Quora_Question_Classification.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/markvasin/Kaggle-Quora-Insincere-Questions-Classification/blob/master/Quora_Question_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UD3UdQmS4GeR",
        "colab_type": "text"
      },
      "source": [
        "#Quora Insincere Questions Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcudHsZIHDd2",
        "colab_type": "text"
      },
      "source": [
        "https://www.kaggle.com/c/quora-insincere-questions-classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrHfXRVKiJoy",
        "colab_type": "text"
      },
      "source": [
        "###Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCHyqRTVUyrK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import re\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.base import BaseEstimator, ClassifierMixin\n",
        "from sklearn.utils.validation import check_X_y, check_is_fitted\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import LinearSVC\n",
        "from scipy import sparse\n",
        "from scipy.sparse import csr_matrix, hstack\n",
        "from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "from nltk.stem import PorterStemmer, SnowballStemmer, WordNetLemmatizer\n",
        "from nltk.stem.lancaster import LancasterStemmer\n",
        "from nltk.util import ngrams\n",
        "from tqdm.autonotebook import tqdm\n",
        "tqdm.pandas()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKfJwbqKO6M-",
        "colab_type": "code",
        "outputId": "0130c15d-2f63-4391-e297-09cc70024ec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faV_Le5BE6MH",
        "colab_type": "text"
      },
      "source": [
        "###Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZkf_Pdw4SNs",
        "colab_type": "code",
        "outputId": "0d7e2e93-8270-4613-fa95-be6092e43c26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DWHeNf77bBC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_path = '/content/drive/My Drive/Colab Notebooks/Advanced Machine Learning/Data/train.csv'\n",
        "test_path = '/content/drive/My Drive/Colab Notebooks/Advanced Machine Learning/Data/test.csv'\n",
        "\n",
        "train_df = pd.read_csv(train_path)\n",
        "test_df = pd.read_csv(test_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cL6PuwDxE2hc",
        "colab_type": "code",
        "outputId": "31e83d6d-83b8-4e87-c421-2052f413f7e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Train shape : ', train_df.shape)\n",
        "print('Test shape : ', test_df.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape :  (1306122, 3)\n",
            "Test shape :  (375806, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kpiVTjv85gG",
        "colab_type": "code",
        "outputId": "0019a68e-7690-4024-bf49-dc2ab2b7ce0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_df.sample(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>qid</th>\n",
              "      <th>question_text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>718733</th>\n",
              "      <td>8cb41dc361304c6e9e12</td>\n",
              "      <td>How can Quora learn from Bot Detectives, Dovet...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249123</th>\n",
              "      <td>30beacdfa08f290863aa</td>\n",
              "      <td>Why are some people stupid enough to believe t...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123108</th>\n",
              "      <td>1818805a6b5344861959</td>\n",
              "      <td>Why does sebaceous cyst grow on scrotum?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1047013</th>\n",
              "      <td>cd29e51b9942fad5807a</td>\n",
              "      <td>How can I heal a broken heart from dealing wit...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718125</th>\n",
              "      <td>8c95ce37ec1b660d7ad5</td>\n",
              "      <td>What are the ways in which blogs with huge tra...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          qid  ... target\n",
              "718733   8cb41dc361304c6e9e12  ...      0\n",
              "249123   30beacdfa08f290863aa  ...      0\n",
              "123108   1818805a6b5344861959  ...      0\n",
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              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cV1tVoingbTu",
        "colab_type": "text"
      },
      "source": [
        "###Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dtaui82uPuyM",
        "colab_type": "text"
      },
      "source": [
        "* Replacing math equations and url's with common abbrevation.\n",
        "* Cleaning contractions.\n",
        "* Spell Correction.\n",
        "* Removing punctuations.\n",
        "* Removing Stopwords.\n",
        "* Using WordNet Lemmatizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKbCZ0fVNIe7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Replacing math equations and url addresses with tags.\n",
        "# https://www.kaggle.com/canming/ensemble-mean-iii-64-36\n",
        "def clean_tag(x):\n",
        "  if '[math]' in x:\n",
        "    x = re.sub('\\[math\\].*?math\\]', 'MATH EQUATION', x) #replacing with [MATH EQUATION]\n",
        "    \n",
        "  if 'http' in x or 'www' in x:\n",
        "    x = re.sub('(?:(?:https?|ftp):\\/\\/)?[\\w/\\-?=%.]+\\.[\\w/\\-?=%.]+', 'URL', x) #replacing with [url]\n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJfPTT29PYrk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# clean_punct\n",
        "# https://www.kaggle.com/canming/ensemble-mean-iii-64-36\n",
        "\n",
        "puncts = [',', '.', '\"', ':', ')', '(', '-', '!', '?', '|', ';', \"'\", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\\\', \n",
        "        '•', '~', '@', '£', '·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', \n",
        "        '█', '…', '“', '★', '”', '–', '●', '►', '−', '¢', '¬', '░', '¡', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', \n",
        "        '—', '‹', '─', '▒', '：', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', '¯', '♦', '¤', '▲', '¸', '⋅', '‘', '∞', \n",
        "        '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '・', '╦', '╣', '╔', '╗', '▬', '❤', '≤', '‡', '√', '◄', '━', \n",
        "        '⇒', '▶', '≥', '╝', '♡', '◊', '。', '✈', '≡', '☺', '✔', '↵', '≈', '✓', '♣', '☎', '℃', '◦', '└', '‟', '～', '！', '○', \n",
