toxic_comments.ipynb

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  "metadata": {
    "colab": {
      "name": "toxic_comments.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMxmqrJNwfsqQZy5puD/uUq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ngcheeyuan/Classification_projects/blob/main/toxic_comments.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "id": "o4ew_OWhX4RD"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds , ds_info = tfds.load('civil_comments' ,\n",
        "                        shuffle_files = True,\n",
        "                        with_info = True)"
      ],
      "metadata": {
        "id": "hSZHWdCMYysr"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "UFApjBUJGzt5"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## prepping dataframe"
      ],
      "metadata": {
        "id": "HgB-eaHdS9OC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_sample = ds['train'].shuffle(buffer_size = 2000).take(500000)\n",
        "val_sample = ds['validation'].shuffle(buffer_size = 2000).take(100000)\n",
        "test_sample = ds['test'].shuffle(buffer_size = 2000).take(100000)"
      ],
      "metadata": {
        "id": "l89YkH2jOZqn"
      },
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.DataFrame(train_sample).applymap(lambda x : x.numpy())\n",
        "df_val = pd.DataFrame(val_sample).applymap(lambda x : x.numpy())\n",
        "df_test = pd.DataFrame(test_sample).applymap(lambda x : x.numpy())"
      ],
      "metadata": {
        "id": "K8T5KcM8Pobc"
      },
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['text'] = df_train['text'].map(lambda x : str(x)[2:-1])\n",
        "df_val['text'] = df_val['text'].map(lambda x : str(x)[2:-1])\n",
        "df_test['text'] = df_test['text'].map(lambda x : str(x)[2:-1])"
      ],
      "metadata": {
        "id": "J6wzmRANIwMN"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#removing duplicates as the sampling was done with replacement\n",
        "df_train = df_train.drop(df_train[df_train.duplicated(keep = 'first')].index)\n",
        "df_val = df_val.drop(df_val[df_val.duplicated(keep = 'first')].index)\n",
        "df_test = df_test.drop(df_test[df_test.duplicated(keep = 'first')].index)"
      ],
      "metadata": {
        "id": "WqD1EAOOTG-a"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "GRuJRShv8wyY",
        "outputId": "6afc0b62-3f08-40cf-8d49-a79152895400"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>identity_attack</th>\n",
              "      <th>insult</th>\n",
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              "      <th>severe_toxicity</th>\n",
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              "      <th>toxicity</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Brian S12--blind much, or total ignorance, or ...</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>They always say they are going to leave - but ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>&gt;&gt;Speaking of strokes of genius ...\\n\\n... and...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Abraham Pineo Gesner was a Canadian physician ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>\"A party of substance\" states Andrew Who.     ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>So killing them is the answer ? And you have n...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>While I applaud PT for looking at ways to incr...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Trump will drain your pocket hon.</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>One never loses siding with solid bishops, of ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Funny, no matter what Obama did, you folks on ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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            ],
            "text/plain": [
              "   identity_attack  insult  ...  threat  toxicity\n",
              "0              0.0     0.3  ...     0.3       0.4\n",
              "1              0.0     0.0  ...     0.0       0.0\n",
              "2              0.0     0.0  ...     0.0       0.0\n",
              "3              0.0     0.0  ...     0.0       0.0\n",
              "4              0.0     0.0  ...     0.0       0.0\n",
              "5              0.0     0.0  ...     1.0       1.0\n",
              "6              0.0     0.0  ...     0.0       0.0\n",
              "7              0.0     0.3  ...     0.0       0.4\n",
              "8              0.0     0.0  ...     0.0       0.0\n",
              "9              0.0     0.0  ...     0.0       0.0\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_2n34k2ajII",
        "outputId": "97de1d1c-eb35-4055-840d-f51b8fcaa6b6"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "identity_attack    0\n",
              "insult             0\n",
              "obscene            0\n",
              "severe_toxicity    0\n",
              "sexual_explicit    0\n",
              "text               0\n",
              "threat             0\n",
              "toxicity           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_val.isna().sum()"
      ],
      "metadata": {
        "id": "zohFdkV3WNTI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e79a1af-1b2b-4937-d1bd-b1b7903e13b9"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "identity_attack    0\n",
              "insult             0\n",
              "obscene            0\n",
              "severe_toxicity    0\n",
              "sexual_explicit    0\n",
              "text               0\n",
              "threat             0\n",
              "toxicity           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing random texts"
      ],
      "metadata": {
        "id": "IyH0uYurizFO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mOYezPzMAas",
        "outputId": "8b867654-3290-4a34-da4d-607f66caf58d"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "identity_attack    0\n",
              "insult             0\n",
              "obscene            0\n",
              "severe_toxicity    0\n",
              "sexual_explicit    0\n",
              "text               0\n",
              "threat             0\n",
              "toxicity           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "random_index = random.randint(0,len(df_train)-9)\n",
        "\n",
        "for row in df_train[random_index:random_index+9].itertuples():\n",
        "  _,attack,insult,obscene,severe_toxic,sexual,text,threat,toxic= row\n",
        "  print(f'text: {str(text)[:]}')\n",
        "  print(f'text: {len(str(text))}')\n",
        "  print(f'attack: {attack}, insults: {insult} obscene: {obscene} severe_toxcity : {severe_toxic}, sexual: {sexual} , threat: {threat}, toxic: {toxic}')\n",
        "  print('-----------------------------------------------------------------------------------------------------------------------------------------------------')"
      ],
      "metadata": {
        "id": "Mavx9-nijQF5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "092c7b3c-ee1e-4d04-d7ed-b132522281a3"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text: I don't care about the tight budget, where the money comes from or whether or not they partner with the city but the state should provide the necessary public access to that park. \\n\\nChugach State Park access has been an issue since at least the early 80s and providing adequate parking is a much greater public need than spending more money on studies for that road to nowhere out of Juneau or funding that KABATA gang  and their bridge to nowhere across Knik Arm. So if anyone wants to know where the money would come from, those are my suggestions.\n",
            "text: 552\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: Maybe one day they will pass voter owned elections and then they won't have fundraisers.\\n\\nAlso noteworthy, not a single fundraiser at a vegan restaurant despite there being 3 within walking distance of the Capitol.  That speaks volumes.\n",
            "text: 238\n",
            "attack: 0.0, insults: 0.0 obscene: 0.1666666716337204 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: Amazing how fast the Globalist Left abandons Human Rights and Equality when they lose an election.\n",
