DEMO_Counter_speech.ipynb

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      "name": "DEMO_Counter_speech.ipynb",
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    {
      "metadata": {
        "id": "vB9fPSxHsZfq",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "# Introduction \n",
        "\n",
        "\n",
        "![alt text](https://techcrunch.com/wp-content/uploads/2017/08/anti-hate.jpg)\n",
        "\n",
        "This notebook provides a demo to use the methods used in the paper with new data. If new to collaboratory ,please check the following [link](https://medium.com/lean-in-women-in-tech-india/google-colab-the-beginners-guide-5ad3b417dfa) to know how to run the code."
      ]
    },
    {
      "metadata": {
        "id": "VWiCkFli4R5t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Import the required libraries:"
      ]
    },
    {
      "metadata": {
        "id": "H4S_jsN3whqb",
        "colab_type": "code",
        "outputId": "d2bee5d3-8ea7-4af2-85b5-fa8546d1b276",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "#import \n",
        "\n",
        "from gensim.test.utils import datapath, get_tmpfile\n",
        "from gensim.models import KeyedVectors\n",
        "from gensim.scripts.glove2word2vec import glove2word2vec\n",
        "import os\n",
        "import joblib\n",
        "import json\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "###ipywigets\n",
        "from __future__ import print_function\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "import ipywidgets as widgets\n",
        "\n",
        "\n",
        "from sklearn import *\n",
        "from sklearn.model_selection import *\n",
        "from sklearn.metrics import *\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "  "
      ],
      "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "sLM_PY2WBtcC",
        "colab_type": "code",
        "outputId": "fb83cdf7-ccba-4e9f-9f17-f56f6812cae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "cell_type": "code",
      "source": [
        "#copy the git clone address here \n",
        " \n",
        "!git clone https://github.com/binny-mathew/Countering_Hate_Speech.git   \n",
        "#Best binary classifier was XGBclassifier \n",
        "#Best multilabel classifier was XGBclassifier\n",
        "\n",
        "best_binary_classifier = joblib.load('Countering_Hate_Speech/Best_model/XGB_classifier_task_1.joblib.pkl')\n",
        "best_multiclass_classifier = joblib.load('Countering_Hate_Speech/Best_model/XGB_classifier_task_3.joblib.pkl')\n",
        "best_black_classifier = joblib.load('Countering_Hate_Speech/Best_model/black_XGB_classifier_task_2.joblib.pkl')\n",
        "best_jew_classifier = joblib.load('Countering_Hate_Speech/Best_model/jew_XGB_classifier_task_2.joblib.pkl')\n",
        "best_lgbt_classifier = joblib.load('Countering_Hate_Speech/Best_model/lgbt_XGB_classifier_task_2.joblib.pkl')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:253: UserWarning: Trying to unpickle estimator LogisticRegression from version 0.20.2 when using version 0.20.3. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:253: UserWarning: Trying to unpickle estimator LabelBinarizer from version 0.20.2 when using version 0.20.3. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:253: UserWarning: Trying to unpickle estimator _ConstantPredictor from version 0.20.2 when using version 0.20.3. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:253: UserWarning: Trying to unpickle estimator OneVsRestClassifier from version 0.20.2 when using version 0.20.3. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "W7DsHi5brvJB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###Word Embeddings Loaded Here"
      ]
    },
    {
      "metadata": {
        "id": "mpjSQfe8r_c2",
        "colab_type": "code",
        "outputId": "8d3de17c-9088-4d2d-9687-9057c382e901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "####downloading the word embeddings\n",
        "!wget http://nlp.stanford.edu/data/glove.840B.300d.zip\n",
        "!unzip glove.840B.300d.zip\n",
        "####extracting the glove model file   \n",
        "#import zipfile\n",
        "#archive = zipfile.ZipFile('glove.840B.300d.zip', 'r')\n",
        "GLOVE_MODEL_FILE ='glove.840B.300d.txt'\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "## change the embedding dimension according to the model\n",
        "EMBEDDING_DIM = 300\n",
        "###change the method type\n",
        "\n",
        "\n",
        "### method two\n",
        "def loadGloveModel2(glove_file):\n",
        "    tmp_file = get_tmpfile(\"test_crawl_200.txt\")\n",
        "\n",
        "    # call glove2word2vec script\n",
        "    # default way (through CLI): python -m gensim.scripts.glove2word2vec --input <glove_file> --output <w2v_file>\n",
