.db')\n",
        "print(pd.read_sql('SELECT * FROM preprocessed_questions', conn).shape)\n",
        "conn.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(85085, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJYCpoDMjuvt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "if os.path.isfile(STACK_OVERFLOW_DIR + 'Preprocessed.db'):\n",
        "  conn = sqlite3.connect(STACK_OVERFLOW_DIR + 'Preprocessed.db')\n",
        "  df_all = pd.read_sql('SELECT * FROM preprocessed_questions', con=conn)\n",
        "else: \n",
        "  print('Please download the Preprocessed.db file!')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5ho5TTVSi99",
        "colab_type": "text"
      },
      "source": [
        "# 4. Machine Learning Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91oykPqhStE_",
        "colab_type": "text"
      },
      "source": [
        "## 4.1 Converting Tags for Multi-Labels Problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJspij1ITL9J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_vectorizer = CountVectorizer(tokenizer=lambda x: x.split(), binary=True)\n",
        "multilabel_y = label_vectorizer.fit_transform(df_all['Tags'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cacf9ceRTeTo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJEz_KgXT1ve",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "questions_explained = []\n",
        "total_tags=multilabel_y.shape[1]\n",
        "total_qs=df_all.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))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDXxF1BcT6jO",
        "colab_type": "code",
        "outputId": "627d14f3-af9e-4e4c-eb9b-fd8649ea6bd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "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\n",
        "print('with ', 500, 'tags we are covering ', questions_explained[0], '% of questions')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "with  500 tags we are covering  99.077 % of questions\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PxFgzkYTUEK2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "multilabel_yx = tags_to_choose(500)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1Pz6RnzbEUb",
        "colab_type": "text"
      },
      "source": [
        "## 4.2 Split the data into test and train (80:20)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sm368gaZau_C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_size = df_all.shape[0]\n",
        "train_size = int(0.80*total_size)\n",
        "\n",
        "x_train = df_all.head(train_size)\n",
        "x_test = df_all.tail(total_size - train_size)\n",
        "\n",
        "y_train = multilabel_yx[0:train_size,:]\n",
        "y_test = multilabel_yx[train_size:total_size,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rz69y_yADeHl",
        "colab_type": "code",
        "outputId": "f6c76b57-94b7-47fd-c0cc-4414ddeece5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "print('Dimensions of Training and Test set')\n",
        "print('X Train: ', x_train.shape, '\\nY Train: ', y_train.shape)\n",
        "print('X Test: ',x_test.shape, '\\nY Test: ', y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of Training and Test set\n",
            "X Train:  (68068, 7) \n",
            "Y Train:  (68068, 500)\n",
            "X Test:  (17017, 7) \n",
            "Y Test:  (17017, 500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IoEXm9KfbYDV",
        "colab_type": "text"
      },
      "source": [
        "## 4.3 Featurizing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCxl76xtJmj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import joblib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c20n5ZmnbFWh",
        "colab_type": "code",
        "outputId": "c9b17c86-10f1-4e5a-8ce1-44c62ff4d65d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "start = time.time()\n",
        "vectorizer = TfidfVectorizer(min_df=0.00009, max_features=200000, smooth_idf=True, norm=\"l2\", \\\n",
        "                             tokenizer=lambda x: x.split(), sublinear_tf=False, ngram_range=(1,4))\n",
        "# if os.path.\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 :\", time.time() - start)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time taken to run this cell : 85.9029324054718\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltxcFUnXQ7WL",
        "colab_type": "code",
        "outputId": "6243b81a-2703-41ad-9c56-f94c7cfde4ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "joblib.dump(x_train_multilabel, STACK_OVERFLOW_DIR + 'x_train_multilabel.pkl')\n",
        "joblib.dump(y_train, STACK_OVERFLOW_DIR + 'y_train.pkl')\n",
        "joblib.dump(x_test_multilabel, STACK_OVERFLOW_DIR + 'x_test_multilabel.pkl')\n",
        "joblib.dump(y_test, STACK_OVERFLOW_DIR + 'y_test.pkl')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/ML Projects/Stack Overflow Tag Prediction/y_test.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-CwH2oGPnrx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer.get_feature_names()[500:520]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvk1_GvnbpbL",
        "colab_type": "code",
        "outputId": "d8f2a25d-6aba-465c-b2cb-f9b149a6f8a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train_multilabel.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(68068, 146633)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeS0YuJIImfC",
        "colab_type": "code",
        "outputId": "98646afc-94b8-4aeb-8378-5a04aa1e6e7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# # Save the features as pickle file\n",
        "# joblib.dump(x_train_multilabel, STACK_OVERFLOW_DIR + 'x_train_multilabel.pkl')\n",
        "# joblib.dump(x_test_multilabel, STACK_OVERFLOW_DIR + 'x_test_multilabel.pkl')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/ML Projects/Stack Overflow Tag Prediction/x_test_multilabel.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COC_RnFDAgaZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train_multilabel = joblib.load(STACK_OVERFLOW_DIR + 'x_train_multilabel.pkl')\n",
        "x_test_multilabel = joblib.load(STACK_OVERFLOW_DIR + 'x_test_multilabel.pkl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MX1SMLLwCWEp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_scores(y_true, y_pred):\n",
        "  print(\"accuracy :\",metrics.accuracy_score(y_true,y_pred))\n",
        "  print(\"macro f1 score :\",metrics.f1_score(y_true, y_pred, average = 'macro', zero_division=1))\n",
        "  print(\"micro f1 scoore :\",metrics.f1_score(y_true, y_pred, average = 'micro', zero_division=1))\n",
        "  print(\"hamming loss :\",metrics.hamming_loss(y_true,y_pred))\n",
        "  print(\"Precision recall report :\\n\",metrics.classification_report(y_true, y_pred, zero_division=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_a4fv1p3CsEt",
        "colab_type": "text"
      },
      "source": [
        "## 4.4 Logistic Regression with Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpUJHuhiJGiB",
        "colab_type": "code",
        "outputId": "c8f959f6-9705-427d-b0a1-c859264ca757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "start = time.time()\n",
        "alphas = [1/(10**i) for i in range(4, 10)]\n",
        "lr_params = {'estimator__alpha': alphas}\n",
        "\n",
        "lr_clf = OneVsRestClassifier(SGDClassifier(loss='log', penalty='l1'), n_jobs=3)\n",
        "gridcv_lr = GridSearchCV(estimator=lr_clf, param_grid=lr_params, \n",
        "                         scoring='f1_micro', cv=5, verbose=10,\n",
        "                         return_train_score=True)\n",
        "gridcv_lr.fit(x_train_multilabel, y_train)\n",
        "end = time.time()\n",
        "print('Total runtime of 5 fold GridSearchCV: ', round(end-start, 3), ' (seconds)')\n",
        "\n",
        "start = time.time()\n",
        "gridcv_pred = gridcv_lr.predict(x_test_multilabel)\n",
        "end = time.time()\n",
        "print('Total runtime of Testing/Evaluation: ', round(end-start, 3), ' (seconds)')\n",
        "show_scores(y_test, gridcv_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
            "[CV] estimator__alpha=0.0001 .........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0001, score=(train=0.336, test=0.339), total= 4.2min\n",
            "[CV] estimator__alpha=0.0001 .........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.3min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0001, score=(train=0.335, test=0.331), total= 4.1min\n",
            "[CV] estimator__alpha=0.0001 .........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  8.6min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0001, score=(train=0.338, test=0.326), total= 4.1min\n",
            "[CV] estimator__alpha=0.0001 .........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 12.8min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0001, score=(train=0.336, test=0.335), total= 4.2min\n",
            "[CV] estimator__alpha=0.0001 .........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed: 17.2min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=0.0001, score=(train=0.335, test=0.334), total= 4.1min\n",
            "[CV] estimator__alpha=1e-05 ..........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed: 21.4min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=1e-05, score=(train=0.504, test=0.468), total= 4.3min\n",
            "[CV] estimator__alpha=1e-05 ..........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed: 25.9min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=1e-05, score=(train=0.504, test=0.460), total= 4.3min\n",
            "[CV] estimator__alpha=1e-05 ..........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed: 30.4min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=1e-05, score=(train=0.503, test=0.459), total= 4.3min\n",
            "[CV] estimator__alpha=1e-05 ..........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed: 34.9min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=1e-05, score=(train=0.505, test=0.462), total= 4.3min\n",
            "[CV] estimator__alpha=1e-05 ..........................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed: 39.3min remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV]  estimator__alpha=1e-05, score=(train=0.503, test=0.464), total= 4.3min\n",
            "[CV] estimator__alpha=1e-06 ..........................................\n",
            "[CV]  estimator__alpha=1e-06, score=(train=0.998, test=0.482), total= 4.8min\n",
            "[CV] estimator__alpha=1e-06 ..........................................\n",
            "[CV]  estimator__alpha=1e-06, score=(train=0.998, test=0.475), total= 4.7min\n",
            "[CV] estimator__alpha=1e-06 ..........................................\n",
            "[CV]  estimator__alpha=1e-06, score=(train=0.998, test=0.475), total= 4.7min\n",
            "[CV] estimator__alpha=1e-06 ..........................................\n",
            "[CV]  estimator__alpha=1e-06, score=(train=0.998, test=0.474), total= 4.7min\n",
            "[CV] estimator__alpha=1e-06 ..........................................\n",
            "[CV]  estimator__alpha=1e-06, score=(train=0.998, test=0.478), total= 4.7min\n",
            "[CV] estimator__alpha=1e-07 ..........................................\n",
            "[CV]  estimator__alpha=1e-07, score=(train=1.000, test=0.470), total= 5.1min\n",
            "[CV] estimator__alpha=1e-07 ..........................................\n",
            "[CV]  estimator__alpha=1e-07, score=(train=0.999, test=0.463), total= 5.1min\n",
            "[CV] estimator__alpha=1e-07 ..........................................\n",
            "[CV]  estimator__alpha=1e-07, score=(train=1.000, test=0.466), total= 5.0min\n",
            "[CV] estimator__alpha=1e-07 ..........................................\n",
            "[CV]  estimator__alpha=1e-07, score=(train=1.000, test=0.464), total= 5.3min\n",
            "[CV] estimator__alpha=1e-07 ..........................................\n",
            "[CV]  estimator__alpha=1e-07, score=(train=1.000, test=0.470), total= 5.2min\n",
            "[CV] estimator__alpha=1e-08 ..........................................\n",
            "[CV]  estimator__alpha=1e-08, score=(train=1.000, test=0.465), total= 5.9min\n",
            "[CV] estimator__alpha=1e-08 ..........................................\n",
            "[CV]  estimator__alpha=1e-08, score=(train=0.999, test=0.457), total= 5.8min\n",
            "[CV] estimator__alpha=1e-08 ..........................................\n",
            "[CV]  estimator__alpha=1e-08, score=(train=0.999, test=0.461), total= 5.7min\n",
            "[CV] estimator__alpha=1e-08 ..........................................\n",
            "[CV]  estimator__alpha=1e-08, score=(train=1.000, test=0.457), total= 5.7min\n",
            "[CV] estimator__alpha=1e-08 ..........................................\n",
            "[CV]  estimator__alpha=1e-08, score=(train=1.000, test=0.464), total= 5.7min\n",
            "[CV] estimator__alpha=1e-09 ..........................................\n",
            "[CV]  estimator__alpha=1e-09, score=(train=1.000, test=0.466), total= 6.6min\n",
            "[CV] estimator__alpha=1e-09 ..........................................\n",
            "[CV]  estimator__alpha=1e-09, score=(train=1.000, test=0.458), total= 6.7min\n",
            "[CV] estimator__alpha=1e-09 ..........................................\n",
