Toxic_Comment_Classification.ipynb

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  "metadata": {
    "colab": {
      "name": "Toxic Comment Classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pranaya-mathur/Deep-Leaning/blob/master/Toxic_Comment_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "04_mFT8e6GdQ",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "d9f3c16d-9760-4129-9322-9a9ff8352d56"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-16a4882f-6bc0-4745-9ad9-b1adea00f809\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-16a4882f-6bc0-4745-9ad9-b1adea00f809\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"chinki86\",\"key\":\"2b37558d5b359235a655730a30d3156b\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "ZXrYDCth60DF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "7c65808f-6185-493b-e3cf-002f93985082"
      },
      "cell_type": "code",
      "source": [
        "!ls -lha kaggle.json\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "!pip install kaggle\n",
        "\n",
        "!kaggle competitions download -c jigsaw-toxic-comment-classification-challenge\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 64 Dec 26 14:29 kaggle.json\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.1.1)\n",
            "Requirement already satisfied: urllib3<1.23.0,>=1.15 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2018.11.29)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.2.6)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: Unidecode>=0.04.16 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.0.23)\n",
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading sample_submission.csv.zip to /content\n",
            "  0% 0.00/1.39M [00:00<?, ?B/s]\n",
            "100% 1.39M/1.39M [00:00<00:00, 45.0MB/s]\n",
            "Downloading test.csv.zip to /content\n",
            " 38% 9.00M/23.4M [00:00<00:00, 81.6MB/s]\n",
            "100% 23.4M/23.4M [00:00<00:00, 115MB/s] \n",
            "Downloading train.csv.zip to /content\n",
            " 87% 23.0M/26.3M [00:00<00:00, 66.0MB/s]\n",
            "100% 26.3M/26.3M [00:00<00:00, 104MB/s] \n",
            "Downloading test_labels.csv.zip to /content\n",
            "  0% 0.00/1.46M [00:00<?, ?B/s]\n",
            "100% 1.46M/1.46M [00:00<00:00, 97.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eDjWwIht8V5e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile(\"glove.6B.zip\", 'r')\n",
        "zip_ref.extractall(\"glove\")\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "E5S6n0b783lk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KafSCWOj9Nhe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.layers import Dense, Input, GlobalAveragePooling1D, Conv1D, MaxPooling1D, Embedding\n",
        "from keras.models import Model\n",
        "from sklearn.metrics import roc_auc_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4hQwyq3TJEcJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "37c2df8d-f3e6-448f-de94-4d06e4419bbb"
      },
      "cell_type": "code",
      "source": [
        "! wget https://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-12-26 15:33:22--  https://nlp.stanford.edu/data/glove.6B.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|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  87.7MB/s    in 9.8s    \n",
            "\n",
            "2018-12-26 15:33:32 (84.2 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sYlZsmvNJhP-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "MAX_SEQUENCE_LENGTH = 100\n",
        "MAX_VOCAB_SIZE = 20000\n",
        "EMBEDDING_DIM = 100\n",
        "VALIDATION_SPLIT = 0.2\n",
        "BATCH_SIZE = 128\n",
        "EPOCHS = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "02ao_grLPvvh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d24d73ea-a24c-4a12-81a0-b100c25aac90"
      },
      "cell_type": "code",
      "source": [
        "word2vec = {}\n",
        "with open(\"glove/glove.6B.100d.txt\") as f:\n",
        "  \n",
        "  for line in f:\n",
        "    \n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    \n",
        "    vec = np.asarray(values[1:],dtype=\"float32\")\n",
        "    word2vec[word] = vec\n",
        "    \n",
        "print(\"Found %s word vectors.\" % len(word2vec))\n",
        "  "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5u63MFMaXOWi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "33cec672-55fe-4dd6-ec88-09d5da20b460"
      },
      "cell_type": "code",
      "source": [
        "word2vec[\"the\"].shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "0AAQ18jsZklg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1914d530-84d3-401e-8039-d0906af63c08"