        "    glove2word2vec(glove_file, tmp_file)\n",
        "    model=KeyedVectors.load_word2vec_format(tmp_file)\n",
        "    return model\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "--2019-03-28 11:17:48--  http://nlp.stanford.edu/data/glove.840B.300d.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.840B.300d.zip [following]\n",
            "--2019-03-28 11:17:48--  https://nlp.stanford.edu/data/glove.840B.300d.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2176768927 (2.0G) [application/zip]\n",
            "Saving to: ‘glove.840B.300d.zip’\n",
            "\n",
            "glove.840B.300d.zip 100%[===================>]   2.03G  6.61MB/s    in 5m 19s  \n",
            "\n",
            "2019-03-28 11:23:07 (6.52 MB/s) - ‘glove.840B.300d.zip’ saved [2176768927/2176768927]\n",
            "\n",
            "Archive:  glove.840B.300d.zip\n",
            "  inflating: glove.840B.300d.txt     \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-RHsmNr6_YCV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "word2vec_model = loadGloveModel2(GLOVE_MODEL_FILE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bI4SBwQcJaV7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Dataset is loaded here"
      ]
    },
    {
      "metadata": {
        "id": "lq6cXvGOCSmI",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Select the type of file used \n",
        "\n",
        "type_of_file = 'X.json' #@param ['X.json','X.csv']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PKMLECNi2mbL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### File type information\n",
        "\n",
        "If the file type is **.json** then each element should contain the following fields:-\n",
        "1.   Community\n",
        "2.   CounterSpeech\n",
        "3.   Category\n",
        "4.   commentText\n",
        "5.   id\n",
        "\n",
        "If the file type is **.csv** then it must have the following columns:-\n",
        "1.   Community\n",
        "2.   CounterSpeech\n",
        "3.   Category\n",
        "4.   commentText\n",
        "5.   id\n",
        "\n",
        "\n",
        "Note:- If you don't have the Category or  Community add an dummy element or column "
      ]
    },
    {
      "metadata": {
        "id": "5pMYvgeY2lDl",
        "colab_type": "code",
        "outputId": "3109c695-8c1b-4786-b3d0-3f77a53c7da7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "####CHANGE THE PATH OF THE FILE\n",
        "path_of_file='Countering_Hate_Speech/Data/Counterspeech_Dataset.json'\n",
        "\n",
        "def convert_class_label(input_text):\n",
        "    if input_text:\n",
        "        return 'counter'\n",
        "    else:\n",
        "        return 'noncounter'\n",
        "\n",
        "\n",
        "\n",
        "if(type_of_file=='X.json'):\n",
        "      with open(path_of_file) as fp:\n",
        "            train_data = json.load(fp)\n",
        "      pd_train = pd.DataFrame(columns=['id','class','community','category','text'])\n",
        "\n",
        "      for count, each in enumerate(train_data):\n",
        "          try:\n",
        "              pd_train.loc[count]  = [each['id'], convert_class_label(each['CounterSpeech']), each['Community'],each['Category'],each['commentText']]\n",
        "          except:\n",
        "              pass\n",
        "      print('Training Data Loading Completed...')\n",
        "elif(type_of_file=='X.csv'):\n",
        "      pd_train=pd.read_csv(path_of_the_file)\n",
        "      \n",
        "pd_train.head()            "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Loading Completed...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>class</th>\n",
              "      <th>community</th>\n",
              "      <th>category</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ugw-_mfnTo9SUniGGTN4AaABAg</td>\n",
              "      <td>noncounter</td>\n",
              "      <td>jews</td>\n",
              "      <td>Default</td>\n",
              "      <td>she is one of the last brave white women with ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>UgiMOgO3u4akkngCoAEC</td>\n",
              "      <td>noncounter</td>\n",
              "      <td>lgbt</td>\n",
              "      <td>Default</td>\n",
              "      <td>AMEN PASTOR!!!!!!!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>UgzemCMbg-ri1ri7sCt4AaABAg</td>\n",
              "      <td>counter</td>\n",
              "      <td>jews</td>\n",
              "      <td>2,8</td>\n",
              "      <td>What kind of God is it that Hates people &amp; Cur...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>UghFhzrURF1svHgCoAEC</td>\n",
              "      <td>counter</td>\n",
              "      <td>jews</td>\n",