        "        '◆', '№', '♠', '▌', '✿', '▸', '⁄', '□', '❖', '✦', '．', '÷', '｜', '┃', '／', '￥', '╠', '↩', '✭', '▐', '☼', '☻', '┐', \n",
        "        '├', '«', '∼', '┌', '℉', '☮', '฿', '≦', '♬', '✧', '〉', '－', '⌂', '✖', '･', '◕', '※', '‖', '◀', '‰', '\\x97', '↺', \n",
        "        '∆', '┘', '┬', '╬', '،', '⌘', '⊂', '＞', '〈', '⎙', '？', '☠', '⇐', '▫', '∗', '∈', '≠', '♀', '♔', '˚', '℗', '┗', '＊', \n",
        "        '┼', '❀', '＆', '∩', '♂', '‿', '∑', '‣', '➜', '┛', '⇓', '☯', '⊖', '☀', '┳', '；', '∇', '⇑', '✰', '◇', '♯', '☞', '´', \n",
        "        '↔', '┏', '｡', '◘', '∂', '✌', '♭', '┣', '┴', '┓', '✨', '\\xa0', '˜', '❥', '┫', '℠', '✒', '［', '∫', '\\x93', '≧', '］', \n",
        "        '\\x94', '∀', '♛', '\\x96', '∨', '◎', '↻', '⇩', '＜', '≫', '✩', '✪', '♕', '؟', '₤', '☛', '╮', '␊', '＋', '┈', '％', \n",
        "        '╋', '▽', '⇨', '┻', '⊗', '￡', '।', '▂', '✯', '▇', '＿', '➤', '✞', '＝', '▷', '△', '◙', '▅', '✝', '∧', '␉', '☭', \n",
        "        '┊', '╯', '☾', '➔', '∴', '\\x92', '▃', '↳', '＾', '׳', '➢', '╭', '➡', '＠', '⊙', '☢', '˝', '∏', '„', '∥', '❝', '☐', \n",
        "        '▆', '╱', '⋙', '๏', '☁', '⇔', '▔', '\\x91', '➚', '◡', '╰', '\\x85', '♢', '˙', '۞', '✘', '✮', '☑', '⋆', 'ⓘ', '❒', \n",
        "        '☣', '✉', '⌊', '➠', '∣', '❑', '◢', 'ⓒ', '\\x80', '〒', '∕', '▮', '⦿', '✫', '✚', '⋯', '♩', '☂', '❞', '‗', '܂', '☜', \n",
        "        '‾', '✜', '╲', '∘', '⟩', '＼', '⟨', '·', '✗', '♚', '∅', 'ⓔ', '◣', '͡', '‛', '❦', '◠', '✄', '❄', '∃', '␣', '≪', '｢', \n",
        "        '≅', '◯', '☽', '∎', '｣', '❧', '̅', 'ⓐ', '↘', '⚓', '▣', '˘', '∪', '⇢', '✍', '⊥', '＃', '⎯', '↠', '۩', '☰', '◥', \n",
        "        '⊆', '✽', '⚡', '↪', '❁', '☹', '◼', '☃', '◤', '❏', 'ⓢ', '⊱', '➝', '̣', '✡', '∠', '｀', '▴', '┤', '∝', '♏', 'ⓐ', \n",
        "        '✎', ';', '␤', '＇', '❣', '✂', '✤', 'ⓞ', '☪', '✴', '⌒', '˛', '♒', '＄', '✶', '▻', 'ⓔ', '◌', '◈', '❚', '❂', '￦', \n",
        "        '◉', '╜', '̃', '✱', '╖', '❉', 'ⓡ', '↗', 'ⓣ', '♻', '➽', '׀', '✲', '✬', '☉', '▉', '≒', '☥', '⌐', '♨', '✕', 'ⓝ', \n",
        "        '⊰', '❘', '＂', '⇧', '̵', '➪', '▁', '▏', '⊃', 'ⓛ', '‚', '♰', '́', '✏', '⏑', '̶', 'ⓢ', '⩾', '￠', '❍', '≃', '⋰', '♋', \n",
        "        '､', '̂', '❋', '✳', 'ⓤ', '╤', '▕', '⌣', '✸', '℮', '⁺', '▨', '╨', 'ⓥ', '♈', '❃', '☝', '✻', '⊇', '≻', '♘', '♞', \n",
        "        '◂', '✟', '⌠', '✠', '☚', '✥', '❊', 'ⓒ', '⌈', '❅', 'ⓡ', '♧', 'ⓞ', '▭', '❱', 'ⓣ', '∟', '☕', '♺', '∵', '⍝', 'ⓑ', \n",
        "        '✵', '✣', '٭', '♆', 'ⓘ', '∶', '⚜', '◞', '்', '✹', '➥', '↕', '̳', '∷', '✋', '➧', '∋', '̿', 'ͧ', '┅', '⥤', '⬆', '⋱', \n",
        "        '☄', '↖', '⋮', '۔', '♌', 'ⓛ', '╕', '♓', '❯', '♍', '▋', '✺', '⭐', '✾', '♊', '➣', '▿', 'ⓑ', '♉', '⏠', '◾', '▹', \n",
        "        '⩽', '↦', '╥', '⍵', '⌋', '։', '➨', '∮', '⇥', 'ⓗ', 'ⓓ', '⁻', '⎝', '⌥', '⌉', '◔', '◑', '✼', '♎', '♐', '╪', '⊚', \n",
        "        '☒', '⇤', 'ⓜ', '⎠', '◐', '⚠', '╞', '◗', '⎕', 'ⓨ', '☟', 'ⓟ', '♟', '❈', '↬', 'ⓓ', '◻', '♮', '❙', '♤', '∉', '؛', \n",
        "        '⁂', 'ⓝ', '־', '♑', '╫', '╓', '╳', '⬅', '☔', '☸', '┄', '╧', '׃', '⎢', '❆', '⋄', '⚫', '̏', '☏', '➞', '͂', '␙', \n",
        "        'ⓤ', '◟', '̊', '⚐', '✙', '↙', '̾', '℘', '✷', '⍺', '❌', '⊢', '▵', '✅', 'ⓖ', '☨', '▰', '╡', 'ⓜ', '☤', '∽', '╘', \n",
        "        '˹', '↨', '♙', '⬇', '♱', '⌡', '⠀', '╛', '❕', '┉', 'ⓟ', '̀', '♖', 'ⓚ', '┆', '⎜', '◜', '⚾', '⤴', '✇', '╟', '⎛', \n",
        "        '☩', '➲', '➟', 'ⓥ', 'ⓗ', '⏝', '◃', '╢', '↯', '✆', '˃', '⍴', '❇', '⚽', '╒', '̸', '♜', '☓', '➳', '⇄', '☬', '⚑', \n",
        "        '✐', '⌃', '◅', '▢', '❐', '∊', '☈', '॥', '⎮', '▩', 'ு', '⊹', '‵', '␔', '☊', '➸', '̌', '☿', '⇉', '⊳', '╙', 'ⓦ', \n",
        "        '⇣', '｛', '̄', '↝', '⎟', '▍', '❗', '״', '΄', '▞', '◁', '⛄', '⇝', '⎪', '♁', '⇠', '☇', '✊', 'ி', '｝', '⭕', '➘', \n",
        "        '⁀', '☙', '❛', '❓', '⟲', '⇀', '≲', 'ⓕ', '⎥', '\\u06dd', 'ͤ', '₋', '̱', '̎', '♝', '≳', '▙', '➭', '܀', 'ⓖ', '⇛', '▊', \n",
        "        '⇗', '̷', '⇱', '℅', 'ⓧ', '⚛', '̐', '̕', '⇌', '␀', '≌', 'ⓦ', '⊤', '̓', '☦', 'ⓕ', '▜', '➙', 'ⓨ', '⌨', '◮', '☷', \n",
        "        '◍', 'ⓚ', '≔', '⏩', '⍳', '℞', '┋', '˻', '▚', '≺', 'ْ', '▟', '➻', '̪', '⏪', '̉', '⎞', '┇', '⍟', '⇪', '▎', '⇦', '␝', \n",
        "        '⤷', '≖', '⟶', '♗', '̴', '♄', 'ͨ', '̈', '❜', '̡', '▛', '✁', '➩', 'ா', '˂', '↥', '⏎', '⎷', '̲', '➖', '↲', '⩵', '̗', '❢', \n",
        "        '≎', '⚔', '⇇', '̑', '⊿', '̖', '☍', '➹', '⥊', '⁁', '✢']\n",
        "\n",
        "def clean_punct(x):\n",
        "  x = str(x)\n",
        "  for punct in puncts:\n",
        "    if punct in x:\n",
        "      x = x.replace(punct, ' ')\n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LM5un8rkP8ie",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# correct_mispell\n",
        "# https://www.kaggle.com/oysiyl/107-place-solution-using-public-kernel\n",
        "\n",
        "mispell_dict = {'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', \"mastrubating\": 'masturbating', 'pennis': 'penis', 'Etherium': 'bitcoin', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', \"whst\": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization', \n",
        "                'electroneum':'bitcoin','nanodegree':'degree','hotstar':'star','dream11':'dream','ftre':'fire','tensorflow':'framework','unocoin':'bitcoin',\n",
        "                'lnmiit':'limit','unacademy':'academy','altcoin':'bitcoin','altcoins':'bitcoin','litecoin':'bitcoin','coinbase':'bitcoin','cryptocurency':'cryptocurrency',\n",
        "                'simpliv':'simple','quoras':'quora','schizoids':'psychopath','remainers':'remainder','twinflame':'soulmate','quorans':'quora','brexit':'demonetized',\n",
        "                'iiest':'institute','dceu':'comics','pessat':'exam','uceed':'college','bhakts':'devotee','boruto':'anime',\n",
        "                'cryptocoin':'bitcoin','blockchains':'blockchain','fiancee':'fiance','redmi':'smartphone','oneplus':'smartphone','qoura':'quora','deepmind':'framework','ryzen':'cpu','whattsapp':'whatsapp',\n",
        "                'undertale':'adventure','zenfone':'smartphone','cryptocurencies':'cryptocurrencies','koinex':'bitcoin','zebpay':'bitcoin','binance':'bitcoin','whtsapp':'whatsapp',\n",
        "                'reactjs':'framework','bittrex':'bitcoin','bitconnect':'bitcoin','bitfinex':'bitcoin','yourquote':'your quote','whyis':'why is','jiophone':'smartphone',\n",
