DL_NLP_Project_COVID_19_GPT2forSC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/mfliou/nlp_covid/blob/main/DL_NLP_Project_COVID_19_GPT2forSC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tSDDdxquqpqz",
    "outputId": "4d90e7e7-3547-4cb5-93d8-0a9dace13ca3"
   },
   "outputs": [],
   "source": [
    "#!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "R3X1_EJ3qqfH"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, random_split\n",
    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
    "from transformers import set_seed, GPT2Config, GPT2Tokenizer, GPT2ForSequenceClassification\n",
    "# from transformers import BertTokenizer, get_linear_schedule_with_warmup\n",
    "# from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "from transformers import AdamW\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "jRfe5op7qsyO"
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import time\n",
    "import datetime\n",
    "import random, re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DhKpMFWnqvzl",
    "outputId": "e2eb2a31-f2eb-4b96-a8fb-ff0e34236f68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are %d GPU(s) available. 2\n",
      "We will use the GPU: GeForce RTX 2080 Ti\n"
     ]
    }
   ],
   "source": [
    "# Check device \n",
    "# Get the GPU device name if available.\n",
    "if torch.cuda.is_available():    \n",
    "\n",
    "    # Tell PyTorch to use the GPU.    \n",
    "    device = torch.device(\"cuda\")\n",
    "\n",
    "    print('There are %d GPU(s) available. {}'.format(torch.cuda.device_count()))\n",
    "\n",
    "    print('We will use the GPU: {}'.format(torch.cuda.get_device_name(0)))\n",
    "\n",
    "# If we dont have GPU but a CPU, training will take place on CPU instead\n",
    "else:\n",
    "    print('No GPU available, using the CPU instead.')\n",
    "    device = torch.device(\"cpu\")\n",
    "    \n",
    "# Set the seed value all over the place to make this reproducible.\n",
    "seed_val = 42\n",
    "\n",
    "random.seed(seed_val)\n",
    "np.random.seed(seed_val)\n",
    "torch.manual_seed(seed_val)\n",
    "torch.cuda.manual_seed_all(seed_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GPT2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 797
    },
    "id": "KKwDdaNTq0Uq",
    "outputId": "2db2830e-fe64-45bc-cbd2-d010c242e0ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6936 entries, 0 to 6935\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Id      6936 non-null   int64 \n",
      " 1   Text    6936 non-null   object\n",
      " 2   Label   6936 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 162.7+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Id      1000 non-null   int64 \n",
      " 1   Text    1000 non-null   object\n",
      " 2   Label   1000 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 23.6+ KB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Id      2000 non-null   int64 \n",
      " 1   Text    2000 non-null   object\n",
      " 2   Label   2000 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 47.0+ KB\n",
      "None\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Text</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1241490299215634434</td>\n",
       "      <td>Official death toll from #covid19 in the United Kingdom is now GREATER than: Germany + Poland + Switzerland + Austria + Portugal + Greece + Sweden...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1245916400981381130</td>\n",
       "      <td>Dearest Mr. President @USER 1,169 coronavirus deaths in the US in 24 hours (?) Covid19 pandemic is an international crime from China - not a natur...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1241132432402849793</td>\n",
       "      <td>Latest Updates March 20 ⚠️5274 new cases and 38 new deaths in the United States Illinois: Governo Pritzker issues \"stay at home\" order for all res...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1236107253666607104</td>\n",
       "      <td>真把公主不当干部 BREAKING: 21 people on Grand Princess cruise ship docked off the California coast tested positive for coronavirus, including 19 crew memb...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1239673817552879619</td>\n",
       "      <td>OKLAHOMA CITY — The State Department of Education announced Monday the closure of all K-12 public schools statewide until at least April 6 as the ...</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                    Id  \\\n",
       "0  1241490299215634434   \n",
       "1  1245916400981381130   \n",
       "2  1241132432402849793   \n",
       "3  1236107253666607104   \n",
       "4  1239673817552879619   \n",
       "\n",
       "                                                                                                                                                    Text  \\\n",
       "0  Official death toll from #covid19 in the United Kingdom is now GREATER than: Germany + Poland + Switzerland + Austria + Portugal + Greece + Sweden...   \n",
       "1  Dearest Mr. President @USER 1,169 coronavirus deaths in the US in 24 hours (?) Covid19 pandemic is an international crime from China - not a natur...   \n",
       "2  Latest Updates March 20 ⚠️5274 new cases and 38 new deaths in the United States Illinois: Governo Pritzker issues \"stay at home\" order for all res...   \n",
       "3  真把公主不当干部 BREAKING: 21 people on Grand Princess cruise ship docked off the California coast tested positive for coronavirus, including 19 crew memb...   \n",
       "4  OKLAHOMA CITY — The State Department of Education announced Monday the closure of all K-12 public schools statewide until at least April 6 as the ...   \n",