              "      <td>8</td>\n",
              "      <td>Question - Which comment is racist and why?\\n ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ugzr50sQdH0Ictuex214AaABAg</td>\n",
              "      <td>counter</td>\n",
              "      <td>jews</td>\n",
              "      <td>8</td>\n",
              "      <td>That Israel fail that is nothing new. That Isr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           id       class community category  \\\n",
              "0  Ugw-_mfnTo9SUniGGTN4AaABAg  noncounter      jews  Default   \n",
              "1        UgiMOgO3u4akkngCoAEC  noncounter      lgbt  Default   \n",
              "2  UgzemCMbg-ri1ri7sCt4AaABAg     counter      jews      2,8   \n",
              "3        UghFhzrURF1svHgCoAEC     counter      jews        8   \n",
              "4  Ugzr50sQdH0Ictuex214AaABAg     counter      jews        8   \n",
              "\n",
              "                                                text  \n",
              "0  she is one of the last brave white women with ...  \n",
              "1                                AMEN PASTOR!!!!!!!!  \n",
              "2  What kind of God is it that Hates people & Cur...  \n",
              "3  Question - Which comment is racist and why?\\n ...  \n",
              "4  That Israel fail that is nothing new. That Isr...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "t0zmHmD7CAnQ",
        "colab_type": "code",
        "outputId": "4f23b813-b0b5-4ba0-8dea-4232299dd763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "cell_type": "code",
      "source": [
        "#@title How your dataframe should look like after extraction {display-mode: \"form\"}\n",
        "\n",
        "# This code will be hidden when the notebook is loaded.\n",
        "\n",
        "path_of_data_file='Countering_Hate_Speech/Data/Counterspeech_Dataset.json'\n",
        "\n",
        "def convert_class_label(input_text):\n",
        "    if input_text:\n",
        "        return 'counter'\n",
        "    else:\n",
        "        return 'noncounter'\n",
        "with open(path_of_data_file) as fp:\n",
        "      train_data = json.load(fp)\n",
        "pd_train_sample = pd.DataFrame(columns=['id','class','community','category','text'])\n",
        "\n",
        "for count, each in enumerate(train_data):\n",
        "    try:\n",
        "        pd_train_sample.loc[count]  = [each['id'], convert_class_label(each['CounterSpeech']), each['Community'],each['Category'],each['commentText']]\n",
        "    except:\n",
        "        pass\n",
        "print('Training Data Loading Completed...')\n",
        "pd_train_sample.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Loading Completed...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>class</th>\n",
              "      <th>community</th>\n",
              "      <th>category</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ugw-_mfnTo9SUniGGTN4AaABAg</td>\n",
              "      <td>noncounter</td>\n",
              "      <td>jews</td>\n",
              "      <td>Default</td>\n",
              "      <td>she is one of the last brave white women with ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>UgiMOgO3u4akkngCoAEC</td>\n",
              "      <td>noncounter</td>\n",
              "      <td>lgbt</td>\n",
              "      <td>Default</td>\n",
              "      <td>AMEN PASTOR!!!!!!!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>UgzemCMbg-ri1ri7sCt4AaABAg</td>\n",
              "      <td>counter</td>\n",
              "      <td>jews</td>\n",
              "      <td>2,8</td>\n",
              "      <td>What kind of God is it that Hates people &amp; Cur...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>UghFhzrURF1svHgCoAEC</td>\n",
              "      <td>counter</td>\n",
              "      <td>jews</td>\n",
              "      <td>8</td>\n",
              "      <td>Question - Which comment is racist and why?\\n ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ugzr50sQdH0Ictuex214AaABAg</td>\n",
              "      <td>counter</td>\n",
              "      <td>jews</td>\n",
              "      <td>8</td>\n",
              "      <td>That Israel fail that is nothing new. That Isr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           id       class community category  \\\n",
              "0  Ugw-_mfnTo9SUniGGTN4AaABAg  noncounter      jews  Default   \n",
              "1        UgiMOgO3u4akkngCoAEC  noncounter      lgbt  Default   \n",
              "2  UgzemCMbg-ri1ri7sCt4AaABAg     counter      jews      2,8   \n",
              "3        UghFhzrURF1svHgCoAEC     counter      jews        8   \n",
              "4  Ugzr50sQdH0Ictuex214AaABAg     counter      jews        8   \n",
              "\n",