        "                'dogecoin':'bitcoin','onecoin':'bitcoin','poloniex':'bitcoin','7700k':'cpu','angular2':'framework','segwit2x':'bitcoin','hashflare':'bitcoin','940mx':'gpu',\n",
        "                'openai':'framework','hashflare':'bitcoin','1050ti':'gpu','nearbuy':'near buy','freebitco':'bitcoin','antminer':'bitcoin','filecoin':'bitcoin','whatapp':'whatsapp',\n",
        "                'empowr':'empower','1080ti':'gpu','crytocurrency':'cryptocurrency','8700k':'cpu','whatsaap':'whatsapp','g4560':'cpu','payymoney':'pay money',\n",
        "                'fuckboys':'fuck boys','intenship':'internship','zcash':'bitcoin','demonatisation':'demonetization','narcicist':'narcissist','mastuburation':'masturbation',\n",
        "                'trignometric':'trigonometric','cryptocurreny':'cryptocurrency','howdid':'how did','crytocurrencies':'cryptocurrencies','phycopath':'psychopath',\n",
        "                'bytecoin':'bitcoin','possesiveness':'possessiveness','scollege':'college','humanties':'humanities','altacoin':'bitcoin','demonitised':'demonetized',\n",
        "                'brasília':'brazilia','accolite':'accolyte','econimics':'economics','varrier':'warrier','quroa':'quora','statergy':'strategy','langague':'language',\n",
        "                'splatoon':'game','7600k':'cpu','gate2018':'gate 2018','in2018':'in 2018','narcassist':'narcissist','jiocoin':'bitcoin','hnlu':'hulu','7300hq':'cpu',\n",
        "                'weatern':'western','interledger':'blockchain','deplation':'deflation', 'cryptocurrencies':'cryptocurrency', 'bitcoin':'blockchain cryptocurrency',}\n",
        "\n",
        "def correct_mispell(x):\n",
        "  words = x.split()\n",
        "  for i in range(0, len(words)):\n",
        "    if mispell_dict.get(words[i]) is not None:\n",
        "      words[i] = mispell_dict.get(words[i])\n",
        "    elif mispell_dict.get(words[i].lower()) is not None:\n",
        "      words[i] = mispell_dict.get(words[i].lower())\n",
        "        \n",
        "  words = \" \".join(words)\n",
        "  return words"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPz9sCvPQPL8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove stopwords\n",
        "def remove_stopwords(x):\n",
        "  x = [word for word in x.split() if word not in STOPWORDS]\n",
        "  x = ' '.join(x)\n",
        "\n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tN3C4FsQTgL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# clean word contractions\n",
        "## https://www.kaggle.com/theoviel/improve-your-score-with-text-preprocessing-v2 \n",
        "\n",
        "contraction_mapping = {\"We'd\": \"We had\", \"That'd\": \"That had\", \"AREN'T\": \"Are not\", \"HADN'T\": \"Had not\", \"Could've\": \"Could have\", \"LeT's\": \"Let us\", \"How'll\": \"How will\", \"They'll\": \"They will\", \"DOESN'T\": \"Does not\", \"HE'S\": \"He has\", \"O'Clock\": \"Of the clock\", \"Who'll\": \"Who will\", \"What'S\": \"What is\", \"Ain't\": \"Am not\", \"WEREN'T\": \"Were not\", \"Y'all\": \"You all\", \"Y'ALL\": \"You all\", \"Here's\": \"Here is\", \"It'd\": \"It had\", \"Should've\": \"Should have\", \"I'M\": \"I am\", \"ISN'T\": \"Is not\", \"Would've\": \"Would have\", \"He'll\": \"He will\", \"DON'T\": \"Do not\", \"She'd\": \"She had\", \"WOULDN'T\": \"Would not\", \"She'll\": \"She will\", \"IT's\": \"It is\", \"There'd\": \"There had\", \"It'll\": \"It will\", \"You'll\": \"You will\", \"He'd\": \"He had\", \"What'll\": \"What will\", \"Ma'am\": \"Madam\", \"CAN'T\": \"Can not\", \"THAT'S\": \"That is\", \"You've\": \"You have\", \"She's\": \"She is\", \"Weren't\": \"Were not\", \"They've\": \"They have\", \"Couldn't\": \"Could not\", \"When's\": \"When is\", \"Haven't\": \"Have not\", \"We'll\": \"We will\", \"That's\": \"That is\", \"We're\": \"We are\", \"They're\": \"They' are\", \"You'd\": \"You would\", \"How'd\": \"How did\", \"What're\": \"What are\", \"Hasn't\": \"Has not\", \"Wasn't\": \"Was not\", \"Won't\": \"Will not\", \"There's\": \"There is\", \"Didn't\": \"Did not\", \"Doesn't\": \"Does not\", \"You're\": \"You are\", \"He's\": \"He is\", \"SO's\": \"So is\", \"We've\": \"We have\", \"Who's\": \"Who is\", \"Wouldn't\": \"Would not\", \"Why's\": \"Why is\", \"WHO's\": \"Who is\", \"Let's\": \"Let us\", \"How's\": \"How is\", \"Can't\": \"Can not\", \"Where's\": \"Where is\", \"They'd\": \"They had\", \"Don't\": \"Do not\", \"Shouldn't\":\"Should not\", \"Aren't\":\"Are not\", \"ain't\": \"is not\", \"What's\": \"What is\", \"It's\": \"It is\", \"Isn't\":\"Is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",  \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\", \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\", \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\", \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",  \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\", \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\" }\n",
        "\n",
        "def clean_contractions(text):\n",
        "    specials = [\"’\", \"‘\", \"´\", \"`\"]\n",
        "    for s in specials:\n",
        "        text = text.replace(s, \"'\")\n",
        "    \n",
        "    text = ' '.join([contraction_mapping[t] if t in contraction_mapping else t for t in text.split(\" \")])\n",
        "    return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDO0Ld9RQYP7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# word lemmatizing\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "def lemma_text(x):\n",
        "  x = x.split()\n",
        "  x = [lemmatizer.lemmatize(word) for word in x]\n",
        "  x = ' '.join(x)\n",
        "\n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aa7HEpw5QnW5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def data_cleaning(x):\n",
        "  x = clean_tag(x)\n",
        "  x = clean_punct(x)\n",
        "  x = correct_mispell(x)\n",
        "  x = remove_stopwords(x)\n",
        "  x = clean_contractions(x)\n",
        "  x = lemma_text(x)\n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6dAtOW7Tw8a",
        "colab_type": "code",
        "outputId": "eb8bc706-0d47-4cc9-a336-b23c04fd9606",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "referenced_widgets": [
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            "99051a81f0de4f948f362966ad790bdd",
            "0c3c13b3f50b4adabdc49b11730602c1",
            "8d860ebd2af0431ebb32f843ff891d18",