            "text: 98\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: good read, thanks\n",
            "text: 17\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: Microaggressions deserve microresponses.\n",
            "text: 40\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: Much ado about nothing! The big question is, did they pay for that seat?\n",
            "text: 72\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: TRUMP IS THE PROBLEM. THE STOCK MARKET WILL COLLAPSE. OH WAIT....LET ME CHECK ON THAT...OH, ITS AT AN ALL TIME HIGH. NEVER MIND I'M WRONG AGAIN...BYE.\n",
            "text: 150\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.0\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: No, she said PP did not use federal funds for abortion. And they do. They continue to perform the same number annually and they have not had their funds cut. Simple math.\n",
            "text: 170\n",
            "attack: 0.0, insults: 0.0 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.20000000298023224 , threat: 0.0, toxic: 0.20000000298023224\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "text: Right, drive with your brain in gear and not on your iPhone\n",
            "text: 59\n",
            "attack: 0.0, insults: 0.30000001192092896 obscene: 0.0 severe_toxcity : 0.0, sexual: 0.0 , threat: 0.0, toxic: 0.30000001192092896\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install vaderSentiment"
      ],
      "metadata": {
        "id": "2_fbEOZqnzN0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae499b03-a867-47d6-a344-2caaa09fe66c"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.7/dist-packages (3.3.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (1.24.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
      ],
      "metadata": {
        "id": "QOajqoYbjRPE"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "analyzer = SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "id": "NqiO6X9Now8v"
      },
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "vfunc = np.vectorize(analyzer.polarity_scores)"
      ],
      "metadata": {
        "id": "bqR9sSV-oMBD"
      },
      "execution_count": 214,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['text']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0P4H4kbzQpye",
        "outputId": "efa0038e-5872-4fa4-cfc6-0335cf50cb45"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Brian S12--blind much, or total ignorance, or ...\n",
              "1         They always say they are going to leave - but ...\n",
              "2         >>Speaking of strokes of genius ...\\n\\n... and...\n",
              "3         Abraham Pineo Gesner was a Canadian physician ...\n",
              "4         \"A party of substance\" states Andrew Who.     ...\n",
              "                                ...                        \n",
              "499995    Chris,\\n\\n Trump is not doing well.  The Repub...\n",
              "499996    I see the zupta  twitterbots out and about tod...\n",
              "499997    \"... full-fledged member in the community of t...\n",
              "499998    I've noticed he bought a bunch go frontage lot...\n",
              "499999    forgiving ones enemies is primarily a new test...\n",
              "Name: text, Length: 496974, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['sentiment'] = vfunc(df_train['text'])\n",
        "df_val['sentiment'] = vfunc(df_val['text'])\n",
        "df_test['sentiment'] = vfunc(df_test['text'])"
      ],
      "metadata": {
        "id": "UUA1GDPhoWSt"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.reset_index(drop = True , inplace = True)\n",
        "df_val.reset_index(drop = True , inplace = True)\n",
        "df_test.reset_index(drop = True , inplace = True)"
      ],
      "metadata": {
        "id": "ir-vvDe6QQCK"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.concat([df_train.drop(columns = ['sentiment'] , axis = 'columns') ,pd.json_normalize(df_train['sentiment'])],axis =1 )\n",
        "df_val = pd.concat([df_val.drop(columns = ['sentiment'] , axis = 'columns') ,pd.json_normalize(df_val['sentiment'])],axis =1 )\n",
        "df_test = pd.concat([df_test.drop(columns = ['sentiment'] , axis = 'columns') ,pd.json_normalize(df_test['sentiment'])],axis =1 )"
      ],
      "metadata": {
        "id": "dKDyeypLp5um"
      },
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[df_train.isnull().any(axis=1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "dDgGKxbkZNBS",
        "outputId": "bdb9e1db-c9b9-447d-c787-a191aaa21580"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [identity_attack, insult, obscene, severe_toxicity, sexual_explicit, text, threat, toxicity, neg, neu, pos, compound]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row', figsize=(12,7))\n",
        "ax = ax.ravel() \n",
        "\n",
        "for idx,columns in enumerate(['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat']):\n",
        "  ax[idx].hist(df_train.loc[:,columns], bins=12, alpha=0.5)\n",
        "  ax[idx].set_title(f'histogram of {columns} label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "d27lL0uuca9p",
        "outputId": "e278e7ab-9dc4-409c-e154-5b80a98407d2"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x504 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1,1,figsize = (7,7))\n",
        "ax = df_train['toxicity'].hist()\n",
        "ax.set_title(f'target label historgram: toxicity')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "7_AITGr3spT7",
        "outputId": "a769d333-c174-4285-9d36-204c5626823e"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'target label historgram: toxicity')"
            ]
          },
          "metadata": {},
          "execution_count": 221
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/DvDwcPu0bUa1P7ydY0iSNG9zuTv0ee0KkCT7AD8LfJ5BGJ7Suq0Crm3La9tz2vpPVFW19lPb3aMvBpYDnwZuAZa3O0H3ZnDzzNq2zahjSJI0b3OZ7zsYWNPeF3wGcE1VfSzJ3cDVSS4APgtc1vpfBrw/yXpgE4NQo6ruSnINcDewFTirTbOS5GzgOmAP4PKquqvt6/dGHEOSpHmbNQSr6nbgFTO038vgzs7p7d8CXjdiXxcCF87Qvg5YN9djSJI0Dn5ijCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW7OGYJLDktyY5O4kdyX5rdb+tiQbk9zWHicObfP7SdYn+WKSE4baV7a29UnOHWp/cZKbW/uHkuzd2p/Znq9v65eN8+QlSX2by5XgVuCcqjocOBY4K8nhbd27qurI9lgH0NadCrwUWAm8J8keSfYA3g28BjgceMPQfv6k7eslwCPAGa39DOCR1v6u1k+SpLGYNQSr6oGq+tu2/A3g88Ah29nkJODqqnqiqr4ErAeObo/1VXVvVX0buBo4KUmAVwEfbtuvAU4e2teatvxh4LjWX5KkedtzRzq36chXADcDPwmcneR04FYGV4uPMAjIm4Y228A/hub909qPAQ4EHq2qrTP0P2TbNlW1Ncnm1v/r08Z1JnAmwMTEBFNTUztyWjOa2AfOOWLr7B13gXGczzht2bJlyY1pqbA2o1mb0azNaAtdmzmHYJIfBP498NtV9ViSS4HzgWpfLwJ+ZUFGOYuqWg2sBlixYkVNTk7Oe5+XXHUtF92xQz8jLJj7Tptc7CE8xdTUFOOo8e7I2oxmbUazNqMtdG3mdHdokr0YBOBVVfURgKp6sKqerKrvAu9jMN0JsBE4bGjzQ1vbqPaHgf2T7Dmt/Sn7auuf0/pLkjRvc7k7NMBlwOer6k+H2g8e6vYLwJ1teS1waruz88XAcuDTwC3A8nYn6N4Mbp5ZW1UF3Aic0rZfBVw7tK9VbfkU4BOtvyRJ8zaX+b6fBN4I3JHkttb2Bwzu7jySwXTofcCvAVTVXUmuAe5mcGfpWVX1JECSs4HrgD2Ay6vqrra/3wOuTnIB8FkGoUv7+v4k64FNDIJTkqSxmDUEq+pvgJnuyFy3nW0uBC6coX3dTNtV1b3843TqcPu3gNfNNkZJknaGnxgjSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSerWrCGY5LAkNya5O8ldSX6rtT83yfVJ7mlfD2jtSXJxkvVJbk9y1NC+VrX+9yRZNdT+yiR3tG0uTpLtHUOSpHGYy5XgVuCcqjocOBY4K8nhwLnADVW1HLihPQd4DbC8Pc4ELoVBoAHnAccARwPnDYXapcBbhrZb2dpHHUOSpHmbNQSr6oGq+tu2/A3g88AhwEnAmtZtDXByWz4JuLIGbgL2T3IwcAJwfVVtqqpHgOuBlW3dflV1U1UVcOW0fc10DEmS5m3PHemcZBnwCuBmYKKqHmirvgZMtOVDgPuHNtvQ2rbXvmGGdrZzjOnjOpPBVScTExNMTU3tyGnNaGIfOOeIrfPezziM43zGacuWLUtuTEuFtRnN2oxmbUZb6NrMOQST/CDw74HfrqrH2tt2AFRVJakFGN+cjlFVq4HVACtWrKjJycl5H++Sq67lojt26GeEBXPfaZOLPYSnmJqaYhw13h1Zm9GszWjWZrSFrs2c7g5NsheDALyqqj7Smh9sU5m0rw+19o3AYUObH9rattd+6Azt2zuGJEnzNpe7QwNcBny+qv50aNVaYNsdnquAa4faT293iR4LbG5TmtcBxyc5oN0QczxwXVv3WJJj27FOn7avmY4hSdK8zWW+7yeBNwJ3JLmttf0B8E7gmiRnAF8GXt/WrQNOBNYDjwNvBqiqTUnOB25p/d5RVZva8luBK4B9gI+3B9s5hiRJ8zZrCFbV3wAZsfq4GfoXcNaIfV0OXD5D+63Ay2Zof3imY0iSNA5+YowkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuGoCSpW4agJKlbhqAkqVuzhmCSy5M8lOTOoba3JdmY5Lb2OHFo3e8nWZ/ki0lOGGpf2drWJzl3qP3FSW5u7R9Ksndrf2Z7vr6tXzauk5YkCeZ2JXgFsHKG9ndV1ZHtsQ4gyeHAqcBL2zbvSbJHkj2AdwOvAQ4H3tD6AvxJ29dLgEeAM1r7GcAjrf1drZ8kSWMzawhW1SeBTXPc30nA1VX1RFV9CVgPHN0e66vq3qr6NnA1cFKSAK8CPty2XwOcPLSvNW35w8Bxrb8kSWMxn/cEz05ye5suPaC1HQLcP9RnQ2sb1X4g8GhVbZ3W/pR9tfWbW39JksZiz