binary-toxic-comment-classification.ipynb

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      "name": "BinaryToxicClassification.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/grecosalvatore/binary-toxicity-classification/blob/master/binary-toxic-comment-classification.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": "cP68IepLkhA_",
        "colab_type": "text"
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      "source": [
        "#**BINARY TOXIC COMMENTS CLASSIFICATION**\n",
        "Train a neural network that predicts whether an input comment text contains inappropriate contents or is clean. The problem is treated as a binary classification problem, where the label **1** corresponds to **toxic** and the label **0** to **clean**. The toxic class label contains differents levels of toxicity (severe_toxicity, obscene, threat, insult, identity_attack, sexual_explicit).\n",
        "\n",
        "In particular, the neural network is composed by an embedding layer derived exploiting transfer learning from [GloVe](https://nlp.stanford.edu/projects/glove/) (glove.6B.zip), two bidirectional **LSTM** layers and a **Dense** layer.\n",
        "\n",
        "The dataset used for the training is [Civil Comments Dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) (train.csv)\n",
        "\n",
        "\n",
        "## Requirements\n",
        "Tensorflow 2.x\n",
        "\n",
        "Check TensorFlow version"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YqTN7ws1yy5",
        "colab_type": "code",
        "colab": {
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          "height": 34
        },
        "outputId": "3a975ef1-77db-4526-bee0-3e90c33dedc0"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.version.VERSION)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUNdbVLZ5V4A",
        "colab_type": "text"
      },
      "source": [
        "Uninstall TensorFlow if version is lower then 2.x."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY_X5yG42AyM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip uninstall tensorflow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vx7SzgYY2E8e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install tensorflow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDjafWEz2Iym",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip uninstall keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "et15qu4N2KJu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0R6m6Hk2Nc_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import os"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O31Vcnv92SqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Read datasets from csv to pandas\n",
        "train = pd.read_csv(\"/content/drive/My Drive/Datasets/new_dataset/train.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXmG2hIw2uUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5ac11760-d86c-4442-9421-1682fac2b323"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1804874, 45)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0pOREcYnPNn",
        "colab_type": "text"
      },
      "source": [
        "The training dataset is composed by 1804874 rows, each one corresponding to a toxic comment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZD6p6g5QjvI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "6e902a09-4cef-4151-db39-5d67ab1f0596"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>publication_id</th>\n",
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              "      <th>article_id</th>\n",
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              "      <th>likes</th>\n",
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              "      <th>identity_annotator_count</th>\n",
              "      <th>toxicity_annotator_count</th>\n",
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              "      <td>haha you guys are a bunch of losers.</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id    target  ... identity_annotator_count  toxicity_annotator_count\n",
              "0  59848  0.000000  ...                        0                         4\n",
              "1  59849  0.000000  ...                        0                         4\n",
              "2  59852  0.000000  ...                        0                         4\n",
              "3  59855  0.000000  ...                        0                         4\n",
              "4  59856  0.893617  ...                        4                        47\n",
              "\n",
              "[5 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAZFNBpg0Mak",
        "colab_type": "text"