            "18447d51a7dc4a869a2e9dea5df94e8f",
            "6201e18cc76c4a09af049ded50849d5d",
            "bb0dd9cfc89e45439cc53844a5915a8c",
            "5d1b106b0953492b970083a19bde1278",
            "06e9d5fa1caa4629893d1afe5ba2a48f",
            "dd42f2fdc94e42968fab37f7bdf7ff8b",
            "64f253336f4f4ed8bc8269e71a278d7e",
            "5c649c277e914071840ac4fa4a70058b",
            "cb5b58d39a8f463abdbf52126d6b5b24",
            "292bfbbe92a24f85a07aa9bf335463fc"
          ]
        }
      },
      "source": [
        "train_df['preprocessed_question_text'] = train_df['question_text'].progress_map(lambda x: data_cleaning(x))\n",
        "test_df['preprocessed_question_text'] = test_df['question_text'].progress_map(lambda x: data_cleaning(x))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "adf0b087d33243a8b867883b38dff5b7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1306122.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb0dd9cfc89e45439cc53844a5915a8c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=375806.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjRs1cTJYr3r",
        "colab_type": "code",
        "outputId": "a642ac4c-0f9e-47ad-cc15-ce21cd1e231d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_df.sample(5)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>qid</th>\n",
              "      <th>question_text</th>\n",
              "      <th>target</th>\n",
              "      <th>preprocessed_question_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>487004</th>\n",
              "      <td>5f5f5cec89cf3e7d8074</td>\n",
              "      <td>Why do Tamilian's always in need for water?</td>\n",
              "      <td>1</td>\n",
              "      <td>Why Tamilian's always need water?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>837692</th>\n",
              "      <td>a4293f92cdf9dea520ef</td>\n",
              "      <td>What is use of static members in Java?</td>\n",
              "      <td>0</td>\n",
              "      <td>What use static member Java?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256637</th>\n",
              "      <td>323ca2df021f6cb1673d</td>\n",
              "      <td>Is it illegal to destroy private lock on publi...</td>\n",
              "      <td>0</td>\n",
              "      <td>Is illegal destroy private lock public bicycles?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1171059</th>\n",
              "      <td>e57eac04854dceba7296</td>\n",
              "      <td>What are some of the best ways to become extre...</td>\n",
              "      <td>0</td>\n",
              "      <td>What best way become extremely successful with...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238982</th>\n",
              "      <td>2ebf80944f10541e8d52</td>\n",
              "      <td>How can I score exemption in IPCC auditing?</td>\n",
              "      <td>0</td>\n",
              "      <td>How I score exemption IPCC auditing?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          qid  ...                         preprocessed_question_text\n",
              "487004   5f5f5cec89cf3e7d8074  ...                  Why Tamilian's always need water?\n",
              "837692   a4293f92cdf9dea520ef  ...                       What use static member Java?\n",
              "256637   323ca2df021f6cb1673d  ...   Is illegal destroy private lock public bicycles?\n",
              "1171059  e57eac04854dceba7296  ...  What best way become extremely successful with...\n",
              "238982   2ebf80944f10541e8d52  ...               How I score exemption IPCC auditing?\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBPbFMCmbnH5",
        "colab_type": "text"
      },
      "source": [
        "###Train, Validation and Test Data Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO7av3sZcDm9",
        "colab_type": "code",
        "outputId": "626e7e37-9f7b-40d0-e2a3-957439140392",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "y = train_df['target']\n",
        "X_train, X_test, y_train, y_test = train_test_split(train_df, y, test_size=0.2, random_state=2020)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=2020)\n",
        "\n",
        "# drop the target column in X\n",
        "X_train = X_train.drop(columns=['target'])\n",
        "X_val = X_val.drop(columns=['target'])\n",
        "X_test = X_test.drop(columns=['target'])\n",
        "\n",
        "print('X_train: ', X_train.shape, y_train.shape)\n",
        "print('X_test: ', X_test.shape, y_test.shape)\n",
        "print('X_val: ', X_val.shape, y_val.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train:  (940407, 3) (940407,)\n",
            "X_test:  (261225, 3) (261225,)\n",
            "X_val:  (104490, 3) (104490,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wA8qiuSmokB",
        "colab_type": "text"
      },
      "source": [
        "### Baseline Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUxOOVntOBKR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def threshold_search(true_y, pred_y):\n",
        "  best_score = 0.0\n",
        "  best_threshold = 0.0\n",
        "  for thresh in np.arange(0.15, 0.25, 0.01):\n",
        "      thresh = np.round(thresh, 2)\n",
        "      score = f1_score(true_y, (pred_y > thresh))\n",
        "      if score > best_score:\n",
        "        best_score = score\n",
        "        best_threshold = thresh\n",
        "      print(\"F1 score at threshold {0} is {1}\".format(thresh, score))\n",
        "  \n",
        "  print('Best threshold is', best_threshold)\n",
        "  return best_threshold"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pa_gtBhjaCCG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(y_test, y_pred):\n",
        "  cm = confusion_matrix(y_test, y_pred)\n",
        "  plt.figure(figsize=(5, 3))\n",
        "  sns.heatmap(cm, annot=True, fmt='d', cmap=\"Blues\")\n",
        "  # plt.title('Confusion Matrix')\n",
        "  plt.show()\n",
        "  \n",
        "  print(f\"Correctly classified sincere questions: {round(cm[0][0]/(cm[0][0] + cm[0][1])*100, 2)}%\")\n",
        "  print(f\"Correctly classified insincere questions: {round(cm[1][1]/(cm[1][0] + cm[1][1])*100, 2)}%\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfUhBW5reDy_",
        "colab_type": "text"
      },
      "source": [