            "[CV]  estimator__alpha=1e-09, score=(train=1.000, test=0.456), total= 6.4min\n",
            "[CV] estimator__alpha=1e-09 ..........................................\n",
            "[CV]  estimator__alpha=1e-09, score=(train=1.000, test=0.459), total= 6.7min\n",
            "[CV] estimator__alpha=1e-09 ..........................................\n",
            "[CV]  estimator__alpha=1e-09, score=(train=1.000, test=0.465), total= 6.7min\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 158.1min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Total runtime of 3 fold GridSearchCV:  9844.551  (seconds)\n",
            "Total runtime of Testing/Evaluation:  2.383  (seconds)\n",
            "accuracy : 0.09278956337779867\n",
            "macro f1 score : 0.4110979300597982\n",
            "micro f1 scoore : 0.4791012585656174\n",
            "hamming loss : 0.004484926837868015\n",
            "Precision recall report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.45      0.51      2675\n",
            "           1       0.59      0.47      0.52      2189\n",
            "           2       0.49      0.34      0.41      1296\n",
            "           3       0.72      0.54      0.62      1167\n",
            "           4       0.51      0.38      0.44       813\n",
            "           5       0.50      0.33      0.40       772\n",
            "           6       0.45      0.22      0.30       748\n",
            "           7       0.43      0.27      0.33       671\n",
            "           8       0.79      0.62      0.69       657\n",
            "           9       0.58      0.39      0.47       619\n",
            "          10       0.71      0.52      0.60       582\n",
            "          11       0.46      0.22      0.30       526\n",
            "          12       0.73      0.59      0.65       524\n",
            "          13       0.78      0.58      0.67       499\n",
            "          14       0.56      0.37      0.45       435\n",
            "          15       0.51      0.25      0.34       414\n",
            "          16       0.72      0.60      0.65       403\n",
            "          17       0.82      0.64      0.72       380\n",
            "          18       0.86      0.71      0.78       410\n",
            "          19       0.25      0.06      0.09       362\n",
            "          20       0.75      0.57      0.64       348\n",
            "          21       0.54      0.32      0.40       320\n",
            "          22       0.71      0.38      0.50       303\n",
            "          23       0.57      0.35      0.44       337\n",
            "          24       0.73      0.51      0.60       327\n",
            "          25       0.35      0.17      0.23       305\n",
            "          26       0.67      0.49      0.56       304\n",
            "          27       0.56      0.29      0.38       308\n",
            "          28       0.89      0.77      0.82       230\n",
            "          29       0.52      0.29      0.38       275\n",
            "          30       0.81      0.62      0.70       273\n",
            "          31       0.68      0.46      0.55       261\n",
            "          32       0.75      0.47      0.58       234\n",
            "          33       0.89      0.67      0.77       254\n",
            "          34       0.82      0.58      0.68       255\n",
            "          35       0.77      0.60      0.67       226\n",
            "          36       0.35      0.12      0.18       278\n",
            "          37       0.42      0.19      0.26       230\n",
            "          38       0.86      0.61      0.72       205\n",
            "          39       0.66      0.30      0.41       237\n",
            "          40       0.65      0.29      0.40       222\n",
            "          41       0.28      0.05      0.09       201\n",
            "          42       0.52      0.30      0.38       191\n",
            "          43       0.63      0.39      0.48       223\n",
            "          44       0.36      0.17      0.23       177\n",
            "          45       0.74      0.47      0.58       215\n",
            "          46       0.57      0.30      0.39       161\n",
            "          47       0.39      0.13      0.20       181\n",
            "          48       0.65      0.53      0.58       190\n",
            "          49       0.75      0.44      0.55       196\n",
            "          50       0.42      0.10      0.16       198\n",
            "          51       0.66      0.44      0.53       177\n",
            "          52       0.93      0.73      0.82       210\n",
            "          53       0.66      0.38      0.48       168\n",
            "          54       0.74      0.72      0.73       162\n",
            "          55       0.52      0.32      0.39       176\n",
            "          56       0.66      0.35      0.46       173\n",
            "          57       0.50      0.20      0.29       176\n",
            "          58       0.68      0.52      0.59       163\n",
            "          59       0.72      0.51      0.60       179\n",
            "          60       0.36      0.11      0.17       205\n",
            "          61       0.56      0.37      0.44       161\n",
            "          62       0.41      0.17      0.24       163\n",
            "          63       0.75      0.52      0.61       167\n",
            "          64       0.28      0.06      0.10       191\n",
            "          65       0.55      0.32      0.40       163\n",
            "          66       0.23      0.12      0.15       138\n",
            "          67       0.61      0.28      0.38       167\n",
            "          68       0.95      0.73      0.82       148\n",
            "          69       0.51      0.30      0.38       144\n",
            "          70       0.45      0.21      0.29       135\n",
            "          71       0.68      0.48      0.56       134\n",
            "          72       0.60      0.39      0.47       137\n",
            "          73       0.60      0.30      0.40       136\n",
            "          74       0.75      0.45      0.56       144\n",
            "          75       0.59      0.27      0.37       132\n",
            "          76       0.63      0.47      0.54       126\n",
            "          77       0.64      0.33      0.44       130\n",
            "          78       0.35      0.16      0.22       135\n",
            "          79       0.76      0.57      0.65       107\n",
            "          80       0.69      0.50      0.58       107\n",
            "          81       0.87      0.66      0.75       128\n",
            "          82       0.49      0.39      0.44       102\n",
            "          83       0.44      0.06      0.10       118\n",
            "          84       0.68      0.54      0.60       128\n",
            "          85       0.65      0.40      0.49       129\n",
            "          86       0.59      0.45      0.51       128\n",
            "          87       0.78      0.55      0.64       142\n",
            "          88       0.55      0.41      0.47       116\n",
            "          89       0.42      0.17      0.24       109\n",
            "          90       0.57      0.42      0.48       103\n",
            "          91       0.65      0.30      0.41       119\n",
            "          92       0.11      0.01      0.02        93\n",
            "          93       0.50      0.12      0.19       112\n",
            "          94       0.55      0.23      0.32       106\n",
            "          95       0.58      0.18      0.27       107\n",
            "          96       0.75      0.65      0.69        82\n",
            "          97       0.67      0.42      0.52        92\n",
            "          98       0.54      0.37      0.44        97\n",
            "          99       0.36      0.10      0.15       102\n",
            "         100       0.80      0.57      0.66       113\n",
            "         101       0.82      0.65      0.73        98\n",
            "         102       0.58      0.33      0.42        98\n",
            "         103       0.65      0.41      0.50       102\n",
            "         104       0.28      0.10      0.15       101\n",
            "         105       0.58      0.30      0.39       114\n",
            "         106       0.89      0.71      0.79        72\n",
            "         107       0.65      0.44      0.53       102\n",
            "         108       0.77      0.53      0.63        90\n",
            "         109       0.67      0.33      0.44        88\n",
            "         110       0.27      0.08      0.13        86\n",
            "         111       0.77      0.51      0.61        95\n",
            "         112       0.70      0.37      0.49        70\n",
            "         113       0.80      0.50      0.62        90\n",
            "         114       0.59      0.36      0.45        98\n",
            "         115       0.77      0.70      0.73        79\n",
            "         116       0.33      0.04      0.07        81\n",
            "         117       0.84      0.38      0.52       101\n",
            "         118       0.45      0.28      0.35        68\n",
            "         119       0.36      0.13      0.19        78\n",
            "         120       0.62      0.36      0.46        83\n",
            "         121       0.47      0.24      0.32        75\n",
            "         122       0.91      0.66      0.76        91\n",
            "         123       0.64      0.33      0.43        85\n",
            "         124       0.62      0.49      0.54        76\n",
            "         125       0.81      0.51      0.63        74\n",
            "         126       0.55      0.30      0.39        79\n",
            "         127       0.54      0.20      0.30        64\n",
            "         128       0.38      0.16      0.23        61\n",
            "         129       0.52      0.23      0.32        64\n",
            "         130       0.67      0.47      0.56        78\n",
            "         131       0.77      0.73      0.75        73\n",
            "         132       0.61      0.41      0.49        66\n",
            "         133       0.74      0.38      0.50        68\n",
            "         134       0.64      0.47      0.54        64\n",
            "         135       0.76      0.54      0.63        69\n",
            "         136       0.25      0.06      0.09        69\n",
            "         137       0.50      0.01      0.03        69\n",
            "         138       0.54      0.32      0.40        65\n",
            "         139       0.53      0.28      0.37        75\n",
            "         140       0.46      0.33      0.39        69\n",
            "         141       0.67      0.39      0.49        83\n",
            "         142       0.14      0.02      0.03        57\n",
            "         143       0.62      0.43      0.51        67\n",
            "         144       0.57      0.36      0.44        66\n",
            "         145       0.29      0.16      0.21        61\n",
            "         146       0.62      0.46      0.53        56\n",
            "         147       0.71      0.54      0.61        65\n",
            "         148       0.65      0.40      0.49        55\n",
            "         149       0.86      0.59      0.70        71\n",
            "         150       0.30      0.11      0.17        61\n",
            "         151       0.71      0.44      0.55        45\n",
            "         152       0.92      0.73      0.81        75\n",
            "         153       0.71      0.54      0.61        67\n",
            "         154       0.37      0.10      0.15        72\n",
            "         155       0.85      0.61      0.71        66\n",
            "         156       0.00      0.00      0.00        50\n",
            "         157       0.48      0.34      0.40        67\n",
            "         158       0.50      0.05      0.09        58\n",
            "         159       0.48      0.22      0.31        67\n",
            "         160       0.94      0.81      0.87        62\n",
            "         161       0.94      0.59      0.73        54\n",
            "         162       0.68      0.30      0.42        50\n",
            "         163       0.56      0.40      0.46        58\n",
            "         164       0.46      0.20      0.28        56\n",
            "         165       0.59      0.34      0.43        59\n",
            "         166       0.32      0.12      0.18        57\n",
            "         167       0.77      0.45      0.57        67\n",
            "         168       0.49      0.29      0.36        63\n",
            "         169       0.88      0.56      0.69        66\n",
            "         170       0.43      0.15      0.23        59\n",
            "         171       0.69      0.55      0.61        49\n",
            "         172       0.69      0.44      0.54        54\n",
            "         173       0.56      0.31      0.39        49\n",
            "         174       0.38      0.20      0.27        54\n",
            "         175       0.74      0.41      0.53        56\n",
            "         176       0.71      0.56      0.62        43\n",
            "         177       0.71      0.54      0.61        65\n",