53c7lLgfKDa14uAXxnXoHZUkjOBMwEmJiaYmpqa9z4n9oFzjtg6e8ddYBznM05btmxZcmNaKqzNaNZmNGsz2kLXZqdCsKoe3Lac5H3Ax9rTjcBhQ10PbW2MaH8Y2D/Jnu1qb7j/tn1tSLIn8JzWf6bxrAZWA6xYsaImJyd35rSe4pKrruWiO3b2Z4Txuu+0ycUewlNMTU0xjhrvjqzNaNZmNGsz2kLXZqemQ5McPPT0F4Btd46uBU5td3a+GFgOfBq4BVje7gTdm8HNM2urqoAbgVPa9quAa4f2taotnwJ8ovWXJGksZr3USfJBYBI4KMkG4DxgMsmRDKZD7wN+DaCq7kpyDXA3sBU4q6qebPs5G7gO2AO4vKruaof4PeDqJBcAnwUua+2XAe9Psp7BjTmnzvtsJUkaMmsIVtUbZmi+bIa2bf0vBC6coX0dsG6G9nsZ3D06vf1bwOtmG58kSTvLT4yRJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdcsQlCR1yxCUJHXLEJQkdWvWEExyeZKHktw51PbcJNcnuad9PaC1J8nFSdYnuT3JUUPbrGr970myaqj9lUnuaNtcnCTbO4YkSeMylyvBK4CV09rOBW6oquXADe05wGuA5e1xJnApDAINOA84BjgaOG8o1C4F3jK03cpZjiFJ0ljMGoJV9Ulg07Tmk4A1bXkNcPJQ+5U1cBOwf5KDgROA66tqU1U9AlwPrGzr9quqm6qqgCun7WumY0iSNBZ77uR2E1X1QFv+GjDRlg8B7h/qt6G1ba99wwzt2zvG90lyJoMrTyYmJpiamtrB0/l+E/vAOUdsnfd+xmEc5zNOW7ZsWXJjWiqszWjWZjRrM9pC12ZnQ/B7qqqS1DgGs7PHqKrVwGqAFStW1OTk5LyPeclV13LRHfMuz1jcd9rkYg/hKaamphhHjXdH1mY0azOatRltoWuzs3eHPtimMmlfH2rtG4HDhvod2tq2137oDO3bO4YkSWOxsyG4Fth2h+cq4Nqh9tPbXaLHApvblOZ1wPFJDmg3xBwPXNfWPZbk2HZX6OnT9jXTMSRJGotZ5/uSfBCYBA5KsoHBXZ7vBK5JcgbwZeD1rfs64ERgPfA48GaAqtqU5HzgltbvHVW17WabtzK4A3Uf4OPtwXaOIUnSWMwaglX1hhGrjpuhbwFnjdjP5cDlM7TfCrxshvaHZzqGJEnj4ifGSJK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6ZQhKkrplCEqSumUISpK6Na8QTHJfkjuS3Jbk1tb23CTXJ7mnfT2gtSfJxUnWJ7k9yVFD+1nV+t+TZNVQ+yvb/te3bTOf8UqSNGwcV4I/U1VHVtWK9vxc4IaqWg7c0J4DvAZY3h5nApfCIDSB84BjgKOB87YFZ+vzlqHtVo5hvJIkAQszHXoSsKYtrwFOHmq/sgZuAvZPcjBwAnB9VW2qqkeA64GVbd1+VXVTVRVw5dC+JEmatz3nuX0B/zlJAe+tqtXARFU90NZ/DZhoy4cA9w9tu6G1ba99wwzt3yfJmQyuLpmYmGBqamoepzQwsQ+cc8TWee9nHMZxPuO0ZcuWJTempcLajGZtRrM2oy10beYbgj9VVRuTPB+4PskXhldWVbWAXFAtfFcDrFixoiYnJ+e9z0uuupaL7phvecbjvtMmF3sITzE1NcU4arw7sjajWZvRrM1oC12beU2HVtXG9vUh4KMM3tN7sE1l0r4+1LpvBA4b2vzQ1ra99kNnaJckaSx2OgST7Jvk2duWgeOBO4G1wLY7PFcB17bltcDp7S7RY4HNbdr0OuD4JAe0G2KOB65r6x5Lcmy7K/T0oX1JkjRv85nvmwA+2n5rYU/gA1X1n5LcAlyT5Azgy8DrW/91wInAeuBx4M0AVbUpyfnALa3fO6pqU1t+K3AFsA/w8faQJGksdjoEq+pe4OUztD8MHDdDewFnjdjX5cDlM7TfCrxsZ8e4u1h27l8v9hCe4oqV+y72ECRpLPzEGElStwxBSVK3DEFJUrcMQUlStwxBSVK3DEFJUrcMQUlStwxBSVK3DEFJUrcMQUlStwxBSVK3DEFJUrcMQUlStwxBSVK35vP3BNWpOzZu5k1L5M873ffO1y72ECQ9jXklKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYfoK2ntWVL5IO8t7li5b6LPQRJO8ArQUlSt7wSlMbIPzMlPb14JShJ6pZXgpJ2Ca+StRQZgtJuaqndNHTOEYs9Aun7OR0qSeqWIShJ6pYhKEnqliEoSeqWIShJ6pYhKEnqlr8iIUmLzN+hXDxeCUqSumUISpK65XSopO74aTraZslfCSZZmeSLSdYnOXexxyNJ2n0s6SvBJHsA7wZ+FtgA3JJkbVXdvbgjk6Td01K7Sl7oP1S91K8EjwbWV9W9VfVt4GrgpEUekyRpN5GqWuwxjJTkFGBlVf1qe/5G4JiqOntavzOBM9vTfwp8cQyHPwj4+hj2szuyNqNZm9GszWjWZrRx1eZFVfW86Y1Lejp0rqpqNd8GfWAAAAPWSURBVLB6nPtMcmtVrRjnPncX1mY0azOatRnN2oy20LVZ6tOhG4HDhp4f2tokSZq3pR6CtwDLk7w4yd7AqcDaRR6TJGk3saSnQ6tqa5KzgeuAPYDLq+quXXT4sU6v7maszWjWZjRrM5q1GW1Ba7Okb4yRJGkhLfXpUEmSFowhKEnqVtchONtHsiV5ZpIPtfU3J1m260e5OOZQm/87yd1Jbk9yQ5IXLcY4F8NcP8ovyf+VpJJ0c+v7XGqT5PXt385dST6wq8e4mObw/+qFSW5M8tn2f+vExRjnrpbk8iQPJblzxPokubjV7fYkR43t4FXV5YPBjTZ/B/wwsDfwOeDwaX3eCvxFWz4V+NBij3sJ1eZngB9oy79hbb6v37OBTwI3ASsWe9xLpTbAcuCzwAHt+fMXe9xLrD6rgd9oy4cD9y32uHdRbf534CjgzhHrTwQ+DgQ4Frh5XMfu+UpwLh/JdhKwpi1/GDguSXbhGBfLrLWpqhur6vH29CYGv8PZg7l+lN/5wJ8A39qVg1tkc6nNW4B3V9UjAFX10C4e42KaS30K2K8tPwf46i4c36Kpqk8Cm7bT5STgyhq4Cdg/ycHjOHbPIXgIcP/Q8w2tbcY+VbUV2AwcuEtGt7jmUpthZzD4Ka0Hs9amTdUcVlVL65OIF95c/t38CPAjST6V5KYkK3fZ6BbfXOrzNuCXk2wA1gG/uWuGtuTt6PekOVvSvyeopS/JLwMrgJ9e7LEsBUmeAfwp8KZFHspStSeDKdFJBrMHn0xyRFU9uqijWjreAFxRVRcl+XHg/UleVlXfXeyB7a56vhKcy0eyfa9Pkj0ZTE88vEtGt7jm9HF1SV4N/CHw81X1xC4a22KbrTbPBl4GTCW5j8H7F2s7uTlmLv9uNgBrq+o7VfUl4H8yCMUezKU+ZwDXAFTV/wCexeADpHu3YB+h2XMIzuUj2dYCq9ryKcAnqr1Lu5ubtTZJXgG8l0EA9vS+znZrU1Wbq+qgqlpWVcsYvF/681V16+IMd5eay/+p/8DgKpAkBzGYHr13Vw5yEc2lPl8BjgNI8qMMQvDvd+kol6a1wOntLtFjgc1V9cA4dtztdGiN+Ei2JO8Abq2qtcBlDKYj1jN40/bUxRvxrjPH2vwr4AeBv2r3Cn2lqn5+0Qa9i8yxNl2aY22uA45PcjfwJPC7VdXD7Mpc63MO8L4k/5zBTTJv6uEH7yQfZPDD0UHt/dDzgL0AquovGLw/eiKwHngcePPYjt1BfSVJmlHP06GSpM4ZgpKkbhmCkqRuGYKSpG4ZgpKkbhmCkqRuGYKSpG79/5fHDNrO0nCRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_target_class(df,label,threshold):\n",
        "  '''\n",
        "  Function to convert target class labels to binary, where positive classes will be where target >= threshold\n",
        "\n",
        "  df : dataframe\n",
        "  label : name of the target column, input has to be a string\n",
        "  threshold : a floating number between 0 and 1\n",
        "  '''\n",
        "  temp_df = df.copy()\n",
        "  temp_df[label] = temp_df[label].map(lambda x : 1 if x >= threshold else 0)\n",
        "\n",
        "  return temp_df"
      ],
      "metadata": {
        "id": "Q9K30oiNtExV"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = convert_target_class(df_train , 'toxicity' , 0.5)\n",
        "df_val = convert_target_class(df_val , 'toxicity' , 0.5)\n",
        "df_val = convert_target_class(df_val , 'toxicity' , 0.5)"
      ],
      "metadata": {
        "id": "w2RXc1fGuHGb"
      },
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.toxicity.mean() , df_val.toxicity.mean() , df_test.toxicity.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTQMRVGCXFtk",
        "outputId": "529c3107-38fa-413f-b8d5-b85de2bfad01"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.08035229207161743, 0.07910123018860142, 0.10316588340479929)"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train['text']\n",
        "X_train_indicators = df_train[['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat']]\n",
        "y_train = df_train['toxicity']\n",
        "\n",
        "X_val = df_val['text']\n",
        "X_val_indicators = df_val[['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat']]\n",
        "y_val = df_val['toxicity']\n",
        "\n",
        "X_test = df_test['text']\n",
        "X_val_indicators = df_val[['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat']]\n",
        "y_test = df_test['toxicity']"
      ],
      "metadata": {
        "id": "SAXUpesvXzs1"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train),len(y_train), len(X_val), len(y_val), len(X_test), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAwMVa5IX0bh",
        "outputId": "f3f93a78-e041-4a74-a93e-2db0b7885788"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(496974, 496974, 96977, 96977, 96972, 96972)"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Our function needs a different name to sklearn's plot_confusion_matrix\n",
        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False): \n",
        "  \"\"\"Makes a labelled confusion matrix comparing predictions and ground truth labels.\n",
        "  If classes is passed, confusion matrix will be labelled, if not, integer class values\n",
        "  will be used.\n",
        "  Args:\n",
        "    y_true: Array of truth labels (must be same shape as y_pred).\n",
        "    y_pred: Array of predicted labels (must be same shape as y_true).\n",
        "    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.\n",
        "    figsize: Size of output figure (default=(10, 10)).\n",
        "    text_size: Size of output figure text (default=15).\n",
        "    norm: normalize values or not (default=False).\n",
        "    savefig: save confusion matrix to file (default=False).\n",
        "  \n",
        "  Returns:\n",
        "    A labelled confusion matrix plot comparing y_true and y_pred.\n",
        "  Example usage:\n",
        "    make_confusion_matrix(y_true=test_labels, # ground truth test labels\n",
        "                          y_pred=y_preds, # predicted labels\n",
        "                          classes=class_names, # array of class label names\n",
        "                          figsize=(15, 15),\n",
        "                          text_size=10)\n",
        "  \"\"\"  \n",
        "  # Create the confustion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
        "\n",
        "  # Plot the figure and make it pretty\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Are there a list of classes?\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "  \n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "         xlabel=\"Predicted label\",\n",
        "         ylabel=\"True label\",\n",
        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
        "         yticks=np.arange(n_classes), \n",