        "####Preparing Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eX_sl8LWeJSO",
        "colab_type": "text"
      },
      "source": [
        "Bag of Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOnM4e1CVPaS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_vectorizer = CountVectorizer(stop_words='english', ngram_range=(1,3), min_df=3, max_df=0.9, max_features=50000)\n",
        "tf_vectorizer.fit(pd.concat([train_df['preprocessed_question_text'], test_df['preprocessed_question_text']]))\n",
        "\n",
        "X_train_BOW = tf_vectorizer.transform(X_train['preprocessed_question_text'])\n",
        "X_val_BOW = tf_vectorizer.transform(X_val['preprocessed_question_text'])\n",
        "X_test_BOW = tf_vectorizer.transform(X_test['preprocessed_question_text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe1wBsRxeQzL",
        "colab_type": "text"
      },
      "source": [
        "TF-IDF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLk_EIAhIAeG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfidf_vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,3), min_df=3, max_df=0.9, max_features=50000)\n",
        "tfidf_vectorizer.fit(pd.concat([train_df['preprocessed_question_text'], test_df['preprocessed_question_text']]))\n",
        "\n",
        "X_train_TFIDF = tfidf_vectorizer.transform(X_train['preprocessed_question_text'])\n",
        "X_val_TFIDF = tfidf_vectorizer.transform(X_val['preprocessed_question_text'])\n",
        "X_test_TFIDF = tfidf_vectorizer.transform(X_test['preprocessed_question_text'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjdzWyYOVQR5",
        "colab_type": "text"
      },
      "source": [
        "####Logistic Regression with Bag of Words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3H83aaG5jSiE",
        "colab_type": "text"
      },
      "source": [
        "Using grid search to tune hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbAXYQv5WJOI",
        "colab_type": "code",
        "outputId": "fd62712e-c1d1-4b11-f431-9cdf1cf013fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "params = {'C': [0.001, 0.001, 0.1, 1, 10]}\n",
        "\n",
        "gridsearch = GridSearchCV(LogisticRegression(), params, scoring='f1', n_jobs=-1, verbose=1)\n",
        "gridsearch.fit(X_train_BOW, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:  1.1min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                          fit_intercept=True,\n",
              "                                          intercept_scaling=1, l1_ratio=None,\n",
              "                                          max_iter=100, multi_class='auto',\n",
              "                                          n_jobs=None, penalty='l2',\n",
              "                                          random_state=None, solver='lbfgs',\n",
              "                                          tol=0.0001, verbose=0,\n",
              "                                          warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'C': [0.001, 0.001, 0.1, 1, 10]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='f1', verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wsqPuFRhl2H",
        "colab_type": "code",
        "outputId": "ccf12d55-ef2f-4f01-f50a-8ce8d8d580fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "gridsearch.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mA8sCo3IjaFp",
        "colab_type": "text"
      },
      "source": [
        "Train the model using best hyperparameter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qpd8Xww0ajxf",
        "colab_type": "code",
        "outputId": "e07ea587-9e02-427b-fb40-5acecab3425c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "model = LogisticRegression(**gridsearch.best_params_)\n",
        "model.fit(X_train_BOW, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiWx9YIHjybm",
        "colab_type": "text"
      },
      "source": [
        "Find the best decision threshold using validation set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADg7drO3jx9i",
        "colab_type": "code",
        "outputId": "e024b970-7dd8-4662-dc61-d492875f9370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "pred_prob_val = model.predict_proba(X_val_BOW)[:,1]\n",
        "threshold = threshold_search(y_val, pred_prob_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score at threshold 0.15 is 0.5769713711702662\n",
            "F1 score at threshold 0.16 is 0.5810248864436057\n",
            "F1 score at threshold 0.17 is 0.5835505735140772\n",
            "F1 score at threshold 0.18 is 0.583837712005321\n",
            "F1 score at threshold 0.19 is 0.5841162554917201\n",
            "F1 score at threshold 0.2 is 0.5843683524325808\n",
            "F1 score at threshold 0.21 is 0.5832114245907349\n",
            "F1 score at threshold 0.22 is 0.5827561837455831\n",
            "F1 score at threshold 0.23 is 0.582003006657599\n",
            "F1 score at threshold 0.24 is 0.5827843335987837\n",
            "Best threshold is 0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJXEstm2kYko",
        "colab_type": "text"
      },
      "source": [
        "Make prediction on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQhBYIawjPwl",
        "colab_type": "code",
        "outputId": "a93f551c-4529-4895-fb64-eb1d65a2e102",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "pred_prob_test = model.predict_proba(X_test_BOW)[:,1]\n",
        "pred_test = pred_prob_test > threshold\n",
        "print('F1 score from the test set is', f1_score(y_test, pred_test))\n",
        "plot_confusion_matrix(y_test, pred_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score from the test set is 0.574886369859263\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Correctly classified sincere questions: 96.03%\n",
            "Correctly classified insincere questions: 64.44%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQW7Mwe3d0c4",
        "colab_type": "code",
        "outputId": "e78629b9-29b5-4ad7-ca51-ba8909108c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(classification_report(y_test, pred_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.96      0.97    244934\n",
            "           1       0.52      0.64      0.57     16291\n",
            "\n",
            "    accuracy                           0.94    261225\n",
            "   macro avg       0.75      0.80      0.77    261225\n",