            "         178       0.27      0.07      0.11        55\n",
            "         179       0.64      0.57      0.60        37\n",
            "         180       0.87      0.54      0.67        50\n",
            "         181       0.65      0.43      0.51        47\n",
            "         182       0.83      0.80      0.81        54\n",
            "         183       0.75      0.36      0.48        59\n",
            "         184       0.25      0.09      0.13        56\n",
            "         185       0.81      0.44      0.57        48\n",
            "         186       0.33      0.11      0.16        56\n",
            "         187       0.70      0.57      0.63        46\n",
            "         188       0.67      0.37      0.48        54\n",
            "         189       0.56      0.48      0.51        42\n",
            "         190       0.94      0.55      0.69        60\n",
            "         191       0.89      0.56      0.69        57\n",
            "         192       0.33      0.13      0.19        52\n",
            "         193       0.61      0.34      0.44        50\n",
            "         194       0.70      0.30      0.42        46\n",
            "         195       0.20      0.02      0.03        64\n",
            "         196       0.40      0.27      0.32        45\n",
            "         197       0.83      0.36      0.50        56\n",
            "         198       0.68      0.36      0.47        47\n",
            "         199       0.59      0.24      0.34        55\n",
            "         200       0.45      0.17      0.25        58\n",
            "         201       0.96      0.54      0.69        46\n",
            "         202       0.48      0.28      0.35        43\n",
            "         203       0.35      0.17      0.23        46\n",
            "         204       0.53      0.20      0.30        44\n",
            "         205       0.76      0.31      0.44        62\n",
            "         206       0.48      0.27      0.35        48\n",
            "         207       0.59      0.32      0.42        50\n",
            "         208       0.62      0.28      0.38        47\n",
            "         209       0.72      0.57      0.64        49\n",
            "         210       0.84      0.56      0.67        57\n",
            "         211       0.43      0.34      0.38        47\n",
            "         212       0.76      0.50      0.60        52\n",
            "         213       0.59      0.30      0.40        43\n",
            "         214       0.41      0.25      0.31        44\n",
            "         215       0.73      0.51      0.60        43\n",
            "         216       0.94      0.48      0.63        61\n",
            "         217       0.70      0.65      0.67        46\n",
            "         218       0.85      0.67      0.75        52\n",
            "         219       0.27      0.17      0.21        41\n",
            "         220       0.22      0.11      0.15        37\n",
            "         221       0.31      0.11      0.16        37\n",
            "         222       0.26      0.11      0.16        44\n",
            "         223       0.59      0.30      0.40        43\n",
            "         224       0.83      0.62      0.71        48\n",
            "         225       0.54      0.17      0.25        42\n",
            "         226       0.78      0.61      0.68        41\n",
            "         227       0.56      0.30      0.39        50\n",
            "         228       0.85      0.55      0.67        42\n",
            "         229       0.00      0.00      0.00        43\n",
            "         230       0.77      0.68      0.72        50\n",
            "         231       0.54      0.28      0.37        47\n",
            "         232       0.68      0.36      0.47        47\n",
            "         233       0.30      0.17      0.21        36\n",
            "         234       0.33      0.09      0.14        46\n",
            "         235       0.45      0.23      0.30        40\n",
            "         236       0.76      0.49      0.59        45\n",
            "         237       0.17      0.03      0.05        32\n",
            "         238       0.56      0.21      0.30        48\n",
            "         239       0.56      0.17      0.26        52\n",
            "         240       0.91      0.68      0.78        47\n",
            "         241       0.60      0.34      0.43        44\n",
            "         242       0.50      0.05      0.09        41\n",
            "         243       0.81      0.63      0.71        35\n",
            "         244       1.00      0.64      0.78        39\n",
            "         245       0.15      0.07      0.10        29\n",
            "         246       0.92      0.56      0.70        39\n",
            "         247       0.82      0.39      0.53        36\n",
            "         248       0.56      0.21      0.31        47\n",
            "         249       0.78      0.32      0.45        57\n",
            "         250       0.17      0.08      0.11        25\n",
            "         251       0.61      0.27      0.37        41\n",
            "         252       0.71      0.15      0.24        34\n",
            "         253       0.14      0.03      0.05        33\n",
            "         254       0.92      0.59      0.72        39\n",
            "         255       0.38      0.12      0.19        41\n",
            "         256       0.46      0.30      0.36        37\n",
            "         257       0.14      0.03      0.04        38\n",
            "         258       0.64      0.19      0.30        36\n",
            "         259       0.80      0.54      0.65        37\n",
            "         260       0.72      0.50      0.59        36\n",
            "         261       0.75      0.21      0.33        43\n",
            "         262       0.43      0.30      0.35        30\n",
            "         263       0.77      0.47      0.59        36\n",
            "         264       0.84      0.69      0.76        45\n",
            "         265       0.89      0.44      0.59        36\n",
            "         266       0.70      0.41      0.52        34\n",
            "         267       1.00      0.60      0.75        40\n",
            "         268       0.91      0.53      0.67        40\n",
            "         269       0.33      0.18      0.24        22\n",
            "         270       0.75      0.58      0.65        26\n",
            "         271       0.75      0.09      0.15        35\n",
            "         272       0.65      0.42      0.52        40\n",
            "         273       0.71      0.33      0.45        30\n",
            "         274       0.33      0.25      0.29        24\n",
            "         275       0.76      0.42      0.54        31\n",
            "         276       0.84      0.75      0.79        28\n",
            "         277       0.68      0.41      0.51        37\n",
            "         278       0.75      0.41      0.53        37\n",
            "         279       0.17      0.07      0.10        30\n",
            "         280       1.00      0.21      0.34        34\n",
            "         281       0.74      0.60      0.66        42\n",
            "         282       0.62      0.35      0.45        37\n",
            "         283       0.84      0.50      0.63        32\n",
            "         284       0.80      0.12      0.22        32\n",
            "         285       0.80      0.52      0.63        31\n",
            "         286       0.63      0.42      0.51        40\n",
            "         287       0.29      0.06      0.10        34\n",
            "         288       0.43      0.36      0.39        25\n",
            "         289       0.90      0.84      0.87        31\n",
            "         290       0.00      0.00      0.00        36\n",
            "         291       0.81      0.61      0.70        36\n",
            "         292       0.79      0.39      0.53        38\n",
            "         293       0.79      0.45      0.58        33\n",
            "         294       0.73      0.50      0.59        32\n",
            "         295       0.00      0.00      0.00        27\n",
            "         296       0.36      0.17      0.24        23\n",
            "         297       0.30      0.10      0.15        29\n",
            "         298       0.86      0.73      0.79        33\n",
            "         299       0.20      0.09      0.13        33\n",
            "         300       0.67      0.37      0.48        27\n",
            "         301       0.27      0.15      0.20        26\n",
            "         302       0.35      0.22      0.27        27\n",
            "         303       0.20      0.04      0.06        28\n",
            "         304       0.50      0.15      0.23        33\n",
            "         305       0.62      0.13      0.21        39\n",
            "         306       0.85      0.82      0.84        34\n",
            "         307       0.71      0.19      0.30        26\n",
            "         308       0.78      0.54      0.64        26\n",
            "         309       0.76      0.33      0.46        40\n",
            "         310       0.86      0.64      0.73        28\n",
            "         311       0.36      0.14      0.20        29\n",
            "         312       0.60      0.21      0.31        29\n",
            "         313       0.70      0.57      0.63        28\n",
            "         314       0.85      0.55      0.67        31\n",
            "         315       0.28      0.18      0.22        28\n",
            "         316       0.00      0.00      0.00        23\n",
            "         317       0.47      0.24      0.32        29\n",
            "         318       1.00      0.75      0.86        24\n",
            "         319       0.71      0.54      0.61        28\n",
            "         320       0.84      0.55      0.67        29\n",
            "         321       0.45      0.19      0.26        27\n",
            "         322       0.00      0.00      0.00        45\n",
            "         323       0.61      0.48      0.54        23\n",
            "         324       0.81      0.52      0.63        25\n",
            "         325       0.69      0.41      0.51        27\n",
            "         326       0.00      0.00      0.00        33\n",
            "         327       0.60      0.26      0.36        23\n",
            "         328       0.81      0.52      0.63        25\n",
            "         329       0.78      0.55      0.64        33\n",
            "         330       0.50      0.12      0.20        24\n",
            "         331       0.73      0.61      0.67        31\n",
            "         332       0.25      0.07      0.11        28\n",
            "         333       0.50      0.14      0.22        29\n",
            "         334       0.72      0.43      0.54        30\n",
            "         335       0.20      0.03      0.06        29\n",
            "         336       0.80      0.28      0.41        29\n",
            "         337       0.83      0.66      0.73        29\n",
            "         338       0.88      0.33      0.48        21\n",
            "         339       0.95      0.61      0.75        31\n",
            "         340       0.36      0.15      0.22        26\n",
            "         341       0.57      0.14      0.23        28\n",
            "         342       0.36      0.19      0.25        21\n",
            "         343       0.55      0.19      0.28        32\n",
            "         344       0.68      0.54      0.60        24\n",
            "         345       0.23      0.12      0.16        24\n",
            "         346       0.67      0.42      0.51        24\n",
            "         347       0.00      0.00      0.00        20\n",
            "         348       0.11      0.04      0.06        23\n",
            "         349       0.76      0.48      0.59        27\n",
            "         350       0.87      0.87      0.87        15\n",
            "         351       0.44      0.17      0.25        23\n",
            "         352       0.38      0.13      0.19        23\n",
            "         353       0.88      0.27      0.41        26\n",
            "         354       0.88      0.52      0.65        27\n",
            "         355       0.50      0.29      0.37        24\n",
            "         356       0.60      0.25      0.35        24\n",
            "         357       0.80      0.44      0.57        18\n",
            "         358       0.73      0.34      0.47        32\n",
            "         359       0.71      0.60      0.65        25\n",
            "         360       0.60      0.24      0.34        25\n",
            "         361       0.78      0.28      0.41        25\n",
            "         362       0.61      0.44      0.51        25\n",
            "         363       0.60      0.18      0.27        34\n",
            "         364       0.67      0.23      0.34        26\n",
            "         365       0.25      0.05      0.08        20\n",
            "         366       1.00      0.05      0.10        20\n",
            "         367       0.62      0.29      0.40        17\n",
            "         368       0.83      0.36      0.50        28\n",
            "         369       0.62      0.22      0.32        23\n",