              "                                                text  \n",
              "0  she is one of the last brave white women with ...  \n",
              "1                                AMEN PASTOR!!!!!!!!  \n",
              "2  What kind of God is it that Hates people & Cur...  \n",
              "3  Question - Which comment is racist and why?\\n ...  \n",
              "4  That Israel fail that is nothing new. That Isr...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "Tq453rXyyKRy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "pd_train['text'].replace('', np.nan, inplace=True)\n",
        "pd_train.dropna(subset=['text'], inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CVuL2Krg0FJ0",
        "colab_type": "code",
        "outputId": "7049fe6c-abe1-4d72-c707-03579df618aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "\n",
        "####features module has the necessary function for feature generation \n",
        "from Countering_Hate_Speech.utils import features\n",
        "from Countering_Hate_Speech.utils import multi_features\n",
        "###tokenize module has the tokenization funciton\n",
        "from Countering_Hate_Speech.utils.tokenize import *\n",
        "###helper prints confusion  matrix and stores results\n",
        "from Countering_Hate_Speech.utils.helper import *\n",
        "###common preprocessing imports\n",
        "from Countering_Hate_Speech.utils.commen_preprocess import *\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "....start....cleaning\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0328 11:47:01.342070 140315258214272 __init__.py:56] Some hub symbols are not available because TensorFlow version is less than 1.14\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py:3632: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0328 11:47:18.272865 140315258214272 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py:3632: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "XHGhSG8dM-4O",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Next few sections cover three different classifiers  namely - \n",
        "\n",
        "*   Binary classification \n",
        "*   Multlabel classification\n",
        "*   Cross community \n",
        "\n",
        "You can run the cells corresponding to the result you want to analyse.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "ltvQYvwtNnlN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Binary Classification**"
      ]
    },
    {
      "metadata": {
        "id": "D8Lqz7bJzHNI",
        "colab_type": "code",
        "outputId": "20f0b69d-50a5-4e21-c917-d5d28542b7cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        }
      },
      "cell_type": "code",
      "source": [
        "X,y= features.combine_tf_rem_google_rem_embed(pd_train,word2vec_model)\n",
        "\n",
        "label_map = {\n",
        "        'counter': 0,\n",
        "        'noncounter': 1\n",
        "    }\n",
        "temp=[]\n",
        "for data in y:\n",
        "        temp.append(label_map[data])\n",
        "\n",
        "y=np.array(temp)\n",
        "\n",
        "\n",
        "y_pred=best_binary_classifier.predict(X)\n",
        "report = classification_report(y, y_pred)\n",
        "cm=confusion_matrix(y, y_pred)\n",
        "plt=plot_confusion_matrix(cm,normalize= True,target_names = ['counter','non_counter'],title = \"Confusion Matrix\")\n",
        "plt.savefig('Confusion_matrix.png')\n",
        "df_result=pandas_classification_report(y,y_pred)\n",
        "df_result.to_csv('Classification_Report.csv',  sep=',')\n",
        "print(\"You can download the files from the file directory now \")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "I0328 11:47:43.327548 140315258214272 saver.py:1483] Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ending\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:301: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['fifti'] not in stop_words.\n",
            "  'stop_words.' % sorted(inconsistent))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "You can download the files from the file directory now \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U2L59OxcvXizoblilpORUnByz/h3m7ORAQlJKtn3LehXH16cMa7ccvG1byzZGkZ7+b/U5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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dZF1Qe-7L7Cc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Multilabel Classification**"
      ]
    },
    {
      "metadata": {
        "id": "JXw_ex7CMR5D",
        "colab_type": "code",
        "outputId": "3baec622-656c-47eb-95f7-8d83a50512b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "\n",
        "pd_train_multilabel =pd_train.copy()\n",
        "pd_train_multilabel =pd_train_multilabel[pd_train_multilabel['category']!='Default']\n",