        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
        "         yticklabels=labels)\n",
        "  \n",
        "  # Make x-axis labels appear on bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    if norm:\n",
        "      plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "              horizontalalignment=\"center\",\n",
        "              color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "              size=text_size)\n",
        "    else:\n",
        "      plt.text(j, i, f\"{cm[i, j]}\",\n",
        "              horizontalalignment=\"center\",\n",
        "              color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "              size=text_size)\n",
        "\n",
        "  # Save the figure to the current working directory\n",
        "  if savefig:\n",
        "    fig.savefig(\"confusion_matrix.png\")"
      ],
      "metadata": {
        "id": "6QEruRVRxc7T"
      },
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import TextVectorization"
      ],
      "metadata": {
        "id": "4WrzOlK1uY7Z"
      },
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_vec = TextVectorization(max_tokens= 10000,\n",
        "                             standardize = 'lower_and_strip_punctuation',\n",
        "                             split = 'whitespace',\n",
        "                             ngrams = None,\n",
        "                             output_mode = 'int',\n",
        "                             output_sequence_length = None, #pad text to have the same length as the longest text,\n",
        "                             pad_to_max_tokens = True)"
      ],
      "metadata": {
        "id": "ToqJyOGCvbUM"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Average number of tokens in the training tweets\n",
        "round(sum([len(i.split()) for i in X_train])/len(X_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQjX0MrNvfAg",
        "outputId": "56500160-36de-4ef2-8096-425920075ed7"
      },
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#setup text vectorization variables\n",
        "max_vocab_length = 10000 # max num of words to have in our vocab\n",
        "max_length = 51 # max length of our sequence\n",
        "\n",
        "text_vectorizer = TextVectorization(max_tokens = max_vocab_length,\n",
        "                                    output_sequence_length = max_length)"
      ],
      "metadata": {
        "id": "bMqOAGsHvk5h"
      },
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit the text vectorizer to the training text\n",
        "text_vectorizer.adapt(X_train)"
      ],
      "metadata": {
        "id": "ngPmtTmsvuh8"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_sentence = \"Trump is awesome!\"\n",
        "text_vectorizer([sample_sentence])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iu8SqG-2vyFe",
        "outputId": "6c142b49-2a07-439f-e896-2e347c059208"
      },
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 51), dtype=int64, numpy=\n",
              "array([[  59,    7, 3069,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0,    0,    0,    0]])>"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get the unique words in the vocabulary\n",
        "words_in_vocab = text_vectorizer.get_vocabulary()\n",
        "top_5_words = words_in_vocab[:5]\n",
        "bottom_5_words = words_in_vocab[-5:]"
      ],
      "metadata": {
        "id": "zikoWMmWv9-3"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(top_5_words)\n",
        "print(bottom_5_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6MBIasKjOLm",
        "outputId": "0c403acd-de3b-49b5-a5dd-4387c76232ce"
      },
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['', '[UNK]', 'the', 'to', 'and']\n",
            "['assemble', 'advancing', 'abundant', 'absorbed', 'versa']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "embedding = layers.Embedding(input_dim = max_vocab_length,\n",
        "                             output_dim = 128,\n",
        "                             input_length = max_length)\n",
        "embedding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QkATteywIlB",
        "outputId": "211a0ae1-1a98-495c-9fcb-e54cbb1cdec9"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.embeddings.Embedding at 0x7f4c0e2bd890>"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Baseline model"
      ],
      "metadata": {
        "id": "eYiGJg4wwVT5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "metadata": {
        "id": "Z64MFA3Wwc8e"
      },
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0 = Pipeline([('tfidf',TfidfVectorizer()),\n",
        "                     ('clf',MultinomialNB())])"
      ],
      "metadata": {
        "id": "kq_aTI-bwfK0"
      },
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfs7iCvLwgvx",
        "outputId": "e9d72629-c08a-4cd3-edc9-68b40d80e317"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', MultinomialNB())])"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score , f1_score"
      ],
      "metadata": {
        "id": "jG3_tI0rwitH"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.score(X_val , y_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Wio3nKLwnVi",
        "outputId": "e9e41d18-bffc-46e3-c5ec-d12e25e0a42f"
      },
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9217546428534601"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_preds = model_0.predict(X_val)"
      ],
      "metadata": {
        "id": "AlVlwX26wsNj"
      },
      "execution_count": 242,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_val , base_preds ,average = 'weighted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vr0703exwzbw",
        "outputId": "c353a169-f4b6-45d9-9976-7907137a40c6"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8852784061964807"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_val,base_preds, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False)"
      ],
      "metadata": {
        "id": "Zch45Khaw2Gi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "28cde2e2-f62e-437e-a304-c6e2337b0e0d"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v//pqps96hcGf3IiLfn4IKy6/DL+7/O/zPvM339+HfXfbgh+dOYynnpnBkQd+iut/fzSD9v85Tz0zc77xffX4C3li6juF19PTZjXhW5G6vvHjxzFixHC2HLQVb859s9U+l/7xYiKCnXfdjaGXX9bkEUofDBZFFbL/7gO5Z/xTfOfUK+e1vfTKa1x1+jdYf51VePSJ5xj08XXZZZuPsvs3zuTWux8DYPSDT/LwX3/CoXtvy+mX3ATAF7/9e16Y9cq88/x99GOsvnIfjvnyp+cVRf1WWZ5DvrANR/30Mi768x0A3Hr3Yzz81xP5zsE7zzcOgAcnTGX8v5/p0O9A6o4+89nP8bnP7wnAgfvvwwvTp7+rz63//Bc9evRg3EMPWRSpVAA9ehgVlXFNUYX0WqwnL7786nxts16aA7yz8O7jA/rxxptz+ceYCfP6TJvxEg9OmMrg7Taa11ZfELW4/5HJrL5yn3nbG/Vfg549e3DznY/Ma3vjzbncds/E+c4lqbEePRb+V3Vb+khqzD9FFXLRn+9g283W40uf3ZJll16C9dZehROP+hy33PUojzz+LABL9O7FW2+9zdtv53zHvvHmXAasu1rD8w/6+LpMmPT8vO0lei8279j5z/UWa6++Akss3mu+9hvOPYaXx5zJI9f/hP/6+m7+34wkqamcPquQEbeN4/ATLuF3JxzE+ScfDMAd9/2bfY49b16ff09+niWX6M1G663BuIlPA7DE4r3Y8CNrsOzSi5eee4ct1+dzn/443zjx0rpz1SL+LTb6EMP/8dC89i02WpsePXrQd7mleOb52bz48qv86oIbuf3eibzx5lvssd3G/PcRn2Hlvstw7K+ubtfvQJKqzoXW5SyKKmT7gf0584cHcPafbmXk7eNZdcVl+eE39uCK3x7GHkf8D2+/nYz618M8MWU6Z/3oAA4/4Y+89MprnHzMnvRZZgnmvvV2q+dde/UVuPBnX+Wvtz7IH/9y17z2cROf5l/3/puff+cLPPP8bCY/O5Njvrwj/ddeBYC3366d7/5Hp3D/o1PmHXfLXY/y+htzOebLO/LzP4xodapOkqT25vRZhfziu3tz/d8f5EdnDuOfYydw1Y33sN93z+VTn1ifz+3wcQDenPsWBx/3f6yywrI88Ocf88Son7FuvxW59K9389wLL77rnH2XW4phZx3J5Gdm8NUfXviu/YedcAlzXnuDf/3pv5h88y/YY/uNOfuyW3njzbm8MLu82Ln2b/fSq1dPNu7fr91+f0lSbQ1pM17dUYcmRRExGDgD6Amcl5m/6MjPU2MD1lmVK0eMma9twqRpzHn1DT685krz2saMm8RGn/8J/T+0CnPfepsnpkzn6jOO4O4Hn5zv2CWX6MU1Zx5B71492fuY3/Pqa+++VPjxydPZ+sBTWaffivRarCcTJk3jtP/al/senszcua0nTwAtK5pabhMgSVJH67CiKCJ6AmcDuwBTgNERcV1mju+oz1RjTz0zg00/utZ8bQPWXZWlluzNpKdnvKv/hEnTAPjI2iuz46ABfPHb/ztvX8+ePbj0l4fykbVX5tNf/S3Pz3y54Wc/OfUFAFZcfmn23nVzTjzrLw37f2HnTXnzzbd4aMLTbfrdJElt4M0bG+rIpGhLYGJmPg4QEZcDewIWRZ3kvKtu45fH7s0zz8+urSlaYVmOP3x3npw6nRG3jZvX77jDBvPYE88xfdbLbNx/DY47bDBXjhzLzXe9c2n9Gcfvz+7bbcz3fnklK/ZZmhU/tvS8ffc9MmXeFWdHHvgpZsx6hanTZrPe2ivzn1/blXETnubC4r5FAGf8YH+mz3yZseMm8cabbzH4kxtyxP6f4qw/3cKMBlNsUlXMmTOHETcMB+DpqVN56aUXuebqqwAYvPseLLXUUowdM4ZJk55kyuTJAPzzH39n+vTpfOhD67DFwIGdNnapO+nIoqgfMLluewowaMFOEXE4cDgAvZbpwOHo7Mtu5Y25czlsn+34+j6fZPZLr/Kve//Nf//Pdcx57Y15/VbsszS/+s8vsuLySzPl2VmccfFNnH7JzfOda+etNwDgN9/f912fM2CPH/PUM7XkacnFe3HCUZ9l9ZX78PyMl7nihjGccu7w+abFHn3iWb661zZ866BP07tXT/49