            "         370       0.00      0.00      0.00        19\n",
            "         371       0.17      0.04      0.06        27\n",
            "         372       0.80      0.55      0.65        22\n",
            "         373       0.73      0.28      0.40        29\n",
            "         374       0.62      0.23      0.33        22\n",
            "         375       0.67      0.08      0.14        26\n",
            "         376       0.55      0.30      0.39        20\n",
            "         377       0.40      0.13      0.20        15\n",
            "         378       0.50      0.32      0.39        19\n",
            "         379       0.60      0.40      0.48        15\n",
            "         380       0.00      0.00      0.00        11\n",
            "         381       0.89      0.68      0.77        25\n",
            "         382       0.67      0.32      0.43        19\n",
            "         383       0.80      0.38      0.52        21\n",
            "         384       0.85      0.52      0.65        21\n",
            "         385       0.57      0.15      0.24        26\n",
            "         386       0.50      0.38      0.43        21\n",
            "         387       0.70      0.58      0.64        24\n",
            "         388       0.83      0.58      0.68        26\n",
            "         389       0.67      0.26      0.38        23\n",
            "         390       0.00      0.00      0.00        22\n",
            "         391       0.83      0.20      0.32        25\n",
            "         392       0.71      0.28      0.40        18\n",
            "         393       0.75      0.55      0.63        22\n",
            "         394       0.88      0.32      0.47        22\n",
            "         395       0.50      0.44      0.47        16\n",
            "         396       0.39      0.50      0.44        14\n",
            "         397       0.80      0.44      0.57        18\n",
            "         398       0.20      0.10      0.13        21\n",
            "         399       0.67      0.15      0.25        13\n",
            "         400       0.68      0.46      0.55        28\n",
            "         401       0.20      0.05      0.07        22\n",
            "         402       0.67      0.32      0.43        19\n",
            "         403       0.20      0.05      0.08        19\n",
            "         404       1.00      0.05      0.09        22\n",
            "         405       0.36      0.27      0.31        15\n",
            "         406       1.00      0.59      0.74        17\n",
            "         407       0.00      0.00      0.00        17\n",
            "         408       0.75      0.50      0.60        18\n",
            "         409       0.50      0.12      0.19        25\n",
            "         410       0.64      0.47      0.54        15\n",
            "         411       0.00      0.00      0.00        13\n",
            "         412       0.71      0.48      0.57        25\n",
            "         413       0.83      0.68      0.75        22\n",
            "         414       0.88      0.71      0.79        21\n",
            "         415       0.00      0.00      0.00        15\n",
            "         416       0.38      0.16      0.22        19\n",
            "         417       0.33      0.05      0.09        20\n",
            "         418       0.83      0.62      0.71        16\n",
            "         419       0.29      0.11      0.15        19\n",
            "         420       0.67      0.53      0.59        15\n",
            "         421       0.71      0.48      0.57        21\n",
            "         422       0.33      0.27      0.30        15\n",
            "         423       0.56      0.23      0.32        22\n",
            "         424       0.58      0.27      0.37        26\n",
            "         425       0.00      0.00      0.00         7\n",
            "         426       0.00      0.00      0.00        16\n",
            "         427       0.78      0.61      0.68        23\n",
            "         428       0.17      0.07      0.10        14\n",
            "         429       0.73      0.55      0.63        20\n",
            "         430       0.60      0.15      0.24        20\n",
            "         431       0.60      0.29      0.39        21\n",
            "         432       0.86      0.63      0.73        19\n",
            "         433       0.00      0.00      0.00        24\n",
            "         434       0.62      0.45      0.53        11\n",
            "         435       0.30      0.16      0.21        19\n",
            "         436       0.00      0.00      0.00        24\n",
            "         437       0.79      0.52      0.63        21\n",
            "         438       0.33      0.07      0.12        14\n",
            "         439       0.00      0.00      0.00        20\n",
            "         440       0.29      0.11      0.16        18\n",
            "         441       0.67      0.57      0.62        14\n",
            "         442       0.58      0.29      0.39        24\n",
            "         443       0.00      0.00      0.00        25\n",
            "         444       0.44      0.25      0.32        16\n",
            "         445       0.50      0.40      0.44        15\n",
            "         446       0.25      0.12      0.16        17\n",
            "         447       0.75      0.38      0.50        16\n",
            "         448       0.00      0.00      0.00        15\n",
            "         449       0.73      0.40      0.52        20\n",
            "         450       1.00      0.11      0.19        19\n",
            "         451       0.93      0.67      0.78        21\n",
            "         452       0.85      0.69      0.76        16\n",
            "         453       0.00      0.00      0.00        15\n",
            "         454       0.80      0.24      0.36        17\n",
            "         455       0.25      0.21      0.23        14\n",
            "         456       0.00      0.00      0.00        12\n",
            "         457       0.40      0.20      0.27        20\n",
            "         458       0.17      0.10      0.12        10\n",
            "         459       0.00      0.00      0.00        20\n",
            "         460       0.56      0.45      0.50        11\n",
            "         461       0.43      0.16      0.23        19\n",
            "         462       0.00      0.00      0.00        17\n",
            "         463       0.75      0.47      0.58        19\n",
            "         464       0.86      0.60      0.71        10\n",
            "         465       0.42      0.38      0.40        13\n",
            "         466       0.38      0.17      0.23        18\n",
            "         467       0.72      0.65      0.68        20\n",
            "         468       0.80      0.19      0.31        21\n",
            "         469       0.50      0.06      0.11        17\n",
            "         470       0.43      0.21      0.29        14\n",
            "         471       0.69      0.47      0.56        19\n",
            "         472       0.57      0.29      0.38        14\n",
            "         473       0.40      0.11      0.17        18\n",
            "         474       0.00      0.00      0.00        16\n",
            "         475       0.75      0.18      0.29        17\n",
            "         476       0.50      0.04      0.08        23\n",
            "         477       0.25      0.06      0.10        16\n",
            "         478       0.33      0.08      0.12        13\n",
            "         479       1.00      0.06      0.12        16\n",
            "         480       0.50      0.31      0.38        13\n",
            "         481       0.45      0.45      0.45        11\n",
            "         482       0.88      0.47      0.61        15\n",
            "         483       0.25      0.10      0.14        10\n",
            "         484       0.44      0.33      0.38        12\n",
            "         485       0.75      0.33      0.46         9\n",
            "         486       1.00      0.11      0.20        18\n",
            "         487       0.60      0.43      0.50        14\n",
            "         488       0.78      0.37      0.50        19\n",
            "         489       0.00      0.00      0.00        18\n",
            "         490       0.60      0.43      0.50        14\n",
            "         491       0.80      0.36      0.50        11\n",
            "         492       0.50      0.19      0.28        21\n",
            "         493       0.75      0.53      0.62        17\n",
            "         494       0.67      0.40      0.50        10\n",
            "         495       0.55      0.75      0.63         8\n",
            "         496       0.75      0.30      0.43        10\n",
            "         497       0.33      0.05      0.08        21\n",
            "         498       0.50      0.33      0.40        15\n",
            "         499       0.82      0.53      0.64        17\n",
            "\n",
            "   micro avg       0.63      0.39      0.48     45465\n",
            "   macro avg       0.58      0.34      0.41     45465\n",
            "weighted avg       0.60      0.39      0.46     45465\n",
            " samples avg       0.56      0.41      0.44     45465\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X8F6DweOBny6",
        "colab_type": "code",
        "outputId": "2534dd0e-5c3c-4a11-b8f6-d0f19458b935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Save the best estimator\n",
        "joblib.dump(gridcv_lr.best_estimator_, STACK_OVERFLOW_DIR + 'LR_BestEstimator.pkl')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/ML Projects/Stack Overflow Tag Prediction/LR_BestEstimator.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEtwXrjYJuCY",
        "colab_type": "code",
        "outputId": "f4808d10-4059-4303-82e9-d8629559a340",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "show_scores(y_test, gridcv_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy : 0.09278956337779867\n",
            "macro f1 score : 0.4110979300597982\n",
            "micro f1 scoore : 0.4791012585656174\n",
            "hamming loss : 0.004484926837868015\n",
            "Precision recall report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.45      0.51      2675\n",
            "           1       0.59      0.47      0.52      2189\n",
            "           2       0.49      0.34      0.41      1296\n",
            "           3       0.72      0.54      0.62      1167\n",
            "           4       0.51      0.38      0.44       813\n",
            "           5       0.50      0.33      0.40       772\n",
            "           6       0.45      0.22      0.30       748\n",
            "           7       0.43      0.27      0.33       671\n",
            "           8       0.79      0.62      0.69       657\n",
            "           9       0.58      0.39      0.47       619\n",
            "          10       0.71      0.52      0.60       582\n",
            "          11       0.46      0.22      0.30       526\n",
            "          12       0.73      0.59      0.65       524\n",
            "          13       0.78      0.58      0.67       499\n",
            "          14       0.56      0.37      0.45       435\n",
            "          15       0.51      0.25      0.34       414\n",
            "          16       0.72      0.60      0.65       403\n",
            "          17       0.82      0.64      0.72       380\n",
            "          18       0.86      0.71      0.78       410\n",
            "          19       0.25      0.06      0.09       362\n",
            "          20       0.75      0.57      0.64       348\n",
            "          21       0.54      0.32      0.40       320\n",
            "          22       0.71      0.38      0.50       303\n",
            "          23       0.57      0.35      0.44       337\n",
            "          24       0.73      0.51      0.60       327\n",
            "          25       0.35      0.17      0.23       305\n",
            "          26       0.67      0.49      0.56       304\n",
            "          27       0.56      0.29      0.38       308\n",
            "          28       0.89      0.77      0.82       230\n",
            "          29       0.52      0.29      0.38       275\n",
            "          30       0.81      0.62      0.70       273\n",
            "          31       0.68      0.46      0.55       261\n",
            "          32       0.75      0.47      0.58       234\n",
            "          33       0.89      0.67      0.77       254\n",
            "          34       0.82      0.58      0.68       255\n",
            "          35       0.77      0.60      0.67       226\n",
            "          36       0.35      0.12      0.18       278\n",
            "          37       0.42      0.19      0.26       230\n",
            "          38       0.86      0.61      0.72       205\n",
            "          39       0.66      0.30      0.41       237\n",
            "          40       0.65      0.29      0.40       222\n",
            "          41       0.28      0.05      0.09       201\n",
            "          42       0.52      0.30      0.38       191\n",
            "          43       0.63      0.39      0.48       223\n",