            "weighted avg       0.95      0.94      0.94    261225\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vk9_xNCbny5_",
        "colab_type": "text"
      },
      "source": [
        "####Logistic Regression with TF-IDF features\n",
        "Using grid search to tune hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dd1euv15oKQ7",
        "colab_type": "code",
        "outputId": "5861b381-7a61-498c-d254-fd3ea40585d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "parmas = {'C': [0.001, 0.001, 0.1, 1, 10]}\n",
        "\n",
        "gridsearch = GridSearchCV(LogisticRegression(), parmas, scoring='f1', n_jobs=-1, verbose=1)\n",
        "gridsearch.fit(X_train_TFIDF, y_train)\n",
        "gridsearch.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:   58.4s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7jNYqCHoBfI",
        "colab_type": "code",
        "outputId": "78db00d2-6fe2-4a23-e763-116c335641c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "model = LogisticRegression(**gridsearch.best_params_)\n",
        "model.fit(X_train_TFIDF, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQwcQZA2oQpu",
        "colab_type": "code",
        "outputId": "027ff505-f38d-42fb-bc5f-17a3d9cbdcb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "pred_prob_val = model.predict_proba(X_val_TFIDF)[:,1]\n",
        "threshold = threshold_search(y_val, pred_prob_val)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score at threshold 0.15 is 0.5858609841514809\n",
            "F1 score at threshold 0.16 is 0.5892065804650033\n",
            "F1 score at threshold 0.17 is 0.5914585012087027\n",
            "F1 score at threshold 0.18 is 0.5954565588812251\n",
            "F1 score at threshold 0.19 is 0.598542029546481\n",
            "F1 score at threshold 0.2 is 0.599763406940063\n",
            "F1 score at threshold 0.21 is 0.6016847172081828\n",
            "F1 score at threshold 0.22 is 0.6030798453293534\n",
            "F1 score at threshold 0.23 is 0.604423620202577\n",
            "F1 score at threshold 0.24 is 0.6042350967922286\n",
            "Best threshold is 0.23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfnTovynoWkw",
        "colab_type": "code",
        "outputId": "394844b2-097b-4fd5-f678-4d99400b81df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "pred_prob_test = model.predict_proba(X_test_TFIDF)[:,1]\n",
        "pred_test = pred_prob_test > threshold\n",
        "print('F1 score from the test set is', f1_score(y_test, pred_test))\n",
        "plot_confusion_matrix(y_test, pred_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score from the test set is 0.5922391682065142\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Correctly classified sincere questions: 96.23%\n",
            "Correctly classified insincere questions: 65.91%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlARx3M2ggOr",
        "colab_type": "text"
      },
      "source": [
        "###Deep Learning Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxA1Rhj6_9i9",
        "colab_type": "text"
      },
      "source": [
        "####Word Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqeW9GkGARKa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from os.path import exists\n",
        "if not exists('glove.840B.300d.zip'):\n",
        "  !wget -O glove.840B.300d.zip http://nlp.stanford.edu/data/glove.840B.300d.zip\n",
        "  !unzip glove.840B.300d.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xUzbh1g_7rI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_glove(word_index):\n",
        "    EMBEDDING_FILE = './glove.840B.300d.txt'\n",
        "    def get_coefs(word,*arr): return word, np.asarray(arr, dtype='float32')\n",
        "    embeddings_index = dict(get_coefs(*o.split(\" \")) for o in open(EMBEDDING_FILE))\n",
        "\n",
        "    all_embs = np.stack(embeddings_index.values())\n",
        "    emb_mean,emb_std = all_embs.mean(), all_embs.std()\n",
        "    embed_size = all_embs.shape[1]\n",
        "\n",
        "    # word_index = tokenizer.word_index\n",
        "    nb_words = min(max_features, len(word_index))\n",
        "    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words, embed_size))\n",
        "    for word, i in word_index.items():\n",
        "        if i >= max_features: continue\n",
        "        embedding_vector = embeddings_index.get(word)\n",
        "        if embedding_vector is not None: embedding_matrix[i] = embedding_vector\n",
        "            \n",
        "    return embedding_matrix "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ukOb3IgkiB-",
        "colab_type": "text"
      },
      "source": [
        "####Bidirectional LSTM with Attention Mechanism"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-GPMO3P0awq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from torchtext import data\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "SEED = 1234\n",
        "torch.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "\n",
        "embedding_size = 300\n",
        "max_features = 120000\n",
        "max_length = 72\n",
        "batch_size = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5CifFXNkiog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Attention(nn.Module):\n",
        "    def __init__(self, feature_dim, step_dim, bias=True, **kwargs):\n",
        "        super(Attention, self).__init__(**kwargs)\n",
        "        \n",
        "        self.supports_masking = True\n",
        "\n",
        "        self.bias = bias\n",
        "        self.feature_dim = feature_dim\n",
        "        self.step_dim = step_dim\n",
        "        self.features_dim = 0\n",
        "        \n",
        "        weight = torch.zeros(feature_dim, 1)\n",
        "        nn.init.xavier_uniform_(weight)\n",
        "        self.weight = nn.Parameter(weight)\n",
        "        \n",
        "        if bias:\n",
        "            self.b = nn.Parameter(torch.zeros(step_dim))\n",
        "        \n",
        "    def forward(self, x, mask=None):\n",
        "        feature_dim = self.feature_dim\n",
        "        step_dim = self.step_dim\n",
        "\n",
        "        eij = torch.mm(\n",
        "            x.contiguous().view(-1, feature_dim), \n",
        "            self.weight\n",
        "        ).view(-1, step_dim)\n",