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"Train/train.csv\")\n",
        "train.head()"
      ],
      "execution_count": 19,
      "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>comment_text</th>\n",
              "      <th>toxic</th>\n",
              "      <th>severe_toxic</th>\n",
              "      <th>obscene</th>\n",
              "      <th>threat</th>\n",
              "      <th>insult</th>\n",
              "      <th>identity_hate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0000997932d777bf</td>\n",
              "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000103f0d9cfb60f</td>\n",
              "      <td>D'aww! He matches this background colour I'm s...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000113f07ec002fd</td>\n",
              "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0001b41b1c6bb37e</td>\n",
              "      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0001d958c54c6e35</td>\n",
              "      <td>You, sir, are my hero. Any chance you remember...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id                                       comment_text  toxic  \\\n",
              "0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0   \n",
              "1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0   \n",
              "2  000113f07ec002fd  Hey man, I'm really not trying to edit war. It...      0   \n",
              "3  0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...      0   \n",
              "4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0   \n",
              "\n",
              "   severe_toxic  obscene  threat  insult  identity_hate  \n",
              "0             0        0       0       0              0  \n",
              "1             0        0       0       0              0  \n",
              "2             0        0       0       0              0  \n",
              "3             0        0       0       0              0  \n",
              "4             0        0       0       0              0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "yLlsjBDvcAMO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "162e01ab-0190-4998-c5fa-7950c9d6391a"
      },
      "cell_type": "code",
      "source": [
        "sentences = train[\"comment_text\"].fillna(\"DUMMY_VALUES\").values\n",
        "sentences"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([\"Explanation\\nWhy the edits made under my username Hardcore Metallica Fan were reverted? They weren't vandalisms, just closure on some GAs after I voted at New York Dolls FAC. And please don't remove the template from the talk page since I'm retired now.89.205.38.27\",\n",
              "       \"D'aww! He matches this background colour I'm seemingly stuck with. Thanks.  (talk) 21:51, January 11, 2016 (UTC)\",\n",
              "       \"Hey man, I'm really not trying to edit war. It's just that this guy is constantly removing relevant information and talking to me through edits instead of my talk page. He seems to care more about the formatting than the actual info.\",\n",
              "       ...,\n",
              "       'Spitzer \\n\\nUmm, theres no actual article for prostitution ring.  - Crunch Captain.',\n",
              "       'And it looks like it was actually you who put on the speedy to have the first version deleted now that I look at it.',\n",
              "       '\"\\nAnd ... I really don\\'t think you understand.  I came here and my idea was bad right away.  What kind of community goes \"\"you have bad ideas\"\" go away, instead of helping rewrite them.   \"'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "3_fTvxpicMYt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "257ef2fd-2ddc-4a24-9623-7d64263fb267"
      },
      "cell_type": "code",
      "source": [
        "sentences.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(159571,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "AuH8HypucQE3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "possible_labels = [\"toxic\",\"severe_toxic\",\"obscene\",\"threat\",\"insult\",\"identity_hate\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "u7ac9LAScmSy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "targets = train[possible_labels].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EkAtq20EcraC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a12b80b1-1112-4d51-d3cd-2af8f2b195ff"
      },
      "cell_type": "code",
      "source": [
        "print(\"max sequence length:\", max(len(s) for s in sentences))\n",
        "print(\"min sequence length:\", min(len(s) for s in sentences))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "max sequence length: 5000\n",
            "min sequence length: 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aLWiZ1Mkc_KB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "s = sorted(len(s) for s in sentences)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-xcIPk1-dIvk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words = MAX_VOCAB_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A7LkUa7edSLX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tokenizer.fit_on_texts(sentences)\n",
        "sequences = tokenizer.texts_to_sequences(sentences)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qKVpcjQ4daNu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get word -> integer mapping\n",
        "word2idx = tokenizer.word_index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9gX-Rd1Fdj4G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17034
        },
        "outputId": "f4459f29-8d4f-42d9-f4c6-b6af0f8098ab"
      },
      "cell_type": "code",
      "source": [
        "word2idx"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'the': 1,\n",
              " 'to': 2,\n",
              " 'of': 3,\n",
              " 'and': 4,\n",
              " 'a': 5,\n",
              " 'you': 6,\n",
              " 'i': 7,\n",
              " 'is': 8,\n",
              " 'that': 9,\n",
              " 'in': 10,\n",
              " 'it': 11,\n",
              " 'for': 12,\n",
              " 'this': 13,\n",
              " 'not': 14,\n",
              " 'on': 15,\n",
              " 'be': 16,\n",
              " 'as': 17,\n",
              " 'have': 18,\n",
              " 'are': 19,\n",
              " 'your': 20,\n",
              " 'with': 21,\n",
              " 'if': 22,\n",
              " 'article': 23,\n",
              " 'was': 24,\n",
              " 'or': 25,\n",
              " 'but': 26,\n",
              " 'page': 27,\n",
              " 'wikipedia': 28,\n",
              " 'my': 29,\n",
              " 'an': 30,\n",
              " 'from': 31,\n",
              " 'by': 32,\n",
              " 'do': 33,\n",
              " 'at': 34,\n",
              " 'me': 35,\n",
              " 'about': 36,\n",
              " 'so': 37,\n",
              " 'talk': 38,\n",
              " 'what': 39,\n",
              " 'can': 40,\n",
              " 'there': 41,\n",
              " 'all': 42,\n",
              " 'has': 43,\n",
              " 'will': 44,\n",
              " 'please': 45,\n",
              " 'no': 46,\n",
              " 'would': 47,\n",
              " 'one': 48,\n",
              " 'like': 49,\n",
              " 'just': 50,\n",
              " 'they': 51,\n",
              " 'he': 52,\n",
              " 'which': 53,\n",
              " 'any': 54,\n",
              " 'been': 55,\n",
              " 'should': 56,\n",
              " 'more': 57,\n",
              " 'we': 58,\n",
              " \"don't\": 59,\n",
              " 'some': 60,\n",
              " 'other': 61,\n",
              " 'who': 62,\n",
              " 'here': 63,\n",
              " 'see': 64,\n",
              " 'also': 65,\n",
              " 'his': 66,\n",
              " 'think': 67,\n",
              " 'because': 68,\n",
              " 'know': 69,\n",
              " 'how': 70,\n",
              " 'edit': 71,\n",
              " 'am': 72,\n",
              " \"i'm\": 73,\n",
              " 'people': 74,\n",
              " 'why': 75,\n",
              " 'up': 76,\n",
              " 'only': 77,\n",
              " \"it's\": 78,\n",
              " 'out': 79,\n",
              " 'articles': 80,\n",
              " 'use': 81,\n",
              " 'when': 82,\n",
              " 'then': 83,\n",
              " 'time': 84,\n",
              " 'may': 85,\n",
              " 'were': 86,\n",
              " 'did': 87,\n",
              " 'them': 88,\n",
              " 'now': 89,\n",
              " 'being': 90,\n",
              " 'user': 91,\n",
              " 'their': 92,\n",
              " 'than': 93,\n",
              " 'thanks': 94,\n",
              " 'even': 95,\n",
              " 'get': 96,\n",
              " 'make': 97,\n",
              " 'good': 98,\n",
              " 'had': 99,\n",
              " 'well': 100,\n",
              " 'very': 101,\n",
              " 'information': 102,\n",
              " 'does': 103,\n",
              " 'could': 104,\n",
              " 'want': 105,\n",
              " 'deletion': 106,\n",
              " 'its': 107,\n",
              " 'such': 108,\n",
              " 'sources': 109,\n",
              " 'way': 110,\n",
              " 'name': 111,\n",
              " 'these': 112,\n",
              " 'first': 113,\n",
              " 'wp': 114,\n",
              " 'help': 115,\n",
              " 'pages': 116,\n",
              " 'new': 117,\n",
              " 'image': 118,\n",
              " 'source': 119,\n",
              " 'editing': 120,\n",
              " 'go': 121,\n",
              " 'need': 122,\n",
              " 'section': 123,\n",
              " 'say': 124,\n",
              " 'again': 125,\n",
              " 'edits': 126,\n",
              " 'thank': 127,\n",
              " 'where': 128,\n",
              " 'fuck': 129,\n",
              " 'made': 130,\n",
              " 'many': 131,\n",
              " 'much': 132,\n",
              " 'used': 133,\n",
              " 'really': 134,\n",
              " 'most': 135,\n",
              " 'deleted': 136,\n",
              " 'discussion': 137,\n",
              " 'same': 138,\n",
              " 'find': 139,\n",
              " 'into': 140,\n",
              " 'work': 141,\n",
              " 'those': 142,\n",
              " 'since': 143,\n",
              " \"i've\": 144,\n",
              " 'right': 145,\n",
              " 'point': 146,\n",
              " 'before': 147,\n",
              " 'after': 148,\n",
              " 'add': 149,\n",
              " 'read': 150,\n",
              " 'look': 151,\n",
              " 'over': 152,\n",
              " 'him': 153,\n",
              " 'take': 154,\n",
              " 'two': 155,\n",
              " 'still': 156,\n",
              " 'back': 157,\n",
              " 'wiki': 158,\n",
              " 'someone': 159,\n",
              " 'fact': 160,\n",
              " 'hi': 161,\n",
              " 'too': 162,\n",
              " 'list': 163,\n",
              " 'link': 164,\n",
              " 'own': 165,\n",
              " 'said': 166,\n",
              " 'something': 167,\n",
              " 'going': 168,\n",
              " 'blocked': 169,\n",
              " '1': 170,\n",
              " '2': 171,\n",
              " 'stop': 172,\n",
              " \"you're\": 173,\n",
              " 'content': 174,\n",
              " 'without': 175,\n",
              " 'block': 176,\n",
              " 'under': 177,\n",
              " 'history': 178,\n",
              " 'http': 179,\n",
              " 'our': 180,\n",
              " 'added': 181,\n",
              " 'utc': 182,\n",
              " 'editors': 183,\n",
              " 'another': 184,\n",
              " 'removed': 185,\n",
              " 'her': 186,\n",
              " 'might': 187,\n",
              " 'welcome': 188,\n",
              " 'note': 189,\n",
              " 'however': 190,\n",
              " 'free': 191,\n",
              " 'place': 192,\n",
              " 'sure': 193,\n",
              " 'case': 194,\n",
              " 'never': 195,\n",
              " \"doesn't\": 196,\n",
              " 'done': 197,\n",
              " 'us': 198,\n",
              " 'vandalism': 199,\n",
              " 'reason': 200,\n",
              " 'put': 201,\n",
              " 'comment': 202,\n",
              " 'personal': 203,\n",
              " 'better': 204,\n",
              " \"that's\": 205,\n",
              " 'yourself': 206,\n",
              " 'using': 207,\n",
              " 'seems': 208,\n",
              " 'ask': 209,\n",
              " 'actually': 210,\n",
              " 'question': 211,\n",
              " 'off': 212,\n",
              " 'while': 213,\n",
              " 'feel': 214,\n",
              " 'anything': 215,\n",
              " 'believe': 216,\n",
              " 'links': 217,\n",
              " 'person': 218,\n",
              " 'things': 219,\n",
              " 'both': 220,\n",
              " 'she': 221,\n",
              " 'best': 222,\n",
              " 'comments': 223,\n",
              " 'policy': 224,\n",
              " 'part': 225,\n",
              " 'hope': 226,\n",
              " 'against': 227,\n",
              " \"can't\": 228,\n",
              " 'already': 229,\n",
              " 'keep': 230,\n",
              " 'thing': 231,\n",
              " '3': 232,\n",
              " 'u': 233,\n",
              " \"didn't\": 234,\n",
              " 'questions': 235,\n",
              " \"i'll\": 236,\n",
              " 'com': 237,\n",
              " 'nothing': 238,\n",
              " 'change': 239,\n",
              " 'wrong': 240,\n",
              " 'though': 241,\n",
              " 'subject': 242,\n",
              " 'problem': 243,\n",
              " 'remove': 244,\n",
              " 'little': 245,\n",
              " 'copyright': 246,\n",
              " 'tag': 247,\n",
              " '•': 248,\n",
              " 'trying': 249,\n",
              " 'long': 250,\n",
              " 'must': 251,\n",
              " 'understand': 252,\n",
              " 'above': 253,\n",
              " 'speedy': 254,\n",
              " 'anyone': 255,\n",
              " 'few': 256,\n",
              " 'world': 257,\n",
              " 'issue': 258,\n",
              " 'last': 259,\n",
              " 'others': 260,\n",
              " 'give': 261,\n",
              " 'editor': 262,\n",
              " 'sorry': 263,\n",
              " 'agree': 264,\n",
              " 'reliable': 265,\n",
              " 'rather': 266,\n",
              " 'let': 267,\n",
              " 'years': 268,\n",
              " 'fair': 269,\n",
              " 'english': 270,\n",
              " 'different': 271,\n",
              " 'making': 272,\n",
              " 'reference': 273,\n",
              " 'come': 274,\n",
              " 'style': 275,\n",
              " 'text': 276,\n",
              " 'references': 277,\n",
              " 'mean': 278,\n",
              " 'try': 279,\n",
              " 'non': 280,\n",
              " 'continue': 281,\n",
              " 'doing': 282,\n",
              " 'great': 283,\n",
              " 'found': 284,\n",
              " 'leave': 285,\n",
              " 'word': 286,\n",
              " 'says': 287,\n",
              " 'got': 288,\n",
              " 'state': 289,\n",
              " 'original': 290,\n",
              " \"isn't\": 291,\n",
              " 'probably': 292,\n",
              " 'site': 293,\n",
              " 'adding': 294,\n",
              " 'every': 295,\n",
              " 'check': 296,\n",
              " 'day': 297,\n",
              " 'simply': 298,\n",
              " 'created': 299,\n",
              " 'life': 300,\n",
              " 'top': 301,\n",
              " 'hello': 302,\n",
              " 'show': 303,\n",
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              " 'scientific': 994,\n",
              " 'arguments': 995,\n",
              " 'tutorial': 996,\n",
              " '2012': 997,\n",
              " 'n': 998,\n",
              " 'reported': 999,\n",
              " 'border': 1000,\n",
              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "Dm_4nx7meR9j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6b3872bd-7342-4a71-c79e-445f4da1a6e9"
      },
      "cell_type": "code",
      "source": [
        "len(word2idx)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "210337"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "ho1VNbeQek5w",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# pad sequences so that we get N x T matrix\n",
        "\n",
        "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pSUjXzWffIVh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c40b24c-859c-44ca-fece-bf65a8792283"
      },
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(159571, 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "nWhyuXlafrLf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "e8249ece-d216-4e71-cdd7-2d85cb7d4eb5"
      },
      "cell_type": "code",
      "source": [
        "data"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[    0,     0,     0, ...,  4583,  2273,   985],\n",
              "       [    0,     0,     0, ...,   589,  8377,   182],\n",
              "       [    0,     0,     0, ...,     1,   737,   468],\n",
              "       ...,\n",
              "       [    0,     0,     0, ...,  3509, 13675,  4528],\n",
              "       [    0,     0,     0, ...,   151,    34,    11],\n",
              "       [    0,     0,     0, ...,  1627,  2056,    88]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "3yWNcScYfs9Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad2a6339-09a9-4a1b-d306-557c811d1400"
      },
      "cell_type": "code",
      "source": [
        "# prepare our embedding matrix\n",
        "\n",
        "num_words = min(MAX_VOCAB_SIZE,len(word2idx)+1)\n",
        "num_words"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "omvRIxIFgIpR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "embedding_matrix = np.zeros((num_words,EMBEDDING_DIM))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GkDhKkOngTlt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for word, i in word2idx.items():\n",
        "  \n",
        "  if i < MAX_VOCAB_SIZE:\n",
        "    \n",
        "    \n",
        "    embedding_vector = word2vec.get(word)\n",
        "    if embedding_vector is not None:\n",
        "      \n",
        "      embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ta1chkLeo0-K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "7c371116-b235-41dc-e030-3e3f142c2f4e"
      },
      "cell_type": "code",
      "source": [
        "embedding_matrix"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
              "         0.        ,  0.        ],\n",
              "       [-0.038194  , -0.24487001,  0.72812003, ..., -0.1459    ,\n",
              "         0.82779998,  0.27061999],\n",
              "       [-0.18970001,  0.050024  ,  0.19084001, ..., -0.39804   ,\n",
              "         0.47646999, -0.15983   ],\n",
              "       ...,\n",
              "       [-1.04809999, -0.32789999, -0.46593001, ..., -0.065447  ,\n",
              "        -0.13019   ,  0.44433001],\n",
              "       [ 0.089782  , -0.33164999, -0.85716999, ..., -0.15765999,\n",
              "        -0.58174002,  0.036233  ],\n",
              "       [ 0.051446  , -0.43564001,  0.26989001, ...,  0.39087   ,\n",
              "        -0.14459001, -0.72372001]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "03r_SMsEo2X1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "embedding_layer = Embedding(num_words,EMBEDDING_DIM,\n",
        "                           weights = [embedding_matrix],\n",
        "                           input_length = MAX_SEQUENCE_LENGTH,\n",
        "                           trainable=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "U0Ab3QyMqE32",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "d3bf2373-f045-40c5-c938-51435d148dbd"
      },
      "cell_type": "code",
      "source": [
        "input_ = Input(shape=(MAX_SEQUENCE_LENGTH,))\n",
        "x = embedding_layer(input_)\n",
        "x = Conv1D(128,3,activation=\"relu\")(x)\n",
        "x = MaxPooling1D(3)(x)\n",
        "x = Conv1D(128,3,activation=\"relu\")(x)\n",
        "x = MaxPooling1D(3)(x)\n",
        "x = Conv1D(128,3,activation=\"relu\")(x)\n",
        "x = GlobalAveragePooling1D()(x)\n",
        "x = Dense(128,activation=\"relu\")(x)\n",
        "\n",
        "output = Dense(len(possible_labels),activation=\"sigmoid\")(x)\n",
        "\n",
        "model = Model(input_,output)\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\",\n",
        "             optimizer=\"rmsprop\",\n",
        "             metrics=[\"accuracy\"])\n",
        "\n",
        "r = model.fit(data,targets,batch_size=BATCH_SIZE,epochs=EPOCHS,validation_split=VALIDATION_SPLIT)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 127656 samples, validate on 31915 samples\n",
            "Epoch 1/10\n",
            "127656/127656 [==============================] - 15s 121us/step - loss: 0.0873 - acc: 0.9723 - val_loss: 0.0810 - val_acc: 0.9751\n",
            "Epoch 2/10\n",
            "127656/127656 [==============================] - 12s 90us/step - loss: 0.0682 - acc: 0.9772 - val_loss: 0.0699 - val_acc: 0.9766\n",
            "Epoch 3/10\n",
            "127656/127656 [==============================] - 12s 90us/step - loss: 0.0639 - acc: 0.9783 - val_loss: 0.0738 - val_acc: 0.9767\n",
            "Epoch 4/10\n",
            "127656/127656 [==============================] - 11s 90us/step - loss: 0.0604 - acc: 0.9791 - val_loss: 0.0665 - val_acc: 0.9772\n",
            "Epoch 5/10\n",
            "127656/127656 [==============================] - 11s 90us/step - loss: 0.0576 - acc: 0.9797 - val_loss: 0.0680 - val_acc: 0.9770\n",
            "Epoch 6/10\n",
            "127656/127656 [==============================] - 11s 90us/step - loss: 0.0549 - acc: 0.9804 - val_loss: 0.0688 - val_acc: 0.9780\n",
            "Epoch 7/10\n",
            "127656/127656 [==============================] - 11s 90us/step - loss: 0.0525 - acc: 0.9811 - val_loss: 0.0719 - val_acc: 0.9775\n",
            "Epoch 8/10\n",
            "127656/127656 [==============================] - 11s 90us/step - loss: 0.0504 - acc: 0.9817 - val_loss: 0.0721 - val_acc: 0.9764\n",
            "Epoch 9/10\n",
            "127656/127656 [==============================] - 11s 89us/step - loss: 0.0484 - acc: 0.9823 - val_loss: 0.0730 - val_acc: 0.9776\n",
            "Epoch 10/10\n",
            "127656/127656 [==============================] - 11s 89us/step - loss: 0.0466 - acc: 0.9829 - val_loss: 0.0819 - val_acc: 0.9770\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VKn3yGXGrvQO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "p = model.predict(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j9VzgzP9tGad",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6392428-a553-4741-8c62-d5365af872af"
      },
      "cell_type": "code",
      "source": [
        "aucs = []\n",
        "for j in range(6):\n",
        "  \n",
        "  auc = roc_auc_score(targets[:,j],p[:,j])\n",
        "  aucs.append(auc)\n",
        "  \n",
        "print(np.mean(aucs))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9754604795205433\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q23pjNjEtT50",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}