            "          44       0.36      0.17      0.23       177\n",
            "          45       0.74      0.47      0.58       215\n",
            "          46       0.57      0.30      0.39       161\n",
            "          47       0.39      0.13      0.20       181\n",
            "          48       0.65      0.53      0.58       190\n",
            "          49       0.75      0.44      0.55       196\n",
            "          50       0.42      0.10      0.16       198\n",
            "          51       0.66      0.44      0.53       177\n",
            "          52       0.93      0.73      0.82       210\n",
            "          53       0.66      0.38      0.48       168\n",
            "          54       0.74      0.72      0.73       162\n",
            "          55       0.52      0.32      0.39       176\n",
            "          56       0.66      0.35      0.46       173\n",
            "          57       0.50      0.20      0.29       176\n",
            "          58       0.68      0.52      0.59       163\n",
            "          59       0.72      0.51      0.60       179\n",
            "          60       0.36      0.11      0.17       205\n",
            "          61       0.56      0.37      0.44       161\n",
            "          62       0.41      0.17      0.24       163\n",
            "          63       0.75      0.52      0.61       167\n",
            "          64       0.28      0.06      0.10       191\n",
            "          65       0.55      0.32      0.40       163\n",
            "          66       0.23      0.12      0.15       138\n",
            "          67       0.61      0.28      0.38       167\n",
            "          68       0.95      0.73      0.82       148\n",
            "          69       0.51      0.30      0.38       144\n",
            "          70       0.45      0.21      0.29       135\n",
            "          71       0.68      0.48      0.56       134\n",
            "          72       0.60      0.39      0.47       137\n",
            "          73       0.60      0.30      0.40       136\n",
            "          74       0.75      0.45      0.56       144\n",
            "          75       0.59      0.27      0.37       132\n",
            "          76       0.63      0.47      0.54       126\n",
            "          77       0.64      0.33      0.44       130\n",
            "          78       0.35      0.16      0.22       135\n",
            "          79       0.76      0.57      0.65       107\n",
            "          80       0.69      0.50      0.58       107\n",
            "          81       0.87      0.66      0.75       128\n",
            "          82       0.49      0.39      0.44       102\n",
            "          83       0.44      0.06      0.10       118\n",
            "          84       0.68      0.54      0.60       128\n",
            "          85       0.65      0.40      0.49       129\n",
            "          86       0.59      0.45      0.51       128\n",
            "          87       0.78      0.55      0.64       142\n",
            "          88       0.55      0.41      0.47       116\n",
            "          89       0.42      0.17      0.24       109\n",
            "          90       0.57      0.42      0.48       103\n",
            "          91       0.65      0.30      0.41       119\n",
            "          92       0.11      0.01      0.02        93\n",
            "          93       0.50      0.12      0.19       112\n",
            "          94       0.55      0.23      0.32       106\n",
            "          95       0.58      0.18      0.27       107\n",
            "          96       0.75      0.65      0.69        82\n",
            "          97       0.67      0.42      0.52        92\n",
            "          98       0.54      0.37      0.44        97\n",
            "          99       0.36      0.10      0.15       102\n",
            "         100       0.80      0.57      0.66       113\n",
            "         101       0.82      0.65      0.73        98\n",
            "         102       0.58      0.33      0.42        98\n",
            "         103       0.65      0.41      0.50       102\n",
            "         104       0.28      0.10      0.15       101\n",
            "         105       0.58      0.30      0.39       114\n",
            "         106       0.89      0.71      0.79        72\n",
            "         107       0.65      0.44      0.53       102\n",
            "         108       0.77      0.53      0.63        90\n",
            "         109       0.67      0.33      0.44        88\n",
            "         110       0.27      0.08      0.13        86\n",
            "         111       0.77      0.51      0.61        95\n",
            "         112       0.70      0.37      0.49        70\n",
            "         113       0.80      0.50      0.62        90\n",
            "         114       0.59      0.36      0.45        98\n",
            "         115       0.77      0.70      0.73        79\n",
            "         116       0.33      0.04      0.07        81\n",
            "         117       0.84      0.38      0.52       101\n",
            "         118       0.45      0.28      0.35        68\n",
            "         119       0.36      0.13      0.19        78\n",
            "         120       0.62      0.36      0.46        83\n",
            "         121       0.47      0.24      0.32        75\n",
            "         122       0.91      0.66      0.76        91\n",
            "         123       0.64      0.33      0.43        85\n",
            "         124       0.62      0.49      0.54        76\n",
            "         125       0.81      0.51      0.63        74\n",
            "         126       0.55      0.30      0.39        79\n",
            "         127       0.54      0.20      0.30        64\n",
            "         128       0.38      0.16      0.23        61\n",
            "         129       0.52      0.23      0.32        64\n",
            "         130       0.67      0.47      0.56        78\n",
            "         131       0.77      0.73      0.75        73\n",
            "         132       0.61      0.41      0.49        66\n",
            "         133       0.74      0.38      0.50        68\n",
            "         134       0.64      0.47      0.54        64\n",
            "         135       0.76      0.54      0.63        69\n",
            "         136       0.25      0.06      0.09        69\n",
            "         137       0.50      0.01      0.03        69\n",
            "         138       0.54      0.32      0.40        65\n",
            "         139       0.53      0.28      0.37        75\n",
            "         140       0.46      0.33      0.39        69\n",
            "         141       0.67      0.39      0.49        83\n",
            "         142       0.14      0.02      0.03        57\n",
            "         143       0.62      0.43      0.51        67\n",
            "         144       0.57      0.36      0.44        66\n",
            "         145       0.29      0.16      0.21        61\n",
            "         146       0.62      0.46      0.53        56\n",
            "         147       0.71      0.54      0.61        65\n",
            "         148       0.65      0.40      0.49        55\n",
            "         149       0.86      0.59      0.70        71\n",
            "         150       0.30      0.11      0.17        61\n",
            "         151       0.71      0.44      0.55        45\n",
            "         152       0.92      0.73      0.81        75\n",
            "         153       0.71      0.54      0.61        67\n",
            "         154       0.37      0.10      0.15        72\n",
            "         155       0.85      0.61      0.71        66\n",
            "         156       0.00      0.00      0.00        50\n",
            "         157       0.48      0.34      0.40        67\n",
            "         158       0.50      0.05      0.09        58\n",
            "         159       0.48      0.22      0.31        67\n",
            "         160       0.94      0.81      0.87        62\n",
            "         161       0.94      0.59      0.73        54\n",
            "         162       0.68      0.30      0.42        50\n",
            "         163       0.56      0.40      0.46        58\n",
            "         164       0.46      0.20      0.28        56\n",
            "         165       0.59      0.34      0.43        59\n",
            "         166       0.32      0.12      0.18        57\n",
            "         167       0.77      0.45      0.57        67\n",
            "         168       0.49      0.29      0.36        63\n",
            "         169       0.88      0.56      0.69        66\n",
            "         170       0.43      0.15      0.23        59\n",
            "         171       0.69      0.55      0.61        49\n",
            "         172       0.69      0.44      0.54        54\n",
            "         173       0.56      0.31      0.39        49\n",
            "         174       0.38      0.20      0.27        54\n",
            "         175       0.74      0.41      0.53        56\n",
            "         176       0.71      0.56      0.62        43\n",
            "         177       0.71      0.54      0.61        65\n",
            "         178       0.27      0.07      0.11        55\n",
            "         179       0.64      0.57      0.60        37\n",
            "         180       0.87      0.54      0.67        50\n",
            "         181       0.65      0.43      0.51        47\n",
            "         182       0.83      0.80      0.81        54\n",
            "         183       0.75      0.36      0.48        59\n",
            "         184       0.25      0.09      0.13        56\n",
            "         185       0.81      0.44      0.57        48\n",
            "         186       0.33      0.11      0.16        56\n",
            "         187       0.70      0.57      0.63        46\n",
            "         188       0.67      0.37      0.48        54\n",
            "         189       0.56      0.48      0.51        42\n",
            "         190       0.94      0.55      0.69        60\n",
            "         191       0.89      0.56      0.69        57\n",
            "         192       0.33      0.13      0.19        52\n",
            "         193       0.61      0.34      0.44        50\n",
            "         194       0.70      0.30      0.42        46\n",
            "         195       0.20      0.02      0.03        64\n",
            "         196       0.40      0.27      0.32        45\n",
            "         197       0.83      0.36      0.50        56\n",
            "         198       0.68      0.36      0.47        47\n",
            "         199       0.59      0.24      0.34        55\n",
            "         200       0.45      0.17      0.25        58\n",
            "         201       0.96      0.54      0.69        46\n",
            "         202       0.48      0.28      0.35        43\n",
            "         203       0.35      0.17      0.23        46\n",
            "         204       0.53      0.20      0.30        44\n",
            "         205       0.76      0.31      0.44        62\n",
            "         206       0.48      0.27      0.35        48\n",
            "         207       0.59      0.32      0.42        50\n",
            "         208       0.62      0.28      0.38        47\n",
            "         209       0.72      0.57      0.64        49\n",
            "         210       0.84      0.56      0.67        57\n",
            "         211       0.43      0.34      0.38        47\n",
            "         212       0.76      0.50      0.60        52\n",
            "         213       0.59      0.30      0.40        43\n",
            "         214       0.41      0.25      0.31        44\n",
            "         215       0.73      0.51      0.60        43\n",
            "         216       0.94      0.48      0.63        61\n",
            "         217       0.70      0.65      0.67        46\n",
            "         218       0.85      0.67      0.75        52\n",
            "         219       0.27      0.17      0.21        41\n",
            "         220       0.22      0.11      0.15        37\n",
            "         221       0.31      0.11      0.16        37\n",
            "         222       0.26      0.11      0.16        44\n",
            "         223       0.59      0.30      0.40        43\n",
            "         224       0.83      0.62      0.71        48\n",
            "         225       0.54      0.17      0.25        42\n",
            "         226       0.78      0.61      0.68        41\n",
            "         227       0.56      0.30      0.39        50\n",
            "         228       0.85      0.55      0.67        42\n",
            "         229       0.00      0.00      0.00        43\n",
            "         230       0.77      0.68      0.72        50\n",
            "         231       0.54      0.28      0.37        47\n",
            "         232       0.68      0.36      0.47        47\n",
            "         233       0.30      0.17      0.21        36\n",
            "         234       0.33      0.09      0.14        46\n",
            "         235       0.45      0.23      0.30        40\n",