        "list1=[[],[],[],[],[],[],[],[],[],[]]\n",
        "for ele in pd_train_multilabel['category']:\n",
        "    temp=[]\n",
        "    if type(ele) is int:\n",
        "        ele =str(ele)\n",
        "    for i in range(0,len(ele),2):\n",
        "        temp.append(ord(ele[i])-ord('0'))\n",
        "    #print(temp)\n",
        "    if(len(temp)==0):\n",
        "        print(temp)\n",
        "    for i in range(0,10):\n",
        "        if i+1 in temp:\n",
        "            list1[i].append(1)\n",
        "        else:\n",
        "            list1[i].append(0)\n",
        "y_train=np.array([np.array(xi) for xi in list1])\n",
        "\n",
        "### final dataframe for the task created \n",
        "pd_train_multilabel = pd.DataFrame({'text':list(pd_train_multilabel['text']),'cat0':list1[0],'cat1':list1[1],'cat2':list1[2],'cat3':list1[3],'cat4':list1[4],'cat5':list1[5],'cat6':list1[6],'cat7':list1[7],'cat8':list1[8],'cat9':list1[9]})\n",
        "### drop the entries having blank entries\n",
        "pd_train_multilabel['text'].replace('', np.nan, inplace=True)\n",
        "pd_train_multilabel.dropna(subset=['text'], inplace=True)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "X,y= multi_features.combine_tf_rem_google_rem_embed(pd_train_multilabel,word2vec_model)\n",
        "path='multilabel_res'\n",
        "os.makedirs(path, exist_ok=True)\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "y_pred = best_multiclass_classifier.predict(X)\n",
        "if(scipy.sparse.issparse(y_pred)):\n",
        "   ham,acc,pre,rec,f1=calculate_score(y,y_pred.toarray())\n",
        "   accuracy_test=accuracy_score(y,y_pred.toarray())\n",
        "\n",
        "else:\n",
        "   ham,acc,pre,rec,f1=calculate_score(y,y_pred)\n",
        "   accuracy_test=my_accuracy_score(y,y_pred)\n",
        "\n",
        "  \n",
        "for i in range(10):\n",
        "    df_result=pandas_classification_report(y[:,i],y_pred[:,i])\n",
        "    df_result.to_csv(path+'/report'+str(i)+'.csv')\n",
        "\n",
        "\n",
        "f = open(path+'/final_report.txt', \"w\")\n",
        "f.write(\"best_model\")  \n",
        "f.write(\"The hard metric score is :- \" + str(accuracy_test))\n",
        "f.write(\"The accuracy is :- \" + str(acc))\n",
        "f.write(\"The precision is :- \" + str(pre))\n",
        "f.write(\"The recall is :- \" + str(rec))\n",
        "f.write(\"The f1_score is :- \" + str(f1))\n",
        "f.write(\"The hamming loss is :-\" + str(ham))\n",
        "f.close()\n",
        "  \n",
        "    \n",
        "!zip -r mulitlabel_results.zip multilabel_res\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "I0328 12:13:22.785738 140315258214272 saver.py:1483] Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ending\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:301: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['fifti'] not in stop_words.\n",
            "  'stop_words.' % sorted(inconsistent))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "  adding: multilabel_res/ (stored 0%)\n",
            "  adding: multilabel_res/report9.csv (deflated 33%)\n",
            "  adding: multilabel_res/report3.csv (deflated 40%)\n",
            "  adding: multilabel_res/report7.csv (deflated 39%)\n",
            "  adding: multilabel_res/report6.csv (deflated 36%)\n",
            "  adding: multilabel_res/report2.csv (deflated 38%)\n",
            "  adding: multilabel_res/report1.csv (deflated 39%)\n",
            "  adding: multilabel_res/report0.csv (deflated 38%)\n",
            "  adding: multilabel_res/final_report.txt (deflated 35%)\n",
            "  adding: multilabel_res/report8.csv (deflated 40%)\n",
            "  adding: multilabel_res/report5.csv (deflated 33%)\n",
            "  adding: multilabel_res/report4.csv (deflated 39%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "18G3RymGPNBn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **Cross CommunityClassification**"
      ]
    },
    {
      "metadata": {
        "id": "J2QBBDqrYywy",
        "colab_type": "code",
        "outputId": "55c742ed-7ada-44fc-d022-081736291940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "cell_type": "code",
      "source": [
        "pd_cross=pd_train.copy()\n",
        "part_j=pd_cross.loc[pd_train['community']=='jews']\n",
        "part_b=pd_cross.loc[pd_train['community']=='black']\n",
        "part_l=pd_cross.loc[pd_train['community']=='lgbt']\n",
        "X_black,y_black= features.combine_tf_rem_google_rem_embed(part_b,word2vec_model)\n",
        "X_jew,y_jew= features.combine_tf_rem_google_rem_embed(part_j,word2vec_model)\n",