        "        \n",
        "        if self.bias:\n",
        "            eij = eij + self.b\n",
        "            \n",
        "        eij = torch.tanh(eij)\n",
        "        a = torch.exp(eij)\n",
        "        \n",
        "        if mask is not None:\n",
        "            a = a * mask\n",
        "\n",
        "        a = a / torch.sum(a, 1, keepdim=True) + 1e-10\n",
        "\n",
        "        weighted_input = x * torch.unsqueeze(a, -1)\n",
        "        return torch.sum(weighted_input, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtIU3uvF0VWF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class NeuralNet(nn.Module):\n",
        "    def __init__(self, embedding_matrix):\n",
        "        super(NeuralNet, self).__init__()\n",
        "        \n",
        "        hidden_size = 128\n",
        "        \n",
        "        self.embedding = nn.Embedding(max_features, embedding_size)\n",
        "        self.embedding.weight = nn.Parameter(torch.tensor(embedding_matrix, dtype=torch.float32))\n",
        "        self.embedding.weight.requires_grad = False\n",
        "        \n",
        "        self.embedding_dropout = nn.Dropout2d(0.1)\n",
        "        self.lstm = nn.LSTM(embedding_size, hidden_size, bidirectional=True, batch_first=True)\n",
        "        self.gru = nn.GRU(hidden_size*2, hidden_size, bidirectional=True, batch_first=True)\n",
        "        \n",
        "        self.lstm_attention = Attention(hidden_size*2, max_length)\n",
        "        self.gru_attention = Attention(hidden_size*2, max_length)\n",
        "        \n",
        "        self.linear = nn.Linear(1024, 16)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.dropout = nn.Dropout(0.1)\n",
        "        self.out = nn.Linear(16, 1)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        h_embedding = self.embedding(x)\n",
        "        h_embedding = torch.squeeze(self.embedding_dropout(torch.unsqueeze(h_embedding, 0)))\n",
        "        \n",
        "        h_lstm, _ = self.lstm(h_embedding)\n",
        "        h_gru, _ = self.gru(h_lstm)\n",
        "        \n",
        "        h_lstm_atten = self.lstm_attention(h_lstm)\n",
        "        h_gru_atten = self.gru_attention(h_gru)\n",
        "        \n",
        "        avg_pool = torch.mean(h_gru, 1)\n",
        "        max_pool, _ = torch.max(h_gru, 1)\n",
        "        \n",
        "        conc = torch.cat((h_lstm_atten, h_gru_atten, avg_pool, max_pool), 1)\n",
        "        conc = self.relu(self.linear(conc))\n",
        "        conc = self.dropout(conc)\n",
        "        out = self.out(conc)\n",
        "        \n",
        "        return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xG6uWcc71r3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_X = X_train[\"question_text\"].fillna(\"_##_\").values\n",
        "val_X = X_val[\"question_text\"].fillna(\"_##_\").values\n",
        "test_X = X_test[\"question_text\"].fillna(\"_##_\").values\n",
        "\n",
        "\n",
        "tokenizer = Tokenizer(num_words=max_features)\n",
        "tokenizer.fit_on_texts(list(train_X))\n",
        "train_X = tokenizer.texts_to_sequences(train_X)\n",
        "val_X = tokenizer.texts_to_sequences(val_X)\n",
        "test_X = tokenizer.texts_to_sequences(test_X)\n",
        "\n",
        "train_X = pad_sequences(train_X, maxlen=max_length)\n",
        "val_X = pad_sequences(val_X, maxlen=max_length)\n",
        "test_X = pad_sequences(test_X, maxlen=max_length)\n",
        "\n",
        "train_y = y_train.values\n",
        "val_y = y_val.values\n",
        "test_y = y_test.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBba04un9gX7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_matrix = load_glove(tokenizer.word_index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZDnV7WvD6G4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = torch.tensor(train_X, dtype=torch.long).cuda()\n",
        "y_train = torch.tensor(train_y, dtype=torch.float32).unsqueeze(-1).cuda()\n",
        "\n",
        "x_val = torch.tensor(val_X, dtype=torch.long).cuda()\n",
        "y_val = torch.tensor(val_y, dtype=torch.float32).unsqueeze(-1).cuda()\n",
        "\n",
        "train_dataset = TensorDataset(x_train, y_train)\n",
        "val_dataset = TensorDataset(x_val, y_val)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "valid_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjEYtjJrlsmo",
        "colab_type": "code",
        "outputId": "2bf94bb1-fcaa-4351-b292-eb8b24f597fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "model = NeuralNet(embedding_matrix)\n",
        "model.cuda()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NeuralNet(\n",
              "  (embedding): Embedding(120000, 300)\n",
              "  (embedding_dropout): Dropout2d(p=0.1, inplace=False)\n",
              "  (lstm): LSTM(300, 128, batch_first=True, bidirectional=True)\n",
              "  (gru): GRU(256, 128, batch_first=True, bidirectional=True)\n",
              "  (lstm_attention): Attention()\n",
              "  (gru_attention): Attention()\n",
              "  (linear): Linear(in_features=1024, out_features=16, bias=True)\n",
              "  (relu): ReLU()\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (out): Linear(in_features=16, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xABX4g3w7Bat",
        "colab_type": "code",
        "outputId": "98cf6398-61da-401f-d108-65c398a5e926",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507,
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      },
      "source": [
        "train_epochs = 10\n",
        "\n",
        "loss_fn = torch.nn.BCEWithLogitsLoss(reduction=\"sum\")\n",
        "optimizer = torch.optim.Adam(model.parameters())\n",
        "\n",
        "\n",
        "for epoch in range(train_epochs):\n",
        "    model.train()\n",
        "    with tqdm(total=len(train_loader), desc='train') as t:\n",
        "      epoch_loss = 0\n",
        "      for i, (x_batch, y_batch) in enumerate(train_loader):\n",
        "          y_pred = model(x_batch)\n",
        "          loss = loss_fn(y_pred, y_batch)\n",
        "          optimizer.zero_grad()\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          \n",
        "          epoch_loss = (epoch_loss*i + loss.item()) / (i+1)\n",
        "          \n",