eTrH/fYazvrTre3/JUjd0PPTpnHQAfP/WWvZfmTCE3xonXX4/Tln8cdLLpq3/6cnnQjAl78yhD9ccGGzhqouLvCBsI1ER63ZiIh9gMGZ+fVi+yvAoMw8uuyYHkutkosP2K9DxiOp3MzRZ3X2EKRK2nbQQMaOHdO0KmWpNdbP9b5+TlM+68GTdxmbmd0qpuzIpGgqUL+AZc2iTZIkdYrue2VYM3TkJfmjgf4RsW5E9AYOAHzkuSRJ6pI6LCnKzLkRcTQwktol+Rdk5riFHCZJkjqQQVG5Dr1PUWYOB4Z35GdIkiS1Bx/zIUlShbimqJyP+ZAkScKkSJKk6vCO1g2ZFEmSJGFRJEmSBDh9JklSZfiYj8ZMiiRJkjApkiSpUgyKypkUSZIkYVIkSVKluKaonEmRJEnqFBGxfERcFRGPRMTDEbF1RKwQEaMiYkLxs2/RNyLizIiYGBEPRMTmdecZUvSfEBFD6tq3iIgHi2POjIVUhBZFkiRVSERzXm10BjAiMzcANgEeBo4DbsrM/sBNxTbA7kD/4nU48Lva7xMrACcAg4AtgRNaCqmiz2F1xw1uNBiLIkmS1HQR0QfYHjgfIDPfyMxZwJ7ARUW3i4C9ivd7AhdnzZ3A8hGxOrAbMCozZ2TmTGAUMLjYt1xm3pmZCVxcd65WuaZIkqSqiKauKVopIsbUbZ+bmefWba8LPA/8X0RsAowFvg2smpnPFH2eBVYt3vcDJtcdP6Voa9Q+pZX2UhZFkiSpI0zPzIEN9i8GbA58KzPviogzeGeqDIDMzIjIjhxkPafPJEmqiNodrbvMmqIpwJTMvKvYvopakfRcMfVF8XNasX8qsFbd8WsWbY3a12ylvZRFkSRJarrMfBaYHBEDiqadgPHAdUDLFWRDgGHF++uAg4ur0LYCZhfTbCOBXSOib7HAeldgZLHvxYjYqrjq7OC6c7XK6TNJkiojutp9ir4FXBoRvYHHgUOoBTZDI+JQYBKwX9F3OLAHMBGYU/QlM2dExMnA6KLfSZk5o3h/JHAhsCRwQ/EqZVEkSZI6RWbeB7S27minVvomcFTJeS4ALmilfQywcVvHY1EkSVKFdK2gqGtxTZEkSRIWRZIkSYDTZ5IkVUoXW2jdpZgUSZIkYVIkSVJ1vLeHtVaOSZEkSRImRZIkVUbtMR9GRWVMiiRJkjApkiSpUkyKypkUSZIkYVIkSVKlGBSVMymSJEnCpEiSpEpxTVE5kyJJkiRMiiRJqg7vaN2QSZEkSRImRZIkVUYQrilqwKRIkiQJiyJJkiTA6TNJkirF2bNyJkWSJEmYFEmSVCk9jIpKmRRJkiRhUiRJUqUYFJUzKZIkScKkSJKkyojwgbCNmBRJkiRhUiRJUqX0MCgqZVIkSZKESZEkSZXimqJyJkWSJEmYFEmSVCkGReVMiiRJkjApkiSpMgIIjIrKmBRJkiRhUSRJkgQ4fSZJUqV488ZyJkWSJEmYFEmSVB0R3ryxAZMiSZIkTIokSaoUg6JyJkWSJEmYFEmSVBkB9DAqKmVSJEmShEmRJEmVYlBUzqRIkiQJkyJJkirF+xSVMymSJEnCpEiSpMqIcE1RIyZFkiRJWBRJklQpPSKa8mqLiHgyIh6MiPsiYkzRtkJEjIqICcXPvkV7RMSZETExIh6IiM3rzjOk6D8hIobUtW9RnH9icWzDgVkUSZKkzvTpzNw0MwcW28cBN2Vmf+CmYhtgd6B/8Toc+B3UiijgBGAQsCVwQkshVfQ5rO64wY0GYlEkSZK6kj2Bi4r3FwF71bVfnDV3AstHxOrAbsCozJyRmTOBUcDgYt9ymXlnZiZwcd25WmVRJElShUSTXsBKETGm7nV4K8NJ4MaIGFu3f9XMfKZ4/yywavG+HzC57tgpRVuj9imttJfy6jNJktQRptdNiZX5ZGZOjYhVgFER8Uj9zszMiMiOG+L8TIokSaqQiGjKqy0yc2rxcxpwLbU1Qc8VU18UP6cV3acCa9UdvmbR1qh9zVbaS1kUSZKkpouIpSNi2Zb3wK7AQ8B1QMsVZEOAYcX764CDi6vQtgJmF9NsI4FdI6JvscB6V2Bkse/FiNiquOrs4LpztcrpM0mSKiKAHl3n5o2rAtcWqdJiwJ8yc0REjAaGRsShwCRgv6L/cGAPYCIwBzgEIDNnRMTJwOii30mZOaN4fyRwIbAkcEPxKmVRJEmSmi4zHwc2aaX9BWCnVtoTOKrkXBcAF7TSPgbYuK1jsiiSJKkq3sN6nypyTZEkSRImRZIkVYpBUTmTIkmSJEyKJEmqFNcUlSstiiLif6jdfrtVmXlMh4xIkiSpEzRKisY0bRSSJKnDdbH7FHU5pUVRZl5Uvx0RS2XmnI4fkiRJUvMtdKF1RGwdEeOBR4rtTSLinA4fmSRJandd6dlnXU1brj47HdgNeAEgM+8Htu/IQUmSJDVbmy7Jz8zJCzS91QFjkSRJ6jRtuSR/ckRsA2RE9AK+DTzcscOSJEkdoXtObDVHW5KiI6g9gK0f8DSwKSUPZJMkSequFpoUZeZ04KAmjEWSJHWgCOjRTRdBN0Nbrj77cET8JSKej4hpETEsIj7cjMFJkiQ1S1umz/4EDAVWB9YArgQu68hBSZKkjhHRnFd31JaiaKnMvCQz5xavPwJLdPTAJEmSmqnRs89WKN7eEBHHAZdTexba/sDwJoxNkiS1s+56Y8VmaLTQeiy1Iqjl2/tG3b4Eju+oQUmSJDVbo2efrdvMgUiSpI5nUFSuLTdvJCI2Bjakbi1RZl7cUYOSJElqtoUWRRFxArADtaJoOLA7cBtgUSRJUjcShPcpaqAtV5/tA+wEPJuZhwCbAH06dFSSJElN1pbps1cz8+2ImBsRywHTgLU6eFySJKm9deN7CDVDW4qiMRGxPPAHalekvQzc0aGjkiRJarK2PPvsyOLt7yNiBLBcZj7QscOSJEkdwfsUlWt088bNG+3LzHvaezAfG7AWI279bXufVpIkaaEaJUW/abAvgR3beSySJKmDteUKq6pqdPPGTzdzIJIkSZ3JglGSJIk23tFakiR1f4ELrRsxKZIkSaJtj/kI4CDgw5l5UkSsDayWmXd3+OgkSVK76mFQVKotSdE5wNbAgcX2S8DZHTYiSZKkTtCWNUWDMnPziLgXIDNnRkTvDh6XJEnqACZF5dqSFL0ZET2p3ZuIiFgZeLtDRyVJktRkbUmKzgSuBVaJiFOAfYAfdeioJElSu4vw6rNG2vLss0sjYiywE7Wr+fbKzIc7fGSSJElN1Jarz9YG5gB/qW/LzKc6cmCSJKn9uaaoXFumz66ntp4ogCWAdYFHgY06cFySJElN1Zbps4/Vb0fE5sCRHTYiSZLUYVxSVO4939E6M+8BBnXAWCRJkjpNW9YUfbduswewOfB0h41IkiR1iAB6GBWVasuaomXr3s+ltsbo6o4ZjiRJUudoWBQVN21cNjOPbdJ4JElSB/JJ8OVKv5uIWCwz3wK2beJ4JEmSOkWjpOhuauuH7ouI64ArgVdadmbmNR08NkmSpKZpy5qiJYAXgB15535FCVgUSZLUzbjOulyjomiV4sqzh3inGGqRHToqSZKkJmtUFPUElmH+YqiFRZEkSd1MRHhJfgONiqJnMvOkpo1EkiSpEzUqiiwlJUn6gDEoKtfodgU7NW0UkiSpciKiZ0TcGxF/LbbXjYi7ImJiRFwREb2L9sWL7YnF/nXqznF80f5oROxW1z64aJsYEce1ZTylRVFmzljUX1KSJHVNPaI5rzb6NvBw3fapwGmZuR4wEzi0aD8UmFm0n1b0IyI2BA4ANgIGA+cUhVZP4Gxgd2BD4MCib+Pvps3DliRJaicRsSbwGeC8Yjuo3f7nqqLLRcBexfs9i22K/TsV/fcELs/M1zPzCWAisGXxmpiZj2fmG8DlRd+G2nKfIkmS9AHQ5AfCrhQRY+q2z83Mc+u2Twe+zzvPWF0RmJWZc4vtKUC/4iO0ee4AABYwSURBVH0/YDJAZs6NiNlF/37AnXXnrD9m8gLtgxY2YIsiSZLUEaZn5sDWdkTEZ4FpmTk2InZo7rDKWRRJklQhXeTqs22Bz0fEHtSenLEccAawfPHs1bnAmsDUov9UYC1gSkQsBvSh9rSNlvYW9ceUtZdyTZEkSWqqzDw+M9fMzHWoLZS+OTMPAm4B9im6DQGGFe+vK7Yp9t+cmVm0H1BcnbYu0J/as1tHA/2Lq9l6F59x3cLGZVIkSVJVvLcrwzrDfwGXR8RPgXuB84v284FLImIiMINakUNmjouIocB4YC5wVGa+BRARRwMjqT2h44LMHLewD7cokiRJnSYzbwVuLd4/Tu3KsQX7vAbsW3L8KcAprbQPB4a/l7FYFEmSVCHhAytKuaZIkiQJiyJJkiTA6TNJkiqjdvPGzh5F12VSJEmShEmRJEmVYlJUzqRIkiQJkyJJkiolushzProikyJJkiRMiiRJqgyvPmvMpEiSJAmTIkmSqiPAJUXlTIokSZIwKZIkqVJ6GBWVMimSJEnCpEiSpMrw6rPGTIokSZIwKZIkqVJcUlTOpEiSJAmLIkmSJMDpM0mSKiTogfNnZUyKJEmSMCmSJKkyAhdaN2JSJEmShEmRJEnVEd68sRGTIkmSJEyKJEmqFB8IW86kSJIkCZMiSZIqw6vPGjMpkiRJwqRIkqRKcU1ROZMiSZIkTIokSaoUg6JyJkWSJEmYFEmSVBmBaUgjfjeSJElYFEmSJAFOn0mSVB0B4UrrUiZFkiRJmBRJklQp5kTlTIokSZIwKZIkqTICH/PRiEmRJEkSJkWSJFWKOVE5kyJJkiRMiiRJqhSXFJUzKZIkScKkSJKkCgnvaN2ASZEkSRImRZIkVUZgGtKI340kSRImRZIkVYprisqZFEmSpKaLiCUi4u6IuD8ixkXET4r2dSPiroiYGBFXRETvon3xYntisX+dunMdX7Q/GhG71bUPLtomRsRxCxuTRZEkSeoMrwM7ZuYmwKbA4IjYCjgVOC0z1wNmAocW/Q8FZhbtpxX9iIgNgQOAjYDBwDkR0TMiegJnA7sDGwIHFn1LWRRJklQh0aTXwmTNy8Vmr+KVwI7AVUX7RcBexfs9i22K/TtFbS5wT+DyzHw9M58AJgJbFq+Jmfl4Zr4BXF70LWVRJEmSOsJKETGm7nX4gh2KROc+YBowCvg3MCsz5xZdpgD9ivf9gMkAxf7ZwIr17QscU9ZeyoXWkiRVRTR1ofX0zBzYqENmvgVsGhHLA9cCGzRlZCVMiiRJUqfKzFnALcDWwPIR0RLarAlMLd5PBdYCKPb3AV6ob1/gmLL2UhZFkiRVRMvNG5vxWuhYIlYuEiIiYklgF+BhasXRPkW3IcCw4v11xTbF/pszM4v2A4qr09YF+gN3A6OB/sXVbL2pLca+rtGYnD6TJEmdYXXgouIqsR7A0Mz8a0SMBy6PiJ8C9wLnF/3PBy6JiInADGpFDpk5LiKGAuOBucBRxbQcEXE0MBLoCVyQmeMaDciiSJKkCukqN2/MzAeAzVppf5zalWMLtr8G7FtyrlOAU1ppHw4Mb+uYnD6TJEnCpEiSpErpGjlR12RSJEmShEmRJEmV0kWWFHVJJkWSJEmYFEmSVBm1+xQZFZUxKaq4L35mF9ZYfvFWX2PuvhOALT+2/rv2bbL+2u/5PP/6599L+xy492ea/rtL3cV1w/7MJzb7OH2WXpwN+q/LGaf9tmH///zed1iyV3Dc949t0gilDwaToor7+W/O5KWXXpyv7Vc/+wkPPXA/m27+ziNrvrDvAXzt8CPnbffq1fs9n+djm2zGX0b9Y74+U6dM5ohDDmLHnXdrl99H+qD51+23c8C+ezPkq1/j57/8NaPvvosf/eC/6NGjB9/69n+8q//D48dz0f+dz3LLLdcJo1V34JqichZFFbf+Bh+db/uNN97ggXvv4fN778Nii73zr8cqq67GFp8Y9L7Os+xyy73rHHfdcRs9evTgc1/YB0nv9vNTTmLrbbbld+eeB8DOu+zKrFmz+PkpJ/GNbx5J797z/w/Kd//jWxz1rW/zp0sv6YzhSt2a02eazy1/G8msWTPZ64v7N+U8f75qKFtvuz2rrb7G+/o86YPqgfvvY6edd5mvbedddmXmzJncdecd87Vfc/VVPProIxz7/eOaOUTpA8OiSPMZds2VrN5vTQZt88n52i+/5EI+tPIyDFh7ZQ47+ACmPDVpkc5T798TH+OhB+5jr332a5exSx9Er732Gr0WSINa0qFHHn54Xturr77Kcd//Hj895RcsvfTSTR2jupNo2j/dkdNnmmfOnDnceMNf+cpXvz7fs3F22+OzbP6JQayxRj8mPPYIvz31FPbaYyduvn0sy/Xp0+bzLGjY1VfSq1cv9vj8Fzrk95E+CD7ykfUYO2b0fG2jR98NwMyZM+a1/erUn7Paaqtz4EFfbur4pA8SiyLNM2rE9cx55RX22mf+Ka+TT33nSpdB23ySgVtuzS7bfYIrLr2Iw448ps3nWdCwa4byqR13pm/fFdrnF5A+gL5++BF866gjuOC8P/CFL+7DmNF38z+n1/5MRo9a2P/kE09w+m9/zYhRt3SZh32q6/JfkXJOn2meYVcPZd0Pf4RNNtuiYb8NNtyIj/Rfnwfvv2+RzzPuwQeY8Ogj73vtkvRBN+SQr3HYN77JMUd/kzVWWYED9t2b437w3wCstupqAPzoh8ex6+DdWX/AAGbNmsWsWbN4++23ef3115k1axaZ2Zm/gtRtdFhRFBEXRMS0iHiooz5D7efF2bO55W8j2bONRUpEtPp/pG09z7BrhrLEkkuy2x6fW6TxSlXRs2dPTj/zLCY/8zyj73mASVOfY8tBWwHM+znh0UcZdu01rL5y33mvKZMn8/tzzmL1lfsyderUzvwV1IW03LyxGa/uqCOnzy4EzgIu7sDPUDu54a/DeP311/lCGxY9PzJ+HBMfe5SDhhy6yOcZdvVQdhn8GZZeZplFHrNUJX379qVv374AnPv7c9hq620YsMEGAJzzv+fxyisvz9f/4IMO4JPbf4rDv/FNVl555aaPV+qOOqwoysx/RMQ6HXV+ta9h1wxlw40/Tv8B899v6G8jh3P10MvYZbc9WHW11Zk44VHO+PUv6LfmWuz/pYPbfJ56Y0ffxeSnJnHiz37V7r+H9EFz15138q9/3cYmm2zKiy++yNArLuNvN47kpltvm9dni4ED33Xc4ksswZprrsX2n9qhiaNVlxeuKWqk0xdaR8ThwOEA/dZaeyG91RFeeGE6t/39Fv7zhye+a98a/dbiheen8ePjj+XF2bPou8KKfHqnXTnuxyex7AJ3zG10nnrDrh7Kcsv1YcddBrfjbyF9MPXq1Yurhl7BKSedSI8ePdj2k9tx899vZ+OPfayzhyZ94ERHLsArkqK/ZubGbem/yWZb5Ihb71h4R0ntqu/SvRfeSVK723bQQMaOHdO07Gb9jTfNs64c1ZTP2m3DVcZm5rtjzC7Mq88kSZLoAtNnkiSpebrr3aaboSMvyb8MuAMYEBFTIuLdlypJkiR1ER159dmBHXVuSZL03gXQw6ColGuKJEmScE2RJEmV4pqiciZFkiRJWBRJkiQBTp9JklQpPuajnEmRJEkSJkWSJFWKC63LmRRJkiRhUiRJUmV488bGTIokSZIwKZIkqULCNUUNmBRJkiRhUiRJUnWE9ylqxKRIkiQJkyJJkirFoKicSZEkSRImRZIkVUbtPkVmRWVMiiRJkjApkiSpUsyJypkUSZIkYVEkSZIEOH0mSVK1OH9WyqRIkiQJkyJJkirFB8KWMymSJEnCpEiSpErx3o3lTIokSZIwKZIkqVIMisqZFEmSJGFRJElStUSTXgsbRsRaEXFLRIyPiHER8e2ifYWIGBURE4qffYv2iIgzI2JiRDwQEZvXnWtI0X9CRAypa98iIh4sjjkzovGKKosiSZLUGeYC38vMDYGtgKMiYkPgOOCmzOwP3FRsA+wO9C9ehwO/g1oRBZwADAK2BE5oKaSKPofVHTe40YAsiiRJqohaiNOcfxYmM5/JzHuK9y8BDwP9gD2Bi4puFwF7Fe/3BC7OmjuB5SNidWA3YFRmzsjMmcAoYHCxb7nMvDMzE7i47lytcqG1JEnqCCtFxJi67XMz89zWOkbEOsBmwF3Aqpn5TLHrWWDV4n0/YHLdYVOKtkbtU1ppL2VRJElSVURT71M0PTMHLqxTRCwDXA38R2a+WL/sJzMzIrIDxzgfp88kSVKniIhe1AqiSzPzmqL5uWLqi+LntKJ9KrBW3eFrFm2N2tdspb2URZEkSRXSRS4+o7gS7Hzg4cz8bd2u64CWK8iGAMPq2g8urkLbCphdTLONBHaNiL7FAutdgZHFvhcjYqvisw6uO1ernD6TJEmdYVvgK8CDEXFf0fYD4BfA0Ig4FJgE7FfsGw7sAUwE5gCHAGTmjIg4GRhd9DspM2cU748ELgSWBG4oXqUsiiRJUtNl5m2Uh0o7tdI/gaNKznUBcEEr7WOAjds6JosiSZKqxOd8lHJNkSRJEiZFkiRVSNturFhVJkWSJEmYFEmSVClNvHljt2NSJEmShEmRJEmV0dYbK1aVSZEkSRImRZIkVYtRUSmTIkmSJEyKJEmqFO9TVM6kSJIkCZMiSZIqxfsUlTMpkiRJwqRIkqRKMSgqZ1IkSZKESZEkSdXhLa0bMimSJEnCokiSJAlw+kySpErx5o3lTIokSZIwKZIkqTICb97YiEmRJEkSJkWSJFWKQVE5kyJJkiRMiiRJqhajolImRZIkSZgUSZJUKd6nqJxJkSRJEiZFkiRVivcpKmdSJEmShEmRJEmVYlBUzqRIkiQJkyJJkqrFqKiUSZEkSRIWRZIkSYDTZ5IkVUbgzRsbMSmSJEnCpEiSpOoIb97YiEmRJEkSJkWSJFWKQVE5kyJJkiRMiiRJqhajolImRZIkSZgUSZJUIeF9ihowKZIkScKkSJKkSvE+ReVMiiRJkjApkiSpMgIvPmvEpEiSJAmTIkmSqsWoqJRJkSRJEhZFkiSpE0TEBRExLSIeqmtbISJGRcSE4mffoj0i4syImBgRD0TE5nXHDCn6T4iIIXXtW0TEg8UxZ0Ys/Lo7iyJJkiokmvRPG1wIDF6g7TjgpszsD9xUbAPsDvQvXocDv4NaEQWcAAwCtgROaCmkij6H1R234Ge9i0WRJElqusz8BzBjgeY9gYuK9xcBe9W1X5w1dwLLR8TqwG7AqMyckZkzgVHA4GLfcpl5Z2YmcHHduUq50FqSpApp4s0bV4qIMXXb52bmuQs5ZtXMfKZ4/yywavG+HzC5rt+Uoq1R+5RW2huyKJIkSR1hemYOXNSDMzMjIttzQAvj9JkkSRUSTXotoueKqS+Kn9OK9qnAWnX91izaGrWv2Up7QxZFkiSpq7gOaLmCbAgwrK794OIqtK2A2cU020hg14joWyyw3hUYWex7MSK2Kq46O7juXKWcPpMkqSqi6zwQNiIuA3agtvZoCrWryH4BDI2IQ4FJwH5F9+HAHsBEYA5wCEBmzoiIk4HRRb+TMrNl8faR1K5wWxK4oXg1ZFEkSZKaLjMPLNm1Uyt9Eziq5DwXABe00j4G2Pi9jMmiSJKkSukiUVEX5JoiSZIkTIokSaqMoOusKeqKTIokSZIwKZIkqVIMisqZFEmSJNHFkqIH7rtn+hrLLz6ps8ehRbISML2zByFVkH/2urcPNfsDXVNUrksVRZm5cmePQYsmIsa8n2fcSFo0/tmT2o/TZ5IkSXSxpEiSJHWscKl1KZMitZdzO3sAUkX5Z09qJyZFaheZ6V/MUifwz57eM4OiUiZFkiRJmBRJklQpBkXlTIr0vkXE4Ih4NCImRsRxnT0eqQoi4oKImBYRD3X2WKQPCosivS8R0RM4G9gd2BA4MCI27NxRSZVwITC4sweh7iWiea/uyKJI79eWwMTMfDwz3wAuB/bs5DFJH3iZ+Q9gRmePQ/ogcU2R3q9+wOS67SnAoE4aiyRpIbxPUTmTIkmSJEyK9P5NBdaq216zaJMkdUUGRaVMivR+jQb6R8S6EdEbOAC4rpPHJEnSe2ZRpPclM+cCRwMjgYeBoZk5rnNHJX3wRcRlwB3AgIiYEhGHdvaY1D1Ek17dkdNnet8yczgwvLPHIVVJZh7Y2WOQPmgsiiRJqpDueg+hZnD6TJIkCYsiSZIkwOkzSZIqJLx5YwMmRZIkSVgUSZ0iIt6KiPsi4qGIuDIilnof57owIvYp3p/X6IG8EbFDRGyzCJ/xZESs1Nb2Bfq8/B4/68SIOPa9jlHSwgU+ELYRiyKpc7yamZtm5sbAG8AR9TsjYpGmtjPz65k5vkGXHYD3XBRJUhVYFEmd75/AekWK88+IuA4YHxE9I+JXETE6Ih6IiG8ARM1ZEfFoRPwNWKXlRBFxa0QMLN4Pjoh7IuL+iLgpItahVnx9p0iptouIlSPi6uIzRkfEtsWxK0bEjRExLiLOow33YouIP0fE2OKYwxfYd1rRflNErFy0fSQiRhTH/DMiNmiPL1OSFpULraVOVCRCuwMjiqbNgY0z84misJidmZ+IiMWB2yPiRmAzYACwIbAqMB64YIHzrgz8Adi+ONcKmTkjIn4PvJyZvy76/Qk4LTNvi4i1qd2Z/KPACcBtmXlSRHwGaMvdkr9WfMaSwOiIuDozXwCWBsZk5nci4sfFuY8GzgWOyMwJETEIOAfYcRG+RklqFxZFUudYMiLuK97/Ezif2rTW3Zn5RNG+K/DxlvVCQB+gP7A9cFlmvgU8HRE3t3L+rYB/tJwrM2eUjGNnYMN4ZwHAchGxTPEZexfHXh8RM9vwOx0TEV8o3q9VjPUF4G3giqL9j8A1xWdsA1xZ99mLt+EzJL1P3XW9TzNYFEmd49XM3LS+oSgOXqlvAr6VmSMX6LdHO46jB7BVZr7WyljaLCJ2oFZgbZ2ZcyLiVmCJku5ZfO6sBb8DSepMrimSuq6RwDcjohdARKwfEUsD/wD2L9YcrQ58upVj7wS2j4h1i2NXKNpfApat63cj8K2WjYhoKVL+AXypaNsd6LuQsfYBZhYF0QbUkqoWPYCWtOtL1KblXgSeiIh9i8+IiNhkIZ8hqR1Ek/7pjiyKpK7rPGrrhe6JiIeA/6WW7l4LTCj2XUztSenzyczngcOpTVXdzzvTV38BvtCy0Bo4BhhYLOQezztXwf2EWlE1jto02lMLGesIYLGIeBj4BbWirMUrwJbF77AjcFLRfhBwaDG+ccCebfhOJKnDRGZ29hgkSVITbLbFwPz77Xc35bP6LNlzbGYObMqHtROTIkmSJFxoLUlSZQRtuOlYhZkUSZIkYVIkSVK1GBWVMimSJEnCokiSJAlw+kySpErprjdWbAaTIkmSJEyKJEmqFB8IW86kSJIkCZMiSZIqxaConEmRJEkSJkWSJFWLUVEpkyJJkiRMiiRJqhTvU1TOpEiSJAmTIkmSKiPwPkWNmBRJkiQBkZmdPQZJktQEETECWKlJHzc9Mwc36bPahUWRJEkSTp9JkiQBFkWSJEmARZEkSRJgUSRJkgRYFEmSJAHw/9yTrzQyyvgcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2 - Logistic Regression"
      ],
      "metadata": {
        "id": "yfVEI9bA0r66"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "y73vdSXC0uB6"
      },
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = LogisticRegression(class_weight = 'balance')"
      ],
      "metadata": {
        "id": "YGGYcVm-0uB-"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.fit(X_train_indicators,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5aa2be7f-dae8-4638-fa8e-0dbe4de17c5f",
        "id": "f9mwZ7G40uCA"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(class_weight='balance')"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.score(X_val_indicators , y_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0f46733-cbc3-4ce9-9e44-759e48490003",
        "id": "RRqo-OLL0uCF"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9845633500726977"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_preds = model_1.predict(X_val_indicators)"
      ],
      "metadata": {
        "id": "M9JMeXXQ0uCG"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_val , base_preds ,average = 'weighted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e36020e-6792-4f99-f76a-9c3ea73208a3",
        "id": "KL2j1KR80uCI"
      },
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9845518325968401"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_val,base_preds, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "49732322-edde-4668-fee9-eb538b4a695e",
        "id": "ez5tIYVs0uCI"
      },
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The model was unable to differentiate  between classes due to the data imbalance"
      ],
      "metadata": {
        "id": "lRpKA7kdx6Qu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2 : Ensemble logistic regression + naive bayes"
      ],
      "metadata": {
        "id": "_YnXDIRWveea"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.base import TransformerMixin, BaseEstimator\n",
        "import numpy as np\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "######################\n",
        "# custom transformer for sklearn pipeline\n",
        "class ColumnExtractor(TransformerMixin, BaseEstimator):\n",
        "    def __init__(self, cols):\n",
        "        self.cols = cols\n",
        "\n",
        "    def transform(self, X):\n",
        "      if len(self.cols) == 1:\n",
        "        return X[self.cols[0]]\n",
        "      else:\n",
        "        return X[self.cols]\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        return self\n",
        "\n",
        "# fit clf1 with df1\n",
        "pipe1 = Pipeline([\n",
        "    ('col_extract', ColumnExtractor( cols= ['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat'] )), # selecting features 0 and 1 (df1) to be used with LR (clf1)\n",
        "    ('clf', LogisticRegression(class_weight = 'balance'))\n",
        "    ])\n",
        "\n",
        "pipe1.fit(df_train, y_train) # sanity check\n",
        "print(f'accuracy - pipe1, logistic regression :{pipe1.score(df_val,y_val)}') # sanity check\n",
        "\n",
        "# fit clf2 with df2\n",
        "pipe2 = Pipeline([\n",
        "    ('col_extract', ColumnExtractor( cols=['text'])), # selecting features 2 and 3 (df2) to be used with SVC (clf2)\n",
        "    ('tfidf',TfidfVectorizer()),\n",
        "    ('clf', MultinomialNB())\n",
        "    ])\n",
        "\n",
        "pipe2.fit(df_train, y_train) # sanity check\n",
        "print(f'accuracy - pipe2, MultinomialNB , with TFidfVectorizer:{pipe2.score(df_val,y_val)}') # sanity check\n",
        "\n",
        "######################\n",
        "# ensemble/voting classifier where clf1 fitted with df1 and clf2 fitted with df2\n",
        "eclf = VotingClassifier(estimators=[('df1-clf1', pipe1), ('df2-clf2', pipe2)], voting='soft', weights= [1.5, 0.5])\n",
        "eclf.fit(df_train, y_train)\n",
        "print(f'accuracy - voting classifier with pipe1 and pipe 2 :{eclf.score(df_val,y_val)}')\n"
      ],
      "metadata": {
        "id": "l8vaflmIvqX6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "586f9cd1-61eb-4efb-bd80-6bc985d5a8cf"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy - pipe1, logistic regression :0.9845633500726977\n",
            "accuracy - pipe2, MultinomialNB , with TFidfVectorizer:0.9217546428534601\n",
            "accuracy - voting classifier with pipe1 and pipe 2 :0.9812017282448415\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_predictions = eclf.predict(df_val)"
      ],
      "metadata": {
        "id": "YLq0-JW_t_5a"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_val,model_2_predictions, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "Jjlcy0FMyEJB",
        "outputId": "9257e049-6f18-4dae-f9f2-3e6ef5ad9153"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 3 : USE encoder + Logistic Regression"
      ],
      "metadata": {
        "id": "zi3Iv_9fxlp3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "tf_hub_embedding_layer = hub.KerasLayer('https://tfhub.dev/google/universal-sentence-encoder/4',\n",