            "         236       0.76      0.49      0.59        45\n",
            "         237       0.17      0.03      0.05        32\n",
            "         238       0.56      0.21      0.30        48\n",
            "         239       0.56      0.17      0.26        52\n",
            "         240       0.91      0.68      0.78        47\n",
            "         241       0.60      0.34      0.43        44\n",
            "         242       0.50      0.05      0.09        41\n",
            "         243       0.81      0.63      0.71        35\n",
            "         244       1.00      0.64      0.78        39\n",
            "         245       0.15      0.07      0.10        29\n",
            "         246       0.92      0.56      0.70        39\n",
            "         247       0.82      0.39      0.53        36\n",
            "         248       0.56      0.21      0.31        47\n",
            "         249       0.78      0.32      0.45        57\n",
            "         250       0.17      0.08      0.11        25\n",
            "         251       0.61      0.27      0.37        41\n",
            "         252       0.71      0.15      0.24        34\n",
            "         253       0.14      0.03      0.05        33\n",
            "         254       0.92      0.59      0.72        39\n",
            "         255       0.38      0.12      0.19        41\n",
            "         256       0.46      0.30      0.36        37\n",
            "         257       0.14      0.03      0.04        38\n",
            "         258       0.64      0.19      0.30        36\n",
            "         259       0.80      0.54      0.65        37\n",
            "         260       0.72      0.50      0.59        36\n",
            "         261       0.75      0.21      0.33        43\n",
            "         262       0.43      0.30      0.35        30\n",
            "         263       0.77      0.47      0.59        36\n",
            "         264       0.84      0.69      0.76        45\n",
            "         265       0.89      0.44      0.59        36\n",
            "         266       0.70      0.41      0.52        34\n",
            "         267       1.00      0.60      0.75        40\n",
            "         268       0.91      0.53      0.67        40\n",
            "         269       0.33      0.18      0.24        22\n",
            "         270       0.75      0.58      0.65        26\n",
            "         271       0.75      0.09      0.15        35\n",
            "         272       0.65      0.42      0.52        40\n",
            "         273       0.71      0.33      0.45        30\n",
            "         274       0.33      0.25      0.29        24\n",
            "         275       0.76      0.42      0.54        31\n",
            "         276       0.84      0.75      0.79        28\n",
            "         277       0.68      0.41      0.51        37\n",
            "         278       0.75      0.41      0.53        37\n",
            "         279       0.17      0.07      0.10        30\n",
            "         280       1.00      0.21      0.34        34\n",
            "         281       0.74      0.60      0.66        42\n",
            "         282       0.62      0.35      0.45        37\n",
            "         283       0.84      0.50      0.63        32\n",
            "         284       0.80      0.12      0.22        32\n",
            "         285       0.80      0.52      0.63        31\n",
            "         286       0.63      0.42      0.51        40\n",
            "         287       0.29      0.06      0.10        34\n",
            "         288       0.43      0.36      0.39        25\n",
            "         289       0.90      0.84      0.87        31\n",
            "         290       0.00      0.00      0.00        36\n",
            "         291       0.81      0.61      0.70        36\n",
            "         292       0.79      0.39      0.53        38\n",
            "         293       0.79      0.45      0.58        33\n",
            "         294       0.73      0.50      0.59        32\n",
            "         295       0.00      0.00      0.00        27\n",
            "         296       0.36      0.17      0.24        23\n",
            "         297       0.30      0.10      0.15        29\n",
            "         298       0.86      0.73      0.79        33\n",
            "         299       0.20      0.09      0.13        33\n",
            "         300       0.67      0.37      0.48        27\n",
            "         301       0.27      0.15      0.20        26\n",
            "         302       0.35      0.22      0.27        27\n",
            "         303       0.20      0.04      0.06        28\n",
            "         304       0.50      0.15      0.23        33\n",
            "         305       0.62      0.13      0.21        39\n",
            "         306       0.85      0.82      0.84        34\n",
            "         307       0.71      0.19      0.30        26\n",
            "         308       0.78      0.54      0.64        26\n",
            "         309       0.76      0.33      0.46        40\n",
            "         310       0.86      0.64      0.73        28\n",
            "         311       0.36      0.14      0.20        29\n",
            "         312       0.60      0.21      0.31        29\n",
            "         313       0.70      0.57      0.63        28\n",
            "         314       0.85      0.55      0.67        31\n",
            "         315       0.28      0.18      0.22        28\n",
            "         316       1.00      0.00      0.00        23\n",
            "         317       0.47      0.24      0.32        29\n",
            "         318       1.00      0.75      0.86        24\n",
            "         319       0.71      0.54      0.61        28\n",
            "         320       0.84      0.55      0.67        29\n",
            "         321       0.45      0.19      0.26        27\n",
            "         322       0.00      0.00      0.00        45\n",
            "         323       0.61      0.48      0.54        23\n",
            "         324       0.81      0.52      0.63        25\n",
            "         325       0.69      0.41      0.51        27\n",
            "         326       0.00      0.00      0.00        33\n",
            "         327       0.60      0.26      0.36        23\n",
            "         328       0.81      0.52      0.63        25\n",
            "         329       0.78      0.55      0.64        33\n",
            "         330       0.50      0.12      0.20        24\n",
            "         331       0.73      0.61      0.67        31\n",
            "         332       0.25      0.07      0.11        28\n",
            "         333       0.50      0.14      0.22        29\n",
            "         334       0.72      0.43      0.54        30\n",
            "         335       0.20      0.03      0.06        29\n",
            "         336       0.80      0.28      0.41        29\n",
            "         337       0.83      0.66      0.73        29\n",
            "         338       0.88      0.33      0.48        21\n",
            "         339       0.95      0.61      0.75        31\n",
            "         340       0.36      0.15      0.22        26\n",
            "         341       0.57      0.14      0.23        28\n",
            "         342       0.36      0.19      0.25        21\n",
            "         343       0.55      0.19      0.28        32\n",
            "         344       0.68      0.54      0.60        24\n",
            "         345       0.23      0.12      0.16        24\n",
            "         346       0.67      0.42      0.51        24\n",
            "         347       0.00      0.00      0.00        20\n",
            "         348       0.11      0.04      0.06        23\n",
            "         349       0.76      0.48      0.59        27\n",
            "         350       0.87      0.87      0.87        15\n",
            "         351       0.44      0.17      0.25        23\n",
            "         352       0.38      0.13      0.19        23\n",
            "         353       0.88      0.27      0.41        26\n",
            "         354       0.88      0.52      0.65        27\n",
            "         355       0.50      0.29      0.37        24\n",
            "         356       0.60      0.25      0.35        24\n",
            "         357       0.80      0.44      0.57        18\n",
            "         358       0.73      0.34      0.47        32\n",
            "         359       0.71      0.60      0.65        25\n",
            "         360       0.60      0.24      0.34        25\n",
            "         361       0.78      0.28      0.41        25\n",
            "         362       0.61      0.44      0.51        25\n",
            "         363       0.60      0.18      0.27        34\n",
            "         364       0.67      0.23      0.34        26\n",
            "         365       0.25      0.05      0.08        20\n",
            "         366       1.00      0.05      0.10        20\n",
            "         367       0.62      0.29      0.40        17\n",
            "         368       0.83      0.36      0.50        28\n",
            "         369       0.62      0.22      0.32        23\n",
            "         370       0.00      0.00      0.00        19\n",
            "         371       0.17      0.04      0.06        27\n",
            "         372       0.80      0.55      0.65        22\n",
            "         373       0.73      0.28      0.40        29\n",
            "         374       0.62      0.23      0.33        22\n",
            "         375       0.67      0.08      0.14        26\n",
            "         376       0.55      0.30      0.39        20\n",
            "         377       0.40      0.13      0.20        15\n",
            "         378       0.50      0.32      0.39        19\n",
            "         379       0.60      0.40      0.48        15\n",
            "         380       0.00      0.00      0.00        11\n",
            "         381       0.89      0.68      0.77        25\n",
            "         382       0.67      0.32      0.43        19\n",
            "         383       0.80      0.38      0.52        21\n",
            "         384       0.85      0.52      0.65        21\n",
            "         385       0.57      0.15      0.24        26\n",
            "         386       0.50      0.38      0.43        21\n",
            "         387       0.70      0.58      0.64        24\n",
            "         388       0.83      0.58      0.68        26\n",
            "         389       0.67      0.26      0.38        23\n",
            "         390       1.00      0.00      0.00        22\n",
            "         391       0.83      0.20      0.32        25\n",
            "         392       0.71      0.28      0.40        18\n",
            "         393       0.75      0.55      0.63        22\n",
            "         394       0.88      0.32      0.47        22\n",
            "         395       0.50      0.44      0.47        16\n",
            "         396       0.39      0.50      0.44        14\n",
            "         397       0.80      0.44      0.57        18\n",
            "         398       0.20      0.10      0.13        21\n",
            "         399       0.67      0.15      0.25        13\n",
            "         400       0.68      0.46      0.55        28\n",
            "         401       0.20      0.05      0.07        22\n",
            "         402       0.67      0.32      0.43        19\n",
            "         403       0.20      0.05      0.08        19\n",
            "         404       1.00      0.05      0.09        22\n",
            "         405       0.36      0.27      0.31        15\n",
            "         406       1.00      0.59      0.74        17\n",
            "         407       0.00      0.00      0.00        17\n",
            "         408       0.75      0.50      0.60        18\n",
            "         409       0.50      0.12      0.19        25\n",
            "         410       0.64      0.47      0.54        15\n",
            "         411       0.00      0.00      0.00        13\n",
            "         412       0.71      0.48      0.57        25\n",
            "         413       0.83      0.68      0.75        22\n",
            "         414       0.88      0.71      0.79        21\n",
            "         415       0.00      0.00      0.00        15\n",
            "         416       0.38      0.16      0.22        19\n",
            "         417       0.33      0.05      0.09        20\n",
            "         418       0.83      0.62      0.71        16\n",
            "         419       0.29      0.11      0.15        19\n",
            "         420       0.67      0.53      0.59        15\n",
            "         421       0.71      0.48      0.57        21\n",
            "         422       0.33      0.27      0.30        15\n",
            "         423       0.56      0.23      0.32        22\n",
            "         424       0.58      0.27      0.37        26\n",
            "         425       0.00      0.00      0.00         7\n",
            "         426       1.00      0.00      0.00        16\n",