        "X_lgbt,y_lgbt= features.combine_tf_rem_google_rem_embed(part_l,word2vec_model)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "I0328 13:36:00.798877 140315258214272 saver.py:1483] Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ending\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:301: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['fifti'] not in stop_words.\n",
            "  'stop_words.' % sorted(inconsistent))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "I0328 13:36:40.198019 140315258214272 saver.py:1483] Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ending\n",
            "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "I0328 13:37:38.361577 140315258214272 saver.py:1483] Saver not created because there are no variables in the graph to restore\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ending\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3f6okcKciG04",
        "colab_type": "code",
        "outputId": "d7657563-be68-46da-e72f-b954b9481f71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "cell_type": "code",
      "source": [
        "label_map = {\n",
        "        'counter': 0,\n",
        "        'noncounter': 1\n",
        "    }\n",
        "temp=[]\n",
        "for data in y_black:\n",
        "        temp.append(label_map[data])\n",
        "\n",
        "y_black=np.array(temp)\n",
        "\n",
        "\n",
        "y_pred_black=best_black_classifier.predict(X_black)\n",
        "report = classification_report(y_black, y_pred_black)\n",
        "cm=confusion_matrix(y_black, y_pred_black)\n",
        "plt=plot_confusion_matrix(cm,normalize= True,target_names = ['counter','non_counter'],title = \"Confusion Matrix\")\n",
        "plt.savefig('black_Confusion_matrix.png')\n",
        "df_result=pandas_classification_report(y_black,y_pred_black)\n",
        "df_result.to_csv('black_Classification_Report.csv',  sep=',')\n",
        "print(\"You can download the files from the file directory now \")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You can download the files from the file directory now \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m5KWYmBji4+OJiIjg6NGjhIWFERERUWibDz74AFdX19tq82c3Dda33377po2+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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dcpLid7Lgu0O",
        "colab_type": "code",
        "outputId": "c3e52c5a-a4b6-45ff-c840-8c32a2f4e101",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "cell_type": "code",
      "source": [
        "label_map = {\n",
        "        'counter': 0,\n",
        "        'noncounter': 1\n",
        "    }\n",
        "temp=[]\n",
        "for data in y_jew:\n",
        "        temp.append(label_map[data])\n",
        "\n",
        "y_jew=np.array(temp)\n",
        "\n",
        "\n",
        "y_pred_jew=best_jew_classifier.predict(X_jew)\n",
        "report = classification_report(y_jew, y_pred_jew)\n",
        "cm=confusion_matrix(y_jew, y_pred_jew)\n",
        "plt=plot_confusion_matrix(cm,normalize= True,target_names = ['counter','non_counter'],title = \"Confusion Matrix\")\n",
        "plt.savefig('jew_Confusion_matrix.png')\n",
        "df_result=pandas_classification_report(y_jew,y_pred_jew)\n",
        "df_result.to_csv('jew_Classification_Report.csv',  sep=',')\n",
        "print(\"You can download the files from the file directory now \")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xd8NgXbp0KT179mTYML1aSURECodL32NdvHhxYbZDREQEUz7/M4I8u4JFREQK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\nicQqCIIgCINIJFZBEARBGET/AX4Gn84meZPVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mmb6-16Hg4Rq",
        "colab_type": "code",
        "outputId": "fd59f48b-6fac-42d7-80c1-db1a598224c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "cell_type": "code",
      "source": [
        "label_map = {\n",
        "        'counter': 0,\n",
        "        'noncounter': 1\n",
        "    }\n",
        "temp=[]\n",
        "for data in y_lgbt:\n",
        "        temp.append(label_map[data])\n",
        "\n",
        "y_lgbt=np.array(temp)\n",
        "\n",
        "\n",
        "y_pred_lgbt=best_lgbt_classifier.predict(X_lgbt)\n",
        "report = classification_report(y_lgbt, y_pred_lgbt)\n",
        "cm=confusion_matrix(y_lgbt, y_pred_lgbt)\n",
        "plt=plot_confusion_matrix(cm,normalize= True,target_names = ['counter','non_counter'],title = \"Confusion Matrix\")\n",
        "plt.savefig('lgbt_Confusion_matrix.png')\n",
        "df_result=pandas_classification_report(y_lgbt,y_pred_lgbt)\n",
        "df_result.to_csv('lgbt_Classification_Report.csv',  sep=',')\n",
        "print(\"You can download the files from the file directory now \")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pWdYN/vwnIs9eA2D57ijO/5nAo1W92RJxnk/CjgCwanQb9hy7TGxC5vDu9cQUlu06TdzN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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}