        "          t.set_postfix(loss='{:05.3f}'.format(epoch_loss))\n",
        "          t.update()\n",
        "    \n",
        "    model.eval()\n",
        "    with tqdm(total=len(valid_loader), desc='val') as t:\n",
        "      with torch.no_grad():\n",
        "        epoch_loss = 0\n",
        "        for i, (x_batch, y_batch) in enumerate(valid_loader):\n",
        "            y_pred = model(x_batch)\n",
        "            loss = loss_fn(y_pred, y_batch)\n",
        "\n",
        "            epoch_loss = (epoch_loss*i + loss.item()) / (i+1)\n",
        "            \n",
        "            t.set_postfix(loss='{:05.3f}'.format(epoch_loss))\n",
        "            t.update()"
      ],
      "execution_count": 0,
      "outputs": [
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              "HBox(children=(FloatProgress(value=0.0, description='train', max=941.0, style=ProgressStyle(description_width=…"
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='val', max=105.0, style=ProgressStyle(description_width='i…"
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Q30gC3lTOEt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test = torch.tensor(test_X, dtype=torch.long).cuda()\n",
        "y_test = torch.tensor(test_y, dtype=torch.float32).unsqueeze(-1).cuda()\n",
        "\n",
        "test_dataset = TensorDataset(x_test, y_test)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "prediction_list = []\n",
        "\n",
        "with torch.no_grad():\n",
        "  for i, (x_batch, y_batch) in enumerate(test_loader):\n",
        "      y_pred = model(x_batch)\n",
        "      prediction_list.append(torch.nn.functional.sigmoid(y_pred).cpu().numpy()[:, 0].tolist())\n",
        "\n",
        "y_predict = np.array([j for sub in prediction_list for j in sub])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whw6yjJKbnke",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def threshold_search(y_true, y_proba):\n",
        "    best_threshold = 0\n",
        "    best_score = 0\n",
        "    for threshold in tqdm([i * 0.01 for i in range(100)]):\n",
        "        score = f1_score(y_true=y_true, y_pred=y_proba > threshold)\n",
        "        if score > best_score:\n",
        "            best_threshold = threshold\n",
        "            best_score = score\n",
        "    search_result = {'threshold': best_threshold, 'f1': best_score}\n",
        "    return search_result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9iNvQ1OeR8h",
        "colab_type": "code",
        "outputId": "374e72d0-fca2-4e95-eb52-77051fd98e02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
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            "c0690bf2d054436b9d368d3c0689ac8c",
            "33809c67642f49afa63da83aec479e76",
            "bfb24e9604344223b86d2322f56f55c7",
            "217ffdb54af34a388fd1de14467ad070",
            "a09211b2a0c0477e9901c80ef9ac2df1",
            "ac6f3b3346074605a6d9666bb692dad5"
          ]
        }
      },
      "source": [
        "threshold_search(test_y, y_predict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58ac6c2a706148b49ef5992c6637e1da",
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              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'f1': 0.6741396263520157, 'threshold': 0.37}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QeIU5-aLV7JO",
        "colab_type": "text"
      },
      "source": [
        "####Using BERT Transformers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Tr9nK4wrGKa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "try: \n",
        "    from simpletransformers.classification import ClassificationModel\n",
        "except:\n",
        "    !pip install simpletransformers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFqo7HHCcFzf",
        "colab_type": "code",
        "outputId": "bec2a334-b0d0-41c1-dc3c-bcbbb536474d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_data = train_df[['question_text','target']]\n",
        "train_data.columns =['text', 'labels']\n",
        "train_data, test_data = train_test_split(train_data, test_size=0.1, random_state=2020)\n",
        "train_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1037760</th>\n",
              "      <td>Are Scandinavians better looking or in general...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1093087</th>\n",
              "      <td>Can GATE really change one's life from 3 tier ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746003</th>\n",
              "      <td>Does anorexia make scoliosis look good?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1160112</th>\n",
              "      <td>What is the criteria used for an answer to inc...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>602530</th>\n",
              "      <td>Why Muslims hate homosexuals?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      text  labels\n",
              "1037760  Are Scandinavians better looking or in general...       1\n",
              "1093087  Can GATE really change one's life from 3 tier ...       0\n",
              "746003             Does anorexia make scoliosis look good?       0\n",
              "1160112  What is the criteria used for an answer to inc...       0\n",
              "602530                       Why Muslims hate homosexuals?       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ur9V1-2OV6h8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_args = {\n",
        "    \"num_train_epochs\": 1,\n",
        "    \"fp16\": False,\n",
        "    \"overwrite_output_dir\": True\n",
        "}\n",
        "model = ClassificationModel('roberta', 'distilroberta-base', args=model_args)\n",
        "model.train_model(train_data, acc=sklearn.metrics.f1_score)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CX9viOC8xSP8",
        "colab_type": "code",
        "outputId": "05dac578-b0fd-4d30-9c39-d11fa9ef7fd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115,
          "referenced_widgets": [
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            "1415377062e24730a5757658b6ebd343"
          ]
        }
      },
      "source": [
        "result, model_outputs, wrong_predictions = model.eval_model(test_data, acc=sklearn.metrics.f1_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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}