            "         427       0.78      0.61      0.68        23\n",
            "         428       0.17      0.07      0.10        14\n",
            "         429       0.73      0.55      0.63        20\n",
            "         430       0.60      0.15      0.24        20\n",
            "         431       0.60      0.29      0.39        21\n",
            "         432       0.86      0.63      0.73        19\n",
            "         433       0.00      0.00      0.00        24\n",
            "         434       0.62      0.45      0.53        11\n",
            "         435       0.30      0.16      0.21        19\n",
            "         436       0.00      0.00      0.00        24\n",
            "         437       0.79      0.52      0.63        21\n",
            "         438       0.33      0.07      0.12        14\n",
            "         439       0.00      0.00      0.00        20\n",
            "         440       0.29      0.11      0.16        18\n",
            "         441       0.67      0.57      0.62        14\n",
            "         442       0.58      0.29      0.39        24\n",
            "         443       0.00      0.00      0.00        25\n",
            "         444       0.44      0.25      0.32        16\n",
            "         445       0.50      0.40      0.44        15\n",
            "         446       0.25      0.12      0.16        17\n",
            "         447       0.75      0.38      0.50        16\n",
            "         448       1.00      0.00      0.00        15\n",
            "         449       0.73      0.40      0.52        20\n",
            "         450       1.00      0.11      0.19        19\n",
            "         451       0.93      0.67      0.78        21\n",
            "         452       0.85      0.69      0.76        16\n",
            "         453       0.00      0.00      0.00        15\n",
            "         454       0.80      0.24      0.36        17\n",
            "         455       0.25      0.21      0.23        14\n",
            "         456       0.00      0.00      0.00        12\n",
            "         457       0.40      0.20      0.27        20\n",
            "         458       0.17      0.10      0.12        10\n",
            "         459       0.00      0.00      0.00        20\n",
            "         460       0.56      0.45      0.50        11\n",
            "         461       0.43      0.16      0.23        19\n",
            "         462       0.00      0.00      0.00        17\n",
            "         463       0.75      0.47      0.58        19\n",
            "         464       0.86      0.60      0.71        10\n",
            "         465       0.42      0.38      0.40        13\n",
            "         466       0.38      0.17      0.23        18\n",
            "         467       0.72      0.65      0.68        20\n",
            "         468       0.80      0.19      0.31        21\n",
            "         469       0.50      0.06      0.11        17\n",
            "         470       0.43      0.21      0.29        14\n",
            "         471       0.69      0.47      0.56        19\n",
            "         472       0.57      0.29      0.38        14\n",
            "         473       0.40      0.11      0.17        18\n",
            "         474       0.00      0.00      0.00        16\n",
            "         475       0.75      0.18      0.29        17\n",
            "         476       0.50      0.04      0.08        23\n",
            "         477       0.25      0.06      0.10        16\n",
            "         478       0.33      0.08      0.12        13\n",
            "         479       1.00      0.06      0.12        16\n",
            "         480       0.50      0.31      0.38        13\n",
            "         481       0.45      0.45      0.45        11\n",
            "         482       0.88      0.47      0.61        15\n",
            "         483       0.25      0.10      0.14        10\n",
            "         484       0.44      0.33      0.38        12\n",
            "         485       0.75      0.33      0.46         9\n",
            "         486       1.00      0.11      0.20        18\n",
            "         487       0.60      0.43      0.50        14\n",
            "         488       0.78      0.37      0.50        19\n",
            "         489       0.00      0.00      0.00        18\n",
            "         490       0.60      0.43      0.50        14\n",
            "         491       0.80      0.36      0.50        11\n",
            "         492       0.50      0.19      0.28        21\n",
            "         493       0.75      0.53      0.62        17\n",
            "         494       0.67      0.40      0.50        10\n",
            "         495       0.55      0.75      0.63         8\n",
            "         496       0.75      0.30      0.43        10\n",
            "         497       0.33      0.05      0.08        21\n",
            "         498       0.50      0.33      0.40        15\n",
            "         499       0.82      0.53      0.64        17\n",
            "\n",
            "   micro avg       0.63      0.39      0.48     45465\n",
            "   macro avg       0.59      0.34      0.41     45465\n",
            "weighted avg       0.60      0.39      0.46     45465\n",
            " samples avg       0.71      0.42      0.45     45465\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dEC200qC2cx",
        "colab_type": "text"
      },
      "source": [
        "## 4.5 Naive Bayes with Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3553u1wMYtCG",
        "colab_type": "code",
        "outputId": "f6ebd8a1-5acf-4617-ce9f-9e01eb79f87a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "nb_clf = OneVsRestClassifier(MultinomialNB(alpha=0.0001), n_jobs=3)\n",
        "nb_clf.fit(x_train_multilabel, y_train)\n",
        "nb_predictions = nb_clf.predict(x_test_multilabel)\n",
        "print(\"accuracy :\",metrics.accuracy_score(y_test,nb_predictions))\n",
        "print(\"macro f1 score :\",metrics.f1_score(y_test, nb_predictions, average = 'macro'))\n",
        "print(\"micro f1 scoore :\",metrics.f1_score(y_test, nb_predictions, average = 'micro'))\n",
        "print(\"hamming loss :\",metrics.hamming_loss(y_test,nb_predictions))\n",
        "print(\"Precision recall report :\\n\",metrics.classification_report(y_test, nb_predictions))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy : 0.04266321913380737\n",
            "macro f1 score : 0.129075858649671\n",
            "micro f1 scoore : 0.2519065530211533\n",
            "hamming loss : 0.005095845331139449\n",
            "Precision recall report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      0.28      0.39      2675\n",
            "           1       0.59      0.34      0.43      2189\n",
            "           2       0.45      0.35      0.39      1296\n",
            "           3       0.73      0.46      0.57      1167\n",
            "           4       0.52      0.30      0.38       813\n",
            "           5       0.46      0.17      0.25       772\n",
            "           6       0.44      0.25      0.32       748\n",
            "           7       0.34      0.13      0.19       671\n",
            "           8       0.82      0.26      0.39       657\n",
            "           9       0.42      0.20      0.27       619\n",
            "          10       0.84      0.16      0.27       582\n",
            "          11       0.35      0.04      0.07       526\n",
            "          12       0.66      0.29      0.40       524\n",
            "          13       0.67      0.33      0.44       499\n",
            "          14       0.55      0.24      0.33       435\n",
            "          15       0.50      0.08      0.14       414\n",
            "          16       0.65      0.32      0.43       403\n",
            "          17       0.85      0.29      0.43       380\n",
            "          18       0.80      0.55      0.65       410\n",
            "          19       0.19      0.05      0.08       362\n",
            "          20       0.73      0.21      0.33       348\n",
            "          21       0.63      0.10      0.17       320\n",
            "          22       0.74      0.11      0.18       303\n",
            "          23       0.67      0.09      0.16       337\n",
            "          24       0.81      0.08      0.14       327\n",
            "          25       0.24      0.04      0.06       305\n",
            "          26       0.67      0.31      0.42       304\n",
            "          27       0.39      0.09      0.14       308\n",
            "          28       0.81      0.38      0.52       230\n",
            "          29       0.68      0.06      0.11       275\n",
            "          30       0.77      0.40      0.53       273\n",
            "          31       0.61      0.29      0.39       261\n",
            "          32       0.44      0.06      0.11       234\n",
            "          33       0.79      0.27      0.40       254\n",
            "          34       0.84      0.19      0.31       255\n",
            "          35       0.60      0.28      0.38       226\n",
            "          36       0.00      0.00      0.00       278\n",
            "          37       0.50      0.01      0.02       230\n",
            "          38       0.88      0.27      0.42       205\n",
            "          39       0.79      0.09      0.17       237\n",
            "          40       0.74      0.08      0.14       222\n",
            "          41       0.00      0.00      0.00       201\n",
            "          42       0.27      0.02      0.03       191\n",
            "          43       0.82      0.14      0.24       223\n",
            "          44       0.21      0.03      0.06       177\n",
            "          45       0.70      0.18      0.28       215\n",
            "          46       0.67      0.01      0.02       161\n",
            "          47       0.55      0.03      0.06       181\n",
            "          48       0.61      0.18      0.28       190\n",
            "          49       0.71      0.03      0.05       196\n",
            "          50       0.33      0.01      0.02       198\n",
            "          51       0.33      0.04      0.07       177\n",
            "          52       0.91      0.30      0.46       210\n",
            "          53       0.43      0.05      0.10       168\n",
            "          54       0.58      0.43      0.49       162\n",
            "          55       0.44      0.06      0.11       176\n",
            "          56       0.58      0.06      0.11       173\n",
            "          57       0.46      0.03      0.06       176\n",
            "          58       0.78      0.17      0.28       163\n",
            "          59       0.64      0.12      0.20       179\n",
            "          60       0.14      0.01      0.02       205\n",
            "          61       0.61      0.09      0.15       161\n",
            "          62       0.11      0.01      0.01       163\n",
            "          63       0.35      0.04      0.07       167\n",
            "          64       0.00      0.00      0.00       191\n",
            "          65       0.43      0.10      0.16       163\n",
            "          66       0.00      0.00      0.00       138\n",
            "          67       0.46      0.08      0.13       167\n",
            "          68       0.93      0.19      0.31       148\n",
            "          69       0.69      0.06      0.11       144\n",
            "          70       0.38      0.21      0.27       135\n",
            "          71       0.65      0.13      0.21       134\n",
            "          72       0.60      0.02      0.04       137\n",
            "          73       0.55      0.08      0.14       136\n",
            "          74       0.67      0.06      0.10       144\n",
            "          75       0.44      0.11      0.18       132\n",
            "          76       0.80      0.10      0.17       126\n",
            "          77       0.55      0.14      0.22       130\n",
            "          78       0.50      0.02      0.04       135\n",
            "          79       0.74      0.33      0.45       107\n",
            "          80       0.65      0.12      0.20       107\n",
            "          81       0.83      0.08      0.14       128\n",
            "          82       0.65      0.11      0.18       102\n",
            "          83       0.33      0.03      0.05       118\n",
            "          84       0.47      0.07      0.12       128\n",
            "          85       0.38      0.04      0.07       129\n",
            "          86       0.41      0.32      0.36       128\n",
            "          87       0.71      0.15      0.25       142\n",
            "          88       0.50      0.08      0.13       116\n",
            "          89       0.00      0.00      0.00       109\n",
            "          90       0.47      0.15      0.22       103\n",
            "          91       0.50      0.03      0.06       119\n",
            "          92       0.00      0.00      0.00        93\n",
            "          93       0.00      0.00      0.00       112\n",
            "          94       0.42      0.08      0.13       106\n",
            "          95       0.75      0.03      0.05       107\n",