        "                                        trainable = False,\n",
        "                                        name = 'universal_sentence_encoder')"
      ],
      "metadata": {
        "id": "xfttxKzJzlfW"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "B6NAnRti0XSY"
      },
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from imblearn.over_sampling import RandomOverSampler\n",
        "# from imblearn.under_sampling import RandomUnderSampler\n",
        "# from imblearn.pipeline import Pipeline as imblearnpipe\n",
        "\n",
        "\n",
        "# ros = RandomOverSampler(sampling_strategy = {0:457046,1:457046},random_state=42)\n",
        "# rus = RandomUnderSampler(sampling_strategy = {0:100000,1:100000} , random_state=42)\n",
        "\n",
        "# imbpipe = imblearnpipe([('ros',ros)])\n",
        "# X_train, y_train = imbpipe.fit_resample(df_train[['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat','text']], df_train['toxicity'])\n",
        "X_train = df_train[['identity_attack','insult','obscene','severe_toxicity','sexual_explicit','threat','text']]\n",
        "y_train = df_train['toxicity']"
      ],
      "metadata": {
        "id": "inlkJJIzLhUU"
      },
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#1.\n",
        "token_inputs = layers.Input(shape = [], dtype = tf.string , name = 'token_inputs')\n",
        "token_embeddings = tf_hub_embedding_layer(token_inputs)\n",
        "token_outputs = layers.Dense(128, activation = 'relu')(token_embeddings)\n",
        "token_model = tf.keras.Model(inputs = token_inputs,\n",
        "                             outputs = token_outputs,\n",
        "                             name = 'token_model')\n",
        "\n",
        "#2. Building a logistic regression layer\n",
        "numeric_inputs = layers.Input(shape = (6,) , dtype = tf.int32, name = 'numeric_input')\n",
        "x = layers.Dense(2, activation = 'sigmoid')(numeric_inputs)\n",
        "numeric_model = tf.keras.Model(inputs = numeric_inputs,\n",
        "                               outputs = x , \n",
        "                               name = 'logistic_regression layer')\n",
        "\n",
        "#5. Combine token and char embedding into a hybrid embedding\n",
        "\n",
        "combined_embeddings = layers.Concatenate(name = 'token_vader')([token_model.output,\n",
        "                                                                numeric_model.output])\n",
        "\n",
        "\n",
        "z = layers.Dense(129,activation = 'sigmoid')(combined_embeddings)\n",
        "z = layers.Dense(64,activation = 'relu')(z)\n",
        "z = layers.Dropout(0.5)(z)\n",
        "\n",
        "\n",
        "#7. Create output layer\n",
        "output_layer = layers.Dense(2,activation = 'sigmoid' , name = 'output_layer')(z)\n",
        "\n",
        "#8. Combine model\n",
        "\n",
        "model_3 = tf.keras.Model(inputs = [token_model.input,\n",
        "                                   numeric_model.input\n",
        "                                   ],\n",
        "                         outputs = output_layer,\n",
        "                         name = 'token_vader_model')"
      ],
      "metadata": {
        "id": "GT06qqMYz0aY"
      },
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBCzdieQ25ye",
        "outputId": "130882fb-aaba-491f-b9f4-41e86d6f2481"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"token_vader_model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " token_inputs (InputLayer)      [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " universal_sentence_encoder (Ke  (None, 512)         256797824   ['token_inputs[0][0]']           \n",
            " rasLayer)                                                                                        \n",
            "                                                                                                  \n",
            " numeric_input (InputLayer)     [(None, 6)]          0           []                               \n",
            "                                                                                                  \n",
            " dense_78 (Dense)               (None, 128)          65664       ['universal_sentence_encoder[10][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_79 (Dense)               (None, 2)            14          ['numeric_input[0][0]']          \n",
            "                                                                                                  \n",
            " token_vader (Concatenate)      (None, 130)          0           ['dense_78[0][0]',               \n",
            "                                                                  'dense_79[0][0]']               \n",
            "                                                                                                  \n",
            " dense_80 (Dense)               (None, 129)          16899       ['token_vader[0][0]']            \n",
            "                                                                                                  \n",
            " dense_81 (Dense)               (None, 64)           8320        ['dense_80[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_22 (Dropout)           (None, 64)           0           ['dense_81[0][0]']               \n",
            "                                                                                                  \n",
            " output_layer (Dense)           (None, 2)            130         ['dropout_22[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 256,888,851\n",
            "Trainable params: 91,027\n",
            "Non-trainable params: 256,797,824\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model_3, show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "9XJjY64N3bM6",
        "outputId": "40389505-ad95-48bf-82cb-078eba2fc25e"
      },
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder"
      ],
      "metadata": {
        "id": "Jh878YgV3gFj"
      },
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoder = OneHotEncoder(sparse = False)\n",
        "train_labels_one_hot = one_hot_encoder.fit_transform(y_train.to_numpy().reshape(-1,1))\n",
        "val_labels_one_hot = one_hot_encoder.transform(y_val.to_numpy().reshape(-1,1))"
      ],
      "metadata": {
        "id": "6GAM7Q8w4Jpp"
      },
      "execution_count": 330,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = LabelEncoder()\n",
        "train_labels_encoded = label_encoder.fit_transform(y_train.to_numpy())\n",
        "val_labels_encoded = label_encoder.transform(y_val.to_numpy())"
      ],
      "metadata": {
        "id": "SXrcwQBD4n6R"
      },
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentences = X_train.text.tolist()\n",
        "train_numeric = X_train[['identity_attack', 'insult', 'obscene', 'severe_toxicity','sexual_explicit', 'threat']]\n",
        "\n",
        "val_sentences = df_val.text.tolist()\n",
        "val_numeric = df_val[['identity_attack', 'insult', 'obscene', 'severe_toxicity','sexual_explicit', 'threat']]"
      ],
      "metadata": {
        "id": "G_aahZSN5iyZ"
      },
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_token_numeric_data = tf.data.Dataset.from_tensor_slices((train_sentences,\n",
        "                                                             train_numeric\n",
        "                                                             ))\n",
        "\n",
        "train_token_numeric_labels = tf.data.Dataset.from_tensor_slices(train_labels_one_hot)\n",
        "train_token_numeric_dataset = tf.data.Dataset.zip((train_token_numeric_data,\n",
        "                                                   train_token_numeric_labels))\n",
        "\n",
        "train_token_numeric_dataset = train_token_numeric_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "\n",
        "val_token_numeric_data = tf.data.Dataset.from_tensor_slices((val_sentences,\n",
        "                                                             val_numeric\n",
        "                                                             ))\n",
        "\n",