       "\n",
       "   Label  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data\n",
    "train = pd.read_csv('./nlp_covid/data/covid_train.tsv',sep='\\t')\n",
    "train = train.rename(columns={'Label': 'pre-Label'})\n",
    "\n",
    "valid = pd.read_csv('./nlp_covid/data/covid_valid.tsv', sep='\\t', names=['Id', 'Text', 'pre-Label'])\n",
    "\n",
    "test = pd.read_csv('./nlp_covid/data/covid_test.tsv',sep='\\t', names=['Id', 'Text', 'pre-Label'])\n",
    "\n",
    "train['Label'] = train['pre-Label'].apply(lambda x: 1 if x == 'INFORMATIVE' else 0)\n",
    "valid['Label'] = valid['pre-Label'].apply(lambda x: 1 if x == 'INFORMATIVE' else 0)\n",
    "test['Label'] = test['pre-Label'].apply(lambda x: 1 if x == 'INFORMATIVE' else 0)\n",
    "\n",
    "train = train.drop('pre-Label', axis = 1)\n",
    "test = test.drop('pre-Label', axis = 1)\n",
    "valid = valid.drop('pre-Label', axis = 1)\n",
    "\n",
    "pd.set_option('display.max_colwidth', 150)\n",
    "\n",
    "\n",
    "print(train.info())\n",
    "print(valid.info())\n",
    "print(test.info())\n",
    "print()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "xzB8meOsq4Sg",
    "outputId": "dc6f0f4c-f6c0-4a4c-ca2d-7586fb329f74"
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   "outputs": [
    {
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       "      <td>1241728922192142336</td>\n",
       "      <td>For those saying Pakistan isn’t Italy; After 3 weeks of the first confirmed case, Pakistan has a higher #Coronavirus growth rate than Italy. Exper...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Second case DR 🇩🇴 The Canadian woman has not been identified, however they indicated that she is 70 years old and that she was staying with her hu...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1245941302367305728</td>\n",
       "      <td>Kill Chain: the cyber war on America's elections is a MUST SEE documentary for everyone who uses computers. HTTPURL As of 4/3/20 @USER @USER will ...</td>\n",
       "      <td>0</td>\n",
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       "      <td>Town hosts FIRST #Virtual #TownCouncil meeting via @USER This technology was implemented due to #socialdistancing orders and FL EO 20-69 allowing ...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1240543259299987457</td>\n",
       "      <td>Report suggested that the actual number of undiagnosed Coronavirus positive cases in the country may be five 20 times than the official figure at ...</td>\n",
       "      <td>0</td>\n",
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       "                    Id  \\\n",
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       "3  1245913002840391681   \n",
       "4  1240543259299987457   \n",
       "\n",
       "                                                                                                                                                    Text  \\\n",
       "0  For those saying Pakistan isn’t Italy; After 3 weeks of the first confirmed case, Pakistan has a higher #Coronavirus growth rate than Italy. Exper...   \n",
       "1  Second case DR 🇩🇴 The Canadian woman has not been identified, however they indicated that she is 70 years old and that she was staying with her hu...   \n",
       "2  Kill Chain: the cyber war on America's elections is a MUST SEE documentary for everyone who uses computers. HTTPURL As of 4/3/20 @USER @USER will ...   \n",
       "3  Town hosts FIRST #Virtual #TownCouncil meeting via @USER This technology was implemented due to #socialdistancing orders and FL EO 20-69 allowing ...   \n",
       "4  Report suggested that the actual number of undiagnosed Coronavirus positive cases in the country may be five 20 times than the official figure at ...   \n",
       "\n",
       "   Label  \n",
       "0      0  \n",
       "1      1  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "PAqX3-0oq9cD",
    "outputId": "927a97ff-ee72-412b-8b21-15e5fbe0df80"
   },
   "outputs": [
    {
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       "      <td>@USER PA hospitals don’t have the capacity. Latest from our local hospital is they will only test those with symptoms who returned from China, Ita...</td>\n",
       "      <td>0</td>\n",
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       "      <td>Coronavirus outbreak: Intel employee in Bengaluru may be discharged today. Follow LIVE updates here: HTTPURL</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Trump is trying to BS his way through the coronavirus outbreak. ⁦@USER brings together good evidence and comparisons here that suggest Trump is mo...</td>\n",
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       "      <td>@USER How dramatically could we slow down this #Coronavirus from spreading if literally every american wore a mask for 2-3 weeks? I think it would...</td>\n",
       "      <td>0</td>\n",
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       "      <td>An Instagram post from comedienne Amy Schumer showed noisemaking and applause coming from buildings at around 7 pm, which is when shift changes oc...</td>\n",
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       "0  1235770448966754309   \n",
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       "3  1241191765195001857   \n",
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       "\n",
       "                                                                                                                                                    Text  \\\n",
       "0  @USER PA hospitals don’t have the capacity. Latest from our local hospital is they will only test those with symptoms who returned from China, Ita...   \n",
       "1                                           Coronavirus outbreak: Intel employee in Bengaluru may be discharged today. Follow LIVE updates here: HTTPURL   \n",
       "2  Trump is trying to BS his way through the coronavirus outbreak. ⁦@USER brings together good evidence and comparisons here that suggest Trump is mo...   \n",
       "3  @USER How dramatically could we slow down this #Coronavirus from spreading if literally every american wore a mask for 2-3 weeks? I think it would...   \n",
       "4  An Instagram post from comedienne Amy Schumer showed noisemaking and applause coming from buildings at around 7 pm, which is when shift changes oc...   \n",
       "\n",
       "   Label  \n",
       "0      0  \n",
       "1      1  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GvE7vQEtq_0W",
    "outputId": "aaf9674a-d344-4d30-d93d-c5e71e3b05a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training sentences: 6,936\n",
      "\n",
      "Number of validation sentences: 1,000\n",
      "\n",
      "Number of test sentences: 2,000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Report the number of sentences.\n",
    "print('Number of training sentences: {:,}\\n'.format(train.shape[0]))\n",
    "print('Number of validation sentences: {:,}\\n'.format(valid.shape[0]))\n",
    "print('Number of test sentences: {:,}\\n'.format(test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "nk2dyFKDrCce"
   },
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "def remove_url(text):\n",
    "  url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
    "  return url.sub(r'', text)\n",
    "\n",
    "# df[\"text\"] = df[\"text\"].apply(lambda x: remove_url(x))\n",
    "    \n",
    "def remove_emoji(text):\n",
    "  emoji_pattern = re.compile(\"[\"\n",
    "                               u\"\\U0001F600-\\U0001F64F\" #emoticons\n",
    "                               u\"\\U0001F300-\\U0001F5FF\" #symbols&pics\n",
    "                               u\"\\U0001F680-\\U0001F6FF\" #transportation pic\n",
    "                               u\"\\U0001F1E0-\\U0001F1FF\" #flags\n",
    "                               u\"\\U00002702-\\U000027B0\"\n",
    "                               u\"\\U000024C2-\\U0001F251\"    \n",
    "                               \"]+\", flags = re.UNICODE)\n",
    "  return emoji_pattern.sub(r'', text)\n",
    "\n",
    "# df[\"text\"] = df[\"text\"].apply(lambda x: remove_emoji(x))\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    table = str.maketrans('', '', string.punctuation)\n",
    "    return text.translate(table)\n",
    "\n",
    "# df[\"text\"] = df[\"text\"].apply(lambda x: remove_punctuation(x))\n",
    "\n",
    "\n",
    "def clean_text(text):\n",
    "    \n",
    "    text = text.lower() #lowercase\n",
    "    \n",
    "    text = re.sub(r'[!]+','!',text)\n",
    "    text = re.sub(r'[?]+','?',text)\n",
    "    #text = re.sub(r'[.]+','.',text)\n",
    "    #text = re.sub(r\"'\",\"\",text)\n",
    "    #text = re.sub('\\s+', '', text).strip() # Remove and double spaces\n",
    "    #text = re.sub(r'&amp;?',r'and', text) # replace & -> and\n",
    "    #text = re.sub(r\"https?:\\/\\/t.co\\/[A-Za-z0-9]+\", \"\", text) # Remove URLs\n",
    "    remove_url(text)\n",
    "    remove_emoji(text)\n",
    "    #text = re.sub(r'[\"$%\\*\\+,-\\/:;<=>@\\\\^_`{|}~]+','',text) #remove some puncts (except . ! # ? &)\n",
    "    \n",
    "    return text\n"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of uninformative (0) and informative (1) tweets contained in the train dataset:\n",
      "0    3663\n",
      "1    3273\n",
      "Name: Label, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of uninformative (0) and informative (1) tweets contained in the validation dataset:\n",
      "0    528\n",
      "1    472\n",
      "Name: Label, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of uninformative (0) and informative (1) tweets contained in the test dataset:\n",
      "0    1056\n",
      "1     944\n",
      "Name: Label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Plot count of informative and uninformative tweets in the train set\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "terms = ['Uninformative', 'Informative']\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "ax = sns.countplot(x=\"Label\", data=train, label = terms)\n",
    "ax.set_xticklabels(terms)\n",
    "weightage = [len(train[train['Label'] == 0]),len(train[train['Label'] == 1])]\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pie(weightage,labels=terms, autopct=\"%1.1f%%\")\n",
    "plt.suptitle(\"Train Dataset label distribution\")\n",
    "plt.savefig('./images/fig_eda_distribution1.png')\n",
    "plt.show()\n",
    "\n",
    "print('Number of uninformative (0) and informative (1) tweets contained in the train dataset:')\n",
    "print(train['Label'].value_counts())\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "terms = ['Uninformative', 'Informative']\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "ax = sns.countplot(x=\"Label\", data=valid, label = terms)\n",
    "ax.set_xticklabels(terms)\n",
    "weightage = [len(valid[valid['Label'] == 0]),len(valid[valid['Label'] == 1])]\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pie(weightage,labels=terms, autopct=\"%1.1f%%\")\n",
    "plt.suptitle(\"Validation Dataset label distribution\")\n",
    "plt.savefig('./images/fig_eda_distribution2.png')\n",
    "plt.show()\n",
    "\n",
    "print('Number of uninformative (0) and informative (1) tweets contained in the validation dataset:')\n",
    "print(valid['Label'].value_