            "          96       0.68      0.21      0.32        82\n",
            "          97       0.71      0.11      0.19        92\n",
            "          98       0.71      0.05      0.10        97\n",
            "          99       0.00      0.00      0.00       102\n",
            "         100       0.83      0.04      0.08       113\n",
            "         101       0.69      0.11      0.19        98\n",
            "         102       1.00      0.04      0.08        98\n",
            "         103       0.53      0.08      0.14       102\n",
            "         104       0.00      0.00      0.00       101\n",
            "         105       0.44      0.04      0.07       114\n",
            "         106       0.92      0.32      0.47        72\n",
            "         107       1.00      0.04      0.08       102\n",
            "         108       0.80      0.13      0.23        90\n",
            "         109       0.47      0.08      0.14        88\n",
            "         110       0.00      0.00      0.00        86\n",
            "         111       0.48      0.15      0.23        95\n",
            "         112       0.67      0.06      0.11        70\n",
            "         113       0.50      0.02      0.04        90\n",
            "         114       0.50      0.14      0.22        98\n",
            "         115       0.73      0.10      0.18        79\n",
            "         116       0.00      0.00      0.00        81\n",
            "         117       1.00      0.04      0.08       101\n",
            "         118       0.29      0.03      0.05        68\n",
            "         119       0.28      0.06      0.10        78\n",
            "         120       0.57      0.10      0.16        83\n",
            "         121       0.22      0.03      0.05        75\n",
            "         122       0.95      0.20      0.33        91\n",
            "         123       1.00      0.01      0.02        85\n",
            "         124       0.52      0.21      0.30        76\n",
            "         125       0.88      0.20      0.33        74\n",
            "         126       0.40      0.03      0.05        79\n",
            "         127       0.67      0.03      0.06        64\n",
            "         128       0.14      0.02      0.03        61\n",
            "         129       0.67      0.12      0.21        64\n",
            "         130       0.71      0.22      0.33        78\n",
            "         131       0.72      0.32      0.44        73\n",
            "         132       0.43      0.05      0.08        66\n",
            "         133       0.50      0.03      0.06        68\n",
            "         134       0.45      0.08      0.13        64\n",
            "         135       0.55      0.17      0.26        69\n",
            "         136       0.00      0.00      0.00        69\n",
            "         137       0.00      0.00      0.00        69\n",
            "         138       0.20      0.02      0.03        65\n",
            "         139       0.86      0.08      0.15        75\n",
            "         140       0.32      0.09      0.14        69\n",
            "         141       0.00      0.00      0.00        83\n",
            "         142       0.00      0.00      0.00        57\n",
            "         143       0.54      0.30      0.38        67\n",
            "         144       0.56      0.15      0.24        66\n",
            "         145       0.35      0.11      0.17        61\n",
            "         146       0.50      0.02      0.03        56\n",
            "         147       0.70      0.29      0.41        65\n",
            "         148       0.64      0.16      0.26        55\n",
            "         149       0.50      0.04      0.08        71\n",
            "         150       0.00      0.00      0.00        61\n",
            "         151       0.29      0.04      0.08        45\n",
            "         152       0.97      0.39      0.55        75\n",
            "         153       0.63      0.18      0.28        67\n",
            "         154       0.25      0.01      0.03        72\n",
            "         155       0.78      0.21      0.33        66\n",
            "         156       0.00      0.00      0.00        50\n",
            "         157       0.10      0.01      0.03        67\n",
            "         158       0.00      0.00      0.00        58\n",
            "         159       0.33      0.04      0.08        67\n",
            "         160       0.94      0.24      0.38        62\n",
            "         161       0.62      0.09      0.16        54\n",
            "         162       1.00      0.02      0.04        50\n",
            "         163       0.00      0.00      0.00        58\n",
            "         164       0.60      0.05      0.10        56\n",
            "         165       0.50      0.03      0.06        59\n",
            "         166       0.00      0.00      0.00        57\n",
            "         167       1.00      0.01      0.03        67\n",
            "         168       0.42      0.08      0.13        63\n",
            "         169       0.67      0.18      0.29        66\n",
            "         170       0.50      0.02      0.03        59\n",
            "         171       0.88      0.14      0.25        49\n",
            "         172       0.61      0.26      0.36        54\n",
            "         173       1.00      0.06      0.12        49\n",
            "         174       0.00      0.00      0.00        54\n",
            "         175       0.52      0.25      0.34        56\n",
            "         176       0.40      0.05      0.08        43\n",
            "         177       0.75      0.14      0.23        65\n",
            "         178       0.00      0.00      0.00        55\n",
            "         179       0.67      0.05      0.10        37\n",
            "         180       0.50      0.08      0.14        50\n",
            "         181       0.44      0.17      0.25        47\n",
            "         182       0.55      0.43      0.48        54\n",
            "         183       0.62      0.08      0.15        59\n",
            "         184       0.00      0.00      0.00        56\n",
            "         185       0.73      0.23      0.35        48\n",
            "         186       0.00      0.00      0.00        56\n",
            "         187       0.80      0.35      0.48        46\n",
            "         188       0.56      0.43      0.48        54\n",
            "         189       0.75      0.07      0.13        42\n",
            "         190       1.00      0.03      0.06        60\n",
            "         191       1.00      0.12      0.22        57\n",
            "         192       0.00      0.00      0.00        52\n",
            "         193       0.00      0.00      0.00        50\n",
            "         194       0.00      0.00      0.00        46\n",
            "         195       0.00      0.00      0.00        64\n",
            "         196       0.33      0.02      0.04        45\n",
            "         197       0.43      0.11      0.17        56\n",
            "         198       0.55      0.13      0.21        47\n",
            "         199       0.14      0.02      0.03        55\n",
            "         200       0.00      0.00      0.00        58\n",
            "         201       1.00      0.07      0.12        46\n",
            "         202       1.00      0.07      0.13        43\n",
            "         203       0.38      0.07      0.11        46\n",
            "         204       0.00      0.00      0.00        44\n",
            "         205       0.21      0.05      0.08        62\n",
            "         206       0.24      0.08      0.12        48\n",
            "         207       0.00      0.00      0.00        50\n",
            "         208       0.00      0.00      0.00        47\n",
            "         209       0.68      0.27      0.38        49\n",
            "         210       0.64      0.12      0.21        57\n",
            "         211       0.67      0.09      0.15        47\n",
            "         212       0.58      0.13      0.22        52\n",
            "         213       0.75      0.07      0.13        43\n",
            "         214       0.36      0.09      0.15        44\n",
            "         215       0.64      0.16      0.26        43\n",
            "         216       0.83      0.08      0.15        61\n",
            "         217       0.82      0.20      0.32        46\n",
            "         218       0.33      0.04      0.07        52\n",
            "         219       0.00      0.00      0.00        41\n",
            "         220       0.00      0.00      0.00        37\n",
            "         221       0.00      0.00      0.00        37\n",
            "         222       0.20      0.02      0.04        44\n",
            "         223       0.43      0.07      0.12        43\n",
            "         224       0.00      0.00      0.00        48\n",
            "         225       0.50      0.05      0.09        42\n",
            "         226       0.67      0.05      0.09        41\n",
            "         227       0.40      0.04      0.07        50\n",
            "         228       0.91      0.24      0.38        42\n",
            "         229       0.00      0.00      0.00        43\n",
            "         230       0.67      0.08      0.14        50\n",
            "         231       0.25      0.02      0.04        47\n",
            "         232       0.67      0.09      0.15        47\n",
            "         233       0.29      0.06      0.09        36\n",
            "         234       0.50      0.02      0.04        46\n",
            "         235       0.00      0.00      0.00        40\n",
            "         236       0.88      0.16      0.26        45\n",
            "         237       0.00      0.00      0.00        32\n",
            "         238       0.50      0.08      0.14        48\n",
            "         239       0.00      0.00      0.00        52\n",
            "         240       1.00      0.19      0.32        47\n",
            "         241       0.80      0.09      0.16        44\n",
            "         242       0.00      0.00      0.00        41\n",
            "         243       0.50      0.03      0.05        35\n",
            "         244       0.75      0.08      0.14        39\n",
            "         245       0.00      0.00      0.00        29\n",
            "         246       1.00      0.05      0.10        39\n",
            "         247       0.33      0.03      0.05        36\n",
            "         248       0.75      0.06      0.12        47\n",
            "         249       1.00      0.11      0.19        57\n",
            "         250       0.00      0.00      0.00        25\n",
            "         251       0.00      0.00      0.00        41\n",
            "         252       0.09      0.03      0.04        34\n",
            "         253       0.00      0.00      0.00        33\n",
            "         254       0.83      0.13      0.22        39\n",
            "         255       0.00      0.00      0.00        41\n",
            "         256       0.00      0.00      0.00        37\n",
            "         257       0.00      0.00      0.00        38\n",
            "         258       1.00      0.03      0.05        36\n",
            "         259       0.00      0.00      0.00        37\n",
            "         260       1.00      0.08      0.15        36\n",
            "         261       1.00      0.02      0.05        43\n",
            "         262       0.00      0.00      0.00        30\n",
            "         263       0.75      0.17      0.27        36\n",
            "         264       0.75      0.13      0.23        45\n",
            "         265       0.71      0.14      0.23        36\n",
            "         266       1.00      0.06      0.11        34\n",
            "         267       0.54      0.17      0.26        40\n",
            "         268       0.67      0.10      0.17        40\n",
            "         269       0.00      0.00      0.00        22\n",
            "         270       0.50      0.12      0.19        26\n",
            "         271       0.00      0.00      0.00        35\n",
            "         272       0.00      0.00      0.00        40\n",
            "         273       0.00      0.00      0.00        30\n",
            "         274       0.00      0.00      0.00        24\n",
            "         275       0.60      0.19      0.29        31\n",
            "         276       0.86      0.21      0.34        28\n",
            "         277       0.20      0.03      0.05        37\n",
            "         278       0.00      0.00      0.00        37\n",
            "         279       0.00      0.00      0.00        30\n",
            "         280       0.67      0.06      0.11        34\n",
            "         281       0.66      0.55      0.60        42\n",
            "         282       0.00      0.00      0.00        37\n",
            "         283       0.73      0.25      0.37        32\n",
            "         284       0.00      0.00      0.00        32\n",
            "         285       0.80      0.13      0.22        31\n",
            "         286       0.00      0.00      0.00        40\n",
            "         287       0.33      0.03      0.05        34\n",