        "val_token_numeric_labels = tf.data.Dataset.from_tensor_slices(val_labels_one_hot)\n",
        "val_token_numeric_dataset = tf.data.Dataset.zip((val_token_numeric_data,\n",
        "                                                 val_token_numeric_labels))\n",
        "\n",
        "val_token_numeric_dataset = val_token_numeric_dataset.batch(32).prefetch(tf.data.AUTOTUNE)\n"
      ],
      "metadata": {
        "id": "N2rj_2LD4vj-"
      },
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_token_numeric_dataset)\n",
        "print(val_token_numeric_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKocD0jC6gj8",
        "outputId": "f4c7688b-cd5a-4a5d-b95e-8272d245d2f3"
      },
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<PrefetchDataset shapes: (((None,), (None, 6)), (None, 2)), types: ((tf.string, tf.float64), tf.float64)>\n",
            "<PrefetchDataset shapes: (((None,), (None, 6)), (None, 2)), types: ((tf.string, tf.float64), tf.float64)>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)/(y_train.sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1LUIdKgENpt",
        "outputId": "5f28eccd-7ca1-405c-957e-b99494676339"
      },
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.445195702802193"
            ]
          },
          "metadata": {},
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "earlystopping = tf.keras.callbacks.EarlyStopping(min_delta = 0.001,monitor = 'val_auc_14', verbose = 1 , patience = 5)"
      ],
      "metadata": {
        "id": "a6QoSV397l-B"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.compile(loss = tf.keras.losses.BinaryCrossentropy(label_smoothing = 0.2),\n",
        "                optimizer = tf.keras.optimizers.Adam(),\n",
        "                metrics = tf.keras.metrics.AUC(curve='PR')\n",
        "                )"
      ],
      "metadata": {
        "id": "2-5DskFg8IzL"
      },
      "execution_count": 337,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classweights = {0:1,\n",
        "                1:len(y_train)/(y_train.sum())}"
      ],
      "metadata": {
        "id": "giL8t9ImEcKP"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3_history = model_3.fit(train_token_numeric_dataset,\n",
        "                              steps_per_epoch = int(len(train_token_numeric_dataset)//32),\n",
        "                              epochs = 20,\n",
        "                              validation_data = val_token_numeric_dataset,\n",
        "                              class_weight = classweights,\n",
        "                              validation_steps = int(len(val_token_numeric_dataset)//32),\n",
        "                              callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath = 'model_checkpoint',\n",
        "                                                                              monitor = 'val_auc_14',\n",
        "                                                                              save_best_only = True,\n",
        "                                                                              save_weights_only = False),\n",
        "                                           earlystopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDu3DENG8Mlh",
        "outputId": "e164823c-dcfc-4138-d5d8-0b0e1f54ea67"
      },
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "484/485 [============================>.] - ETA: 0s - loss: 1.2268 - auc_14: 0.6705INFO:tensorflow:Assets written to: model_checkpoint/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_checkpoint/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "485/485 [==============================] - 26s 50ms/step - loss: 1.2270 - auc_14: 0.6708 - val_loss: 0.5983 - val_auc_14: 0.8070\n",
            "Epoch 2/20\n",
            "485/485 [==============================] - 13s 28ms/step - loss: 1.1324 - auc_14: 0.7872 - val_loss: 0.4963 - val_auc_14: 0.9322\n",
            "Epoch 3/20\n",
            "485/485 [==============================] - ETA: 0s - loss: 1.1237 - auc_14: 0.7977INFO:tensorflow:Assets written to: model_checkpoint/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_checkpoint/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "485/485 [==============================] - 117s 242ms/step - loss: 1.1237 - auc_14: 0.7977 - val_loss: 0.6082 - val_auc_14: 0.7943\n",
            "Epoch 4/20\n",
            "483/485 [============================>.] - ETA: 0s - loss: 1.0966 - auc_14: 0.8139INFO:tensorflow:Assets written to: model_checkpoint/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_checkpoint/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "485/485 [==============================] - 24s 49ms/step - loss: 1.0967 - auc_14: 0.8136 - val_loss: 0.6268 - val_auc_14: 0.7596\n",
            "Epoch 5/20\n",
            "485/485 [==============================] - 13s 27ms/step - loss: 1.0972 - auc_14: 0.8112 - val_loss: 0.5513 - val_auc_14: 0.8714\n",
            "Epoch 6/20\n",
            "485/485 [==============================] - 13s 27ms/step - loss: 1.1421 - auc_14: 0.7897 - val_loss: 0.6205 - val_auc_14: 0.7620\n",
            "Epoch 7/20\n",
            "485/485 [==============================] - 13s 28ms/step - loss: 1.1196 - auc_14: 0.8045 - val_loss: 0.5949 - val_auc_14: 0.8129\n",
            "Epoch 8/20\n",
            "485/485 [==============================] - 13s 27ms/step - loss: 1.0864 - auc_14: 0.8235 - val_loss: 0.5448 - val_auc_14: 0.8815\n",
            "Epoch 9/20\n",
            "485/485 [==============================] - 13s 27ms/step - loss: 1.0787 - auc_14: 0.8257 - val_loss: 0.5556 - val_auc_14: 0.8642\n",
            "Epoch 00009: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3 = tf.keras.models.load_model('model_checkpoint/')"
      ],
      "metadata": {
        "id": "Id7PpthqRitR"
      },
      "execution_count": 340,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.evaluate(val_token_numeric_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1R0hec8Z8YLY",
        "outputId": "0dd9c403-ed8d-47f4-8950-26e16e6f0cf1"
      },
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3031/3031 [==============================] - 69s 22ms/step - loss: 0.6210 - auc_14: 0.7683\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6210083365440369, 0.768345832824707]"
            ]
          },
          "metadata": {},
          "execution_count": 341
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = model_3.predict(val_token_numeric_dataset)"
      ],
      "metadata": {
        "id": "2PxaCfJfAwqR"
      },
      "execution_count": 342,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T01ZZfei-hlb",
        "outputId": "dca02322-7115-4eed-e4fb-68f3964579b9"
      },
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.16717924, 0.8326807 ],\n",
              "       [0.17174152, 0.82807153],\n",
              "       [0.44176853, 0.5627157 ],\n",
              "       ...,\n",
              "       [0.19316919, 0.80720544],\n",
              "       [0.64183915, 0.36466184],\n",
              "       [0.810389  , 0.193633  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.argmax(preds,axis = 1)"
      ],
      "metadata": {
        "id": "aEQ3LduRBXz6"
      },
      "execution_count": 344,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_val,predictions, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "wXUJtXDlAiCY",
        "outputId": "8d8c0703-4c68-44a1-e189-cf46a4f19fa1"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m3_history_df = pd.DataFrame(model_3_history.history)\n",
        "m3_history_df['epoch'] = range(0,len(m3_history_df))"
      ],
      "metadata": {
        "id": "SrIJATDFowaD"
      },
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(m3_history_df.epoch ,m3_history_df.auc_14 , label = 'train_auc')\n",
        "plt.plot(m3_history_df.epoch ,m3_history_df.val_auc_14 , label = 'val_auc')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "o00_d9I0pshL",
        "outputId": "9eb554a7-5a7a-4f2e-bbbe-2bdd46956ff4"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4be48a72d0>"
            ]
          },
          "metadata": {},
          "execution_count": 357
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "g3Z87_ckqHay"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}