      },
      "source": [
        "The ***comment_text*** column contains the input comment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xo-JjzDU2vO1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8c8dcd60-8d2f-4203-b66a-47dc4631ab36"
      },
      "source": [
        "train['comment_text'][4]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'haha you guys are a bunch of losers.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDSiG6S1oOQ7",
        "colab_type": "text"
      },
      "source": [
        "##**Data Cleaning** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkAo7gEy3Kq8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"can't\", \"cannot \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\"\\'scuse\", \" excuse \", text)\n",
        "    text = re.sub('\\W', ' ', text)\n",
        "    text = re.sub('\\s+', ' ', text)\n",
        "    text = text.strip(' ')\n",
        "    return text\n",
        "\n",
        "  "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YGpXiyp3PFs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['comment_text'] = train['comment_text'].map(lambda com : clean_text(com))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAukiktnG_SY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RE_PATTERNS = {\n",
        "    ' fuck':\n",
        "        [\n",
        "            '(f)(u|[^a-z0-9 ])(c|[^a-z0-9 ])(k|[^a-z0-9 ])([^ ])*',\n",
        "            '(f)([^a-z]*)(u)([^a-z]*)(c)([^a-z]*)(k)',\n",
        "            ' f[!@#\\$%\\^\\&\\*]*u[!@#\\$%\\^&\\*]*k', 'f u u c',\n",
        "            '(f)(c|[^a-z ])(u|[^a-z ])(k)', r'f\\*',\n",
        "            'feck ', ' fux ', 'f\\*\\*', \n",
        "             'f\\.u\\.', 'f###', ' fu ', 'f@ck', 'f u c k', 'f uck', 'f ck','fuk','fk'\n",
        "        ],\n",
        "    ' ass ':\n",
        "        [\n",
        "            '[^a-z]ass ', '[^a-z]azz ', 'arrse', ' arse ', '@\\$\\$','[^a-z]anus', ' a\\*s\\*s', '[^a-z]ass[^a-z ]',\n",
        "            'a[@#\\$%\\^&\\*][@#\\$%\\^&\\*]', '[^a-z]anal ', 'a s s'\n",
        "        ],\n",
        "    ' asshole ':\n",
        "        [\n",
        "            ' a[s|z]*wipe', 'a[s|z]*[w]*h[o|0]+[l]*e', '@\\$\\$hole'\n",
        "        ],\n",
        "    ' bitch ':\n",
        "        [\n",
        "            'b[w]*i[t]*ch', 'b!tch',\n",
        "            'bi\\+ch', 'b!\\+ch', '(b)([^a-z]*)(i)([^a-z]*)(t)([^a-z]*)(c)([^a-z]*)(h)',\n",
        "            'biatch', 'bi\\*\\*h', 'bytch', 'b i t c h'\n",
        "        ],\n",
        "    ' bastard ':\n",
        "        [\n",
        "            'ba[s|z]+t[e|a]+rd'\n",
        "        ],\n",
        "    ' trans gender':\n",
        "        [\n",
        "            'transgender'\n",
        "        ],\n",
        "    ' gay ':\n",
        "        [\n",
        "            'gay' , 'g4y'\n",
        "        ],\n",
        "    ' cock ':\n",
        "        [\n",
        "            '[^a-z]cock', 'c0ck', '[^a-z]cok ', 'c0k', '[^a-z]cok[^aeiou]', ' cawk',\n",
        "            '(c)([^a-z ])(o)([^a-z ]*)(c)([^a-z ]*)(k)', 'c o c k'\n",
        "        ],\n",
        "    ' dick ':\n",
        "        [\n",
        "            ' dick[^aeiou]', 'deek', 'd i c k','dik'\n",
        "        ],\n",
        "    ' suck ':\n",
        "        [\n",
        "             '(s)([^a-z ]*)(u)([^a-z ]*)(c)([^a-z ]*)(k)', 'suck', '5uck', 's u c k'\n",
        "        ],\n",
        "    ' cunt ':\n",
        "        [\n",
        "            'cunt', 'c u n t'\n",
        "        ],\n",
        "    ' bullshit ':\n",
        "        [\n",
        "            'bullsh\\*t', 'bull\\$hit'\n",
        "        ],\n",
        "    ' idiot ':\n",
        "        [\n",
        "            'i[d]+io[t]+', '(i)([^a-z ]*)(d)([^a-z ]*)(i)([^a-z ]*)(o)([^a-z ]*)(t)',  'i d i o t'\n",
        "                                                                                     \n",
        "        ],\n",
        "    ' dumb ':\n",
        "        [\n",
        "            '(d)([^a-z ]*)(u)([^a-z ]*)(m)([^a-z ]*)(b)'\n",
        "        ],\n",
        "    ' shit ':\n",
        "        [\n",
        "            'shitty', '(s)([^a-z ]*)(h)([^a-z ]*)(i)([^a-z ]*)(t)', 'shite', '\\$hit', 's h i t'\n",
        "        ],\n",
        "    ' shit hole ':\n",
        "        [\n",
        "            'shythole'\n",
        "        ],\n",
        "    ' retard ':\n",
        "        [\n",
        "            'returd', 'retad', 'retard', 'wiktard', 'wikitud'\n",
        "        ],\n",
        "    ' rape ':\n",
        "        [\n",
        "            ' raped'\n",
        "        ],\n",
        "    ' dumb ass':\n",
        "        [\n",
        "            'dumbass', 'dubass'\n",
        "        ],\n",
        "    ' ass head':\n",
        "        [\n",
        "            'butthead'\n",
        "        ],\n",
        "    ' sex ':\n",
        "        [\n",
        "             's3x'\n",
        "        ],\n",
        "    ' nigger ':\n",
        "        [\n",
        "            'nigger', ' nigr ', 'negrito', 'niguh', 'n3gr', 'n i g g e r'\n",
        "        ],\n",
        "    ' nigga ':\n",
        "        [\n",
        "            'niga', 'ni[g]+a', ' nigg[a]+'\n",
        "        ],\n",
        "    ' shut the fuck up':\n",
        "        [\n",
        "            'stfu'\n",
        "        ],\n",
        "    ' pussy ':\n",
        "        [\n",
        "            'pussy[^c]', 'pusy', 'pussi[^l]', 'pusses'\n",
        "        ],\n",
        "    ' faggot ':\n",
        "        [\n",
        "            'faggot', ' fa[g]+[s]*[^a-z ]', 'fagot', 'f a g g o t', 'faggit',\n",
        "            '(f)([^a-z ]*)(a)([^a-z ]*)([g]+)([^a-z ]*)(o)([^a-z ]*)(t)', 'fau[g]+ot', 'fae[g]+ot',\n",
        "        ],\n",
        "    ' motherfucker':\n",
        "        [\n",
        "            ' motha ', ' motha f', ' mother f', 'motherucker',\n",
        "        ],\n",
        "    ' whore ':\n",
        "        [\n",
        "             'w h o r e'\n",
        "        ],\n",
        "}\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TR6ujVwOLvhJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for target,patterns in RE_PATTERNS.items():\n",
        "  for pat in patterns:\n",
        "    train['comment_text'] = train['comment_text'].map(lambda x : re.sub(pat,target,x))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnfjxSefQiqe",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z18oxABU3Vh-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "904b82be-616c-4213-c115-0edd2ac32a3d"
      },
      "source": [
        "X = train.comment_text\n",
        "print(X[16])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "interesting i will be curious to see how this works out i often refrain from commenting because i do not have the time or desire to engage with the couple of resident trolls who seem to jump on every active ww comment thread\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOF5YuGH3X8t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "92ba401e-16db-435f-da5c-49c25495be28"
      },
      "source": [
        "print(X.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1804874,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poR06Ke83a0J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y = [1 if x >= 0.5 else 0 for x in train[\"target\"]]\n",
        "Y = np.array(Y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sl_3qf88lhQ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "657500ca-64fc-4646-e311-c313592b8fb7"
      },
      "source": [
        "df= train[['id','comment_text']]\n",
        "df.head()"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59848</td>\n",
              "      <td>this is so cool it like would you want your mo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>59849</td>\n",
              "      <td>thank you this would make my life a lot less a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>59852</td>\n",
              "      <td>this is such an urgent design problem kudos to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59855</td>\n",
              "      <td>is this something i will be able to install on...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>59856</td>\n",
              "      <td>haha you guys are a bunch of losers</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id                                       comment_text\n",
              "0  59848  this is so cool it like would you want your mo...\n",
              "1  59849  thank you this would make my life a lot less a...\n",
              "2  59852  this is such an urgent design problem kudos to...\n",
              "3  59855  is this something i will be able to install on...\n",
              "4  59856                haha you guys are a bunch of losers"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQdi2zVlVdjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2 = df.assign(label = Y) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teUxDiH3QxpR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "58ed3b8a-34c1-4d28-d984-7d9e250edfc1"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "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>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>59848</td>\n",
              "      <td>this is so cool it like would you want your mo...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>59849</td>\n",
              "      <td>thank you this would make my life a lot less a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>59852</td>\n",
              "      <td>this is such an urgent design problem kudos to...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59855</td>\n",
              "      <td>is this something i will be able to install on...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>59856</td>\n",
              "      <td>haha you guys are a bunch of losers</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id                                       comment_text  label\n",
              "0  59848  this is so cool it like would you want your mo...      0\n",
              "1  59849  thank you this would make my life a lot less a...      0\n",
              "2  59852  this is such an urgent design problem kudos to...      0\n",
              "3  59855  is this something i will be able to install on...      0\n",
              "4  59856                haha you guys are a bunch of losers      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8xrEJY9Vw32",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_toxic = df2[df2['label'] == 1]\n",
        "df_non_toxic = df2[df2['label'] == 0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dad-fxaQWKL-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9e0ee470-8d7e-4c9e-bf99-a0c8c8a22944"
      },
      "source": [
        "print(\"Toxic = \", len(df_toxic))\n",
        "print(\"Not Toxic = \", len(df_non_toxic))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Toxic =  144334\n",
            "Not Toxic =  1660540\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KernWDUpW3VR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_non_toxic_subset = df_non_toxic.drop_duplicates(subset =\"comment_text\", \n",
        "                     keep = 'first', inplace = False) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ozZiM6DXGUi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e37c6ad1-6341-4052-df17-8f11654b6899"
      },
      "source": [
        "print(\"Not Toxic = \", len(df_non_toxic_subset))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Not Toxic =  1627591\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12A6OKtSYI-u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_non_toxic_sample =  df_non_toxic_subset.sample(n=150000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elkGiOMkYPj7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eeb41e24-9963-4b21-aa0f-f4110566fb7e"
      },
      "source": [
        "print(len(df_non_toxic_sample))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "150000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jzOUHOuYmTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_concat =  pd.concat([df_toxic,df_non_toxic_sample])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFKJa9Y1YwGQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ce345b39-bc85-4e56-9d4c-6cad19a5a00f"
      },
      "source": [
        "train_concat.head()\n"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>4</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>59859</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>239583</td>\n",
              "      <td>it ridiculous that these guys are being called...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>239584</td>\n",
              "      <td>this story gets more ridiculous by the hour an...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>239592</td>\n",
              "      <td>angry trolls misogynists and racists oh my it ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                                       comment_text  label\n",
              "4    59856                haha you guys are a bunch of losers      1\n",
              "5    59859                                ur a sh tty comment      1\n",
              "13  239583  it ridiculous that these guys are being called...      1\n",
              "14  239584  this story gets more ridiculous by the hour an...      1\n",
              "19  239592  angry trolls misogynists and racists oh my it ...      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_4JqyayZS8M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "12fe200b-2dea-4585-b6c2-e3e0624fd2b8"
      },
      "source": [
        "train = train_concat.sample(frac=1).reset_index(drop=True)\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6094354</td>\n",
              "      <td>i not sure who you calling you lot accordingly...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5221459</td>\n",
              "      <td>did you read this article carefully kim jong u...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>468789</td>\n",
              "      <td>you are assuming anyone that does not have a d...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6189929</td>\n",
              "      <td>well she basically a brain dead vegetable so i...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>941261</td>\n",
              "      <td>liberals will mildly chastise justin for break...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                                       comment_text  label\n",
              "0  6094354  i not sure who you calling you lot accordingly...      0\n",
              "1  5221459  did you read this article carefully kim jong u...      1\n",
              "2   468789  you are assuming anyone that does not have a d...      1\n",
              "3  6189929  well she basically a brain dead vegetable so i...      1\n",
              "4   941261  liberals will mildly chastise justin for break...      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CO6QZgZZdqh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = train.comment_text\n",
        "Y = train.label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FC4PB9r7NjuO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "10875340-6e16-4abe-8d70-94f1ba152b81"
      },
      "source": [
        "print(train['comment_text'][122])\n",
        "print(train['label'][122])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "i love donald trump the businessman america is and has been paying through the nose for the un and nafta and getting sh t in exchange they should be called on their waste and horrible results why should we be funding the major part of the un budget while they do little more than undermine us next we should stop funding to countries who hate and badmouth us a positive affect on the budget too\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-QYoKUsHkls",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "43b62b3d-f144-4391-908d-5ff84039db47"
      },
      "source": [
        "count_0 = 0\n",
        "count_1 = 0\n",
        "for y in Y:\n",
        "  if (y == 0):\n",
        "    count_0 += 1\n",
        "  else:\n",
        "    count_1 += 1\n",
        "\n",
        "print(\"Number of Not Toxic comments \",count_0)\n",
        "print(\"Number of Toxic comments \",count_1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Not Toxic comments  150000\n",
            "Number of Toxic comments  144334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AH9EjLzPISOf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca03d78a-a7cf-43dd-e4cd-7a43a9e8840f"
      },
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "MAX_NB_WORDS = 2000000\n",
        "\n",
        "tokenizer = Tokenizer(lower=False,oov_token=\"<OOV>\",num_words = MAX_NB_WORDS)\n",
        "tokenizer.fit_on_texts(X)\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHp3x-EwJuyk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7c88f0b1-56c8-469c-8590-9a5d9ee3a8f8"
      },
      "source": [
        "max_seq = 0\n",
        "sum_len = 0\n",
        "for s in sequences:\n",
        "  tmp_len = len(s)\n",
        "  sum_len += tmp_len\n",
        "  if (tmp_len > max_seq):\n",
        "    max_seq = tmp_len\n",
        "\n",
        "print(\"Max sequence length: \",max_seq)\n",
        "print(\"Mean sequence length: \", round(sum_len/len(sequences),1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Max sequence length:  311\n",
            "Mean sequence length:  51.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWTxVGFcysc8",
        "colab_type": "text"
      },
      "source": [
        "Maximum length of training set is about 300, then pad all input sequences up to 300."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nO8dGf_S3fRM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bc765d52-3b35-44cd-b7bc-5eec0563c0fd"
      },
      "source": [
        "MAX_SEQUENCE_LENGTH = 300\n",
        "pad_type = 'post'\n",
        "trunc_type = 'post'\n",
        "\n",
        "#test_sequences = tokenizer.texts_to_sequences(test['comment_text'])\n",
        "\n",
        "X_pad = pad_sequences(sequences, padding=pad_type , truncating=trunc_type, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "print('Shape of train data tensor:', X_pad.shape)\n",
        "\n",
        "#test_data = pad_sequences(test_sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "\n",
        "nb_words = len(tokenizer.word_index)+1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of train data tensor: (294334, 300)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQufY6lLzOhb",
        "colab_type": "text"
      },
      "source": [
        "Representation of one input example with tokenization and padding.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEcfK2nOK8pz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "68bbe24d-2ff7-4f20-9462-ac4a9330ef2e"
      },
      "source": [
        "print(X_pad[10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    2  1387   101     9  3775 19408    25  2572    15    87   120     4\n",
            "  1914   106  4079  4056   729     2   248    61    23    73   108    15\n",
            " 28514   624     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZS0u84YQzb5g",
        "colab_type": "text"
      },
      "source": [
        "Save tokenizer as pickle file.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Li-uEBa7eGzf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "\n",
        "# saving\n",
        "with open('/content/drive/My Drive/Saved_Models/BinaryToxicClassification/tokenizer_hope.pickle', 'wb') as handle:\n",
        "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ayt47dmy3gOW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8e8ecf4a-6dcb-4f84-8570-3066dacd137a"
      },
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "embedding_vector = {}\n",
        "f = open('/content/drive/My Drive/Datasets/glove.840B.300d.txt')\n",
        "for line in tqdm(f):\n",
        "    value = line.split(' ')\n",
        "    word = value[0]\n",
        "    coef = np.array(value[1:],dtype = 'float32')\n",
        "    embedding_vector[word] = coef"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2196017it [02:56, 12437.83it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RuBRZiVP3jFA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1496261d-d457-46e0-a047-5aa55aa8f9af"
      },
      "source": [
        "embedding_matrix = np.zeros((nb_words,300))\n",
        "\n",
        "for word,i in tqdm(tokenizer.word_index.items()):\n",
        "    embedding_value = embedding_vector.get(word)\n",
        "    if embedding_value is not None:\n",
        "        embedding_matrix[i] = embedding_value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 121901/121901 [00:00<00:00, 431560.00it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2o5uB6oR3oQ3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "checkpoint_path = \"/content/drive/My Drive/Saved_Models/BinaryToxicClassification/cp_subset.ckpt\"\n",
        "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
        "\n",
        "# Create a callback that saves the model's weights\n",
        "cp_callback = ModelCheckpoint(filepath=checkpoint_path,\n",
        "                                                 save_weights_only=True,\n",
        "                                                 verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQg4q1Utt96O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e62de3a8-72d0-4dd1-cce9-cd67613ce43c"
      },
      "source": [
        "print(X_pad.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(294334, 320)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80VzoiWso110",
        "colab_type": "text"
      },
      "source": [
        "## **Create your model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJkrJv-e3qtZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "ef30bd65-3e1f-4477-dace-c0ad6f20768b"
      },
      "source": [
        "from keras.layers.recurrent import LSTM, GRU\n",
        "from keras.layers import Flatten\n",
        "from keras.regularizers import l1\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.layers import Conv1D, MaxPooling1D, Embedding\n",
        "from keras.models import Model\n",
        "from keras.layers.wrappers import TimeDistributed, Bidirectional\n",
        "from keras.layers.core import Dense, Activation, Dropout\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(nb_words,300,weights = [embedding_matrix],input_length=MAX_SEQUENCE_LENGTH,trainable = False))\n",
        "model.add(Bidirectional(LSTM(256, return_sequences=True)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Bidirectional(LSTM(256)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(128, activation=\"relu\"))\n",
        "model.add(Dropout(0.2))\n",
        "#model.add(Dense(64, activation=\"relu\"))\n",
        "#model.add(Dropout(0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.summary()\n",
        "\n",
        "adam_opt = Adam(learning_rate=0.001)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 300, 300)          36570600  \n",
            "_________________________________________________________________\n",
            "bidirectional_5 (Bidirection (None, 300, 512)          1140736   \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 300, 512)          0         \n",
            "_________________________________________________________________\n",
            "bidirectional_6 (Bidirection (None, 512)               1574912   \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 39,352,041\n",
            "Trainable params: 2,781,441\n",
            "Non-trainable params: 36,570,600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgQi-uGq3tjb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c37dddec-829c-46f7-a52a-00466a33cb55"
      },
      "source": [
        "history = model.fit(X_pad, Y, validation_split=0.2, nb_epoch=5, batch_size=256)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 235467 samples, validate on 58867 samples\n",
            "Epoch 1/5\n",
            "235467/235467 [==============================] - 1986s 8ms/step - loss: 0.3392 - accuracy: 0.8548 - val_loss: 0.2743 - val_accuracy: 0.8853\n",
            "Epoch 2/5\n",
            "235467/235467 [==============================] - 2007s 9ms/step - loss: 0.2675 - accuracy: 0.8889 - val_loss: 0.2727 - val_accuracy: 0.8862\n",
            "Epoch 3/5\n",
            "235467/235467 [==============================] - 2096s 9ms/step - loss: 0.2492 - accuracy: 0.8970 - val_loss: 0.2516 - val_accuracy: 0.8949\n",
            "Epoch 4/5\n",
            "235467/235467 [==============================] - 2035s 9ms/step - loss: 0.2346 - accuracy: 0.9031 - val_loss: 0.2487 - val_accuracy: 0.8970\n",
            "Epoch 5/5\n",
            "235467/235467 [==============================] - 2044s 9ms/step - loss: 0.2183 - accuracy: 0.9104 - val_loss: 0.2552 - val_accuracy: 0.8959\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mv8juroUpDtr",
        "colab_type": "text"
      },
      "source": [
        "### **Save the trained model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tM4vXIMZ0moC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(\"/content/drive/My Drive/Saved_Models/BinaryToxicClassification/model_hope.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ9yX7fuT3j_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "6a98c9d4-fc9e-41a4-8002-51b5811133cd"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(history)\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(len(acc))\n",
        "plt.plot(epochs, acc,val_acc, 'b', label='Training accuracy')\n",
        "plt.title('Training accuracy')\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
        "plt.title('Training loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<keras.callbacks.callbacks.History object at 0x7f242c58cfd0>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gD1HPLStiR/lh7p09kpho+89kjDl1liAhaF/VER57dytTR/TjgqGpXpdjjAlz\nFvQh6JG3t1BT18CPZ9lwSmNM+wUU9CIyXUQ2i8hWEbm7mfUZIvKeiHwiImtFZKa7/Ksissbv4ROR\nMR39ISLJpt2VvLpyO9dPGEJOak+vyzHGRIBWg15EooF5wAxgFHCtiIxq0uxe4HVVPRuYAzwOoKov\nq+oYVR0D3AAUquqajvwAkUTVudl3Ynwst08b6nU5xpgIEcgR/Thgq6oWqGot8CpwRZM2CvRyn/cG\ndjXzc65132ta8O6mvfxn6z5unzaUPglxXpdjjIkQgcx1mwbs8HtdDIxv0uY+4C0R+R7QA5jWzM/5\nCif+ggBAROYCcwEyMjICKCny1DX4+N9FG8lO7cH1E4Z4XY4xJoJ01MnYa4HnVDUdmAm8KCJHf7aI\njAeqVfWz5t6sqk+paq6q5qamds1RJi99tI2C0kP8ZOZIYm04pTGmAwWSKDsB/yt20t1l/m4GXgdQ\n1eVAPJDit34O8MqplxnZ9lfX8tt3PueCoSlMGdHP63KMMREmkKBfBQwVkSwRicMJ7flN2mwHpgKI\nyEicoC91X0cB12D98y367Tufc7CmjntnjULE5po3xnSsVoNeVeuB24AlwEac0TXrReR+EbncbXYX\n8E0R+RTnyP1GVVV33YXADlUt6Pjyw19+aRUvfbSNOeMyGD4g0etyjDERKKAbj6rqImBRk2U/83u+\nATivhfcuBSaceomR7cGFG+keG82dlwzzuhRjTISys34e+uDzUv61aS/fnXIaKT27eV2OMSZCWdB7\npL7BxwMLNpKRlMBN52V6XY4xJoJZ0HvktbwdbN5zkHtmjKBbTLTX5RhjIpgFvQcqa+p45K0tjMtK\nYvoZA7wuxxgT4QI6GWs61rx3t1JeXctzNpzSGBMEdkQfZNvLqnn2wyK+dE46o9N7e12OMaYLsKAP\nsocWbyQmWvjhZcO9LsUY00VY0AfRioIyFn+2m29flEP/XvFel2OM6SIs6IPE51N+sXADA3vH880L\nsr0uxxjThVjQB8kbHxfz2c5K7p4xgu5xNpzSGBM8FvRBcOhIPb9espkxg/tw+VmDvC7HGNPFWNAH\nwZP/zmfvwSP8dLYNpzTGBJ8FfSfbuf8wT75fwBfOGsTYIX29LscY0wVZ0HeyX725CYD/nm7DKY0x\n3rCg70SfbK/g/1+zi29ekE163wSvyzHGdFEW9J1EVfnFgg2kJnbjO5NzvC7HGNOFWdB3kn+uLeHj\n7fv54aXD6dHNphQyxnjHgr4T1NQ18MtFGzl9UC++NDbd63KMMV2cBX0n+NMHBew6UMNPZ48iOsqG\nUxpjvGVB38H2Vtbw+NJ8Lgg6buAAAA3HSURBVDu9PxOyk70uxxhjLOg72m/e2kxdg497Zoz0uhRj\njAEs6DvUZzsP8NfVxdw4KZPMlB5el2OMMYAFfYdpHE7ZNyGO26YM9bocY4w5KqCgF5HpIrJZRLaK\nyN3NrM8QkfdE5BMRWSsiM/3WnSkiy0VkvYisE5GInIh9yfo9rCgs545LhtG7e6zX5RhjzFGtDvAW\nkWhgHnAJUAysEpH5qrrBr9m9wOuq+oSIjAIWAZkiEgO8BNygqp+KSDJQ1+GfwmNH6ht4aPFGhvXv\nybXnDva6HGOMOU4gR/TjgK2qWqCqtcCrwBVN2ijQy33eG9jlPr8UWKuqnwKoapmqNrS/7NDy/LIi\ntpVV85NZo4iJtt4wY0xoCSSV0oAdfq+L3WX+7gOuF5FinKP577nLhwEqIktE5GMR+VFzGxCRuSKS\nJyJ5paWlbfoAXiurOsIf/rWVi4enctGwVK/LMcaYE3TU4ee1wHOqmg7MBF4UkSicrqHzga+6/35R\nRKY2fbOqPqWquaqam5oaXmH56DtbqK5r4CezbDilMSY0BRL0OwH/jud0d5m/m4HXAVR1ORAPpOAc\n/b+vqvtUtRrnaP+c9hYdKjbvPshfVmzn+vEZnNYv0etyjDGmWYEE/SpgqIhkiUgcMAeY36TNdmAq\ngIiMxAn6UmAJMFpEEtwTsxcBG4gAqsoDCzfQs1sMt08b5nU5xhjTolaDXlXrgdtwQnsjzuia9SJy\nv4hc7ja7C/imiHwKvALcqI4K4BGcXxZrgI9VdWFnfJBgW7q5lA8+38d/TRtG3x5xXpdjjDEtElX1\nuobj5Obmal5entdlnFRdg4/pv30fn8KS2y8kLsZG2hhjvCUiq1U1t7l1llCn4C8rtpNfeogfzxxp\nIW+MCXmWUm10oLqOR9/ZwqScZKaN7Od1OcYY0yoL+jb63b8+p/JwHT+dPQoRm2veGBP6LOjboKC0\niheWF/GVcwczcmCvVtsbY0wosKBvgwcXbSI+Npo7LxnudSnGGBMwC/oAfbh1H+9s3MOtF+eQmtjN\n63KMMSZgFvQBaPA5c82n9+3ON87L8rocY4xpEwv6ALyet4NNuw9yz4yRxMdGe12OMca0iQV9Kw7W\n1PHwW5s5N7MvM0cP8LocY4xpMwv6Vjy+NJ99VbXcO8uGUxpjwpMF/UnsKK/mzx8UctU5aZw1uI/X\n5RhjzCmxoD+JXy7eRHSU8KPLRnhdijHGnDIL+hasKipn4boSvnVRNgN6R+T9zI0xXYQFfTN87nDK\nAb3imXthttflGGNMu1jQN+P/+2Qna4sP8KPpw0mIi/G6HGOMaRcL+iaqa+v51ZJNnJXemyvHNL0H\nujHGhB8L+iae/HcBeyqP8NPZo4iKsuGUxpjwZ0Hvp+TAYZ58P59ZZw4kNzPJ63KMMaZDWND7+fWb\nm/Ep3D3dhlMaYyKHBb1rzY79/P2TndxyfhaDkxK8LscYYzqMBT2gqjywYAMpPbtx68WneV2OMcZ0\nKAt6YOG6EvK2VfCDS4fRs5sNpzTGRJaAgl5EpovIZhHZKiJ3N7M+Q0TeE5FPRGStiMx0l2eKyGER\nWeM+/tjRH6C9auoaeGjRJkYO7MWXcwd7XY4xxnS4Vg9fRSQamAdcAhQDq0Rkvqpu8Gt2L/C6qj4h\nIqOARUCmuy5fVcd0bNkd58//KWTn/sP8+stnEm3DKY0xESiQI/pxwFZVLVDVWuBV4IombRRovFt2\nb2BXx5XYefYerOHx97Zyyaj+TMpJ8bocY4zpFIEEfRqww+91sbvM333A9SJSjHM0/z2/dVlul86/\nReSC5jYgInNFJE9E8kpLSwOvvp0eeWsLtQ0+fjxzZNC2aYwxwdZRJ2OvBZ5T1XRgJvCiiEQBJUCG\nqp4N3An8RUR6NX2zqj6lqrmqmpuamtpBJZ3c+l0HeC1vB1+bmElWSo+gbNMYY7wQSNDvBPzPUqa7\ny/zdDLwOoKrLgXggRVWPqGqZu3w1kA8Ma2/R7eUMp9xIn+6xfH/KUK/LMcaYThVI0K8ChopIlojE\nAXOA+U3abAemAojISJygLxWRVPdkLiKSDQwFCjqq+FP19oY9LC8o445LhtE7IdbrcowxplO1OupG\nVetF5DZgCRANPKOq60XkfiBPVecDdwFPi8gdOCdmb1RVFZELgftFpA7wAd9W1fJO+zQBqK338eCi\njZzWryfXjcvwshRjjAmKgK4OUtVFOCdZ/Zf9zO/5BuC8Zt73BvBGO2vsUC8sL6KorJpnbzqXmGi7\nXswYE/m6VNKVH6rld//6nIuGpXLx8H5el2OMMUHRpYL+t+9sobq2gXtn2XBKY0zX0WWC/vM9B3l5\nxXauG5fB0P6JXpdjjDFB02WC/n8XbSQhLpo7LvF8dKcxxgRVlwj6pZv3snRzKd+fMpSkHnFel2OM\nMUEV8UFf3+DjfxduJDM5ga9PyvS6HGOMCbqID/pXVm7n871V3DNzJHExEf9xjTHmBBGdfAcO1/HI\n21uYkJ3EpaP6e12OMcZ4IqKD/rF3P2f/4Tp+OnsUIjbXvDGma4rYoC/cd4jnlhVxzdjBnD6ot9fl\nGGOMZyI26B9atJG46CjuusyGUxpjuraIDPpl+ft4a8Mebr34NPolxntdjjHGeCrigr7B58w1n9an\nOzefn+V1OcYY47mIC/q/rd7BhpJK7p4xgvjYaK/LMcYYz0VU0FcdqefXS7YwdkhfZp850OtyjDEm\nJERU0D+xdCv7qo7YcEpjjPETMUFfXFHN0x8UcuWYQYwZ3MfrcowxJmRETNAfqfcxITuZH00f4XUp\nxhgTUgK6lWA4yEntyQvfGOd1GcYYE3Ii5ojeGGNM8yzojTEmwlnQG2NMhLOgN8aYCBdQ0IvIdBHZ\nLCJbReTuZtZniMh7IvKJiKwVkZnNrK8SkR90VOHGGGMC02rQi0g0MA+YAYwCrhWRUU2a3Qu8rqpn\nA3OAx5usfwRY3P5yjTHGtFUgR/TjgK2qWqCqtcCrwBVN2ijQy33eG9jVuEJErgQKgfXtL9cYY0xb\nBRL0acAOv9fF7jJ/9wHXi0gxsAj4HoCI9AT+G/ifk21AROaKSJ6I5JWWlgZYujHGmEB01AVT1wLP\nqerDIjIReFFEzsD5BfCoqladbO4ZVX0KeApAREpFZFs7akkB9rXj/Z3F6mobq6ttrK62icS6hrS0\nIpCg3wkM9nud7i7zdzMwHUBVl4tIPE7B44GrReRXQB/AJyI1qvpYSxtT1dQAamqRiOSpam57fkZn\nsLraxupqG6urbbpaXYEE/SpgqIhk4QT8HOC6Jm22A1OB50RkJBAPlKrqBY0NROQ+oOpkIW+MMabj\ntdpHr6r1wG3AEmAjzuia9SJyv4hc7ja7C/imiHwKvALcqKraWUUbY4wJXEB99Kq6COckq/+yn/k9\n3wCc18rPuO8U6jsVTwVpO21ldbWN1dU2VlfbdKm6xA68jTEmstkUCMYYE+Es6I0xJsKFZdAHMPdO\nNxF5zV2/QkQyQ6SuG93rBNa4j1uCVNczIrJXRD5rYb2IyO/duteKyDkhUtdkETngt79+1ly7Tqhr\nsDt30wYRWS8i/9VMm6DvswDrCvo+E5F4EVkpIp+6dZ1wgaQX38kA6/LqOxntzg22oJl1Hb+vVDWs\nHkA0kA9kA3HAp8CoJm1uBf7oPp8DvBYidd0IPObBPrsQOAf4rIX1M3HmIhJgArAiROqaDCzwYH8N\nBM5xnycCW5r5bxn0fRZgXUHfZ+4+6Ok+jwVWABOatPHiOxlIXV59J+8E/tLcf6vO2FfheEQfyNw7\nVwDPu8//BkyVk12aG7y6PKGq7wPlJ2lyBfCCOj4C+ojIwBCoyxOqWqKqH7vPD+IMK2467UfQ91mA\ndQWduw+q3Jex7qPpKI+gfycDrCvoRCQdmAX8qYUmHb6vwjHoA5l752gbda4DOAAkh0BdAF9y/9T/\nm4gMbma9FwKt3QsT3T+9F4vI6cHeuPtn89k4R4P+PN1nJ6kLPNhnblfEGmAv8Laqtri/gvidDKQu\nCP538rfAjwBfC+s7fF+FY9CHs38Cmap6JvA2x35rm+Z9DAxR1bOAPwD/CObGxZmU7w3gdlWtDOa2\nT6aVujzZZ6raoKpjcKZIGSfOXFeeC6CuoH4nRWQ2sFdVV3fmdpoKx6APZO6do21EJAZn6uQyr+tS\n1TJVPeK+/BMwtpNrClQg+zToVLWy8U9vdS7aixWRlGBsW0RiccL0ZVX9ezNNPNlnrdXl5T5zt7kf\neA937is/XnwnW63Lg+/kecDlIlKE0707RUReatKmw/dVOAb90bl3RCQO52TF/CZt5gNfd59fDbyr\n7pkNL+tq0od7OU4fayiYD3zNHUkyATigqiVeFyUiAxr7JkVkHM7/r50eDu42/wxsVNVHWmgW9H0W\nSF1e7DMRSRWRPu7z7sAlwKYmzYL+nQykrmB/J1X1HlVNV9VMnIx4V1Wvb9Ksw/dVR01THDSqWi8i\njXPvRAPPqDv3DpCnqvNxvgwvishWnJN9c0Kkru+LMz9QvVvXjZ1dF4CIvIIzGiNFnHsG/BznxBSq\n+kec6S1mAluBauCmEKnrauA7IlIPHAbmBOEXNjhHXTcA69z+XYAfAxl+tXmxzwKpy4t9NhB4Xpy7\n0UXhzIe1wOvvZIB1efKdbKqz95VNgWCMMREuHLtujDHGtIEFvTHGRDgLemOMiXAW9MYYE+Es6I0x\nJsJZ0BtjTISzoDfGmAj3/wBbULM9yOHaeQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CyC8vdWwjMys+Te8F6FYZqbQaNaBr19A7Z/FiePPNcIY/cWI4w2/SJCy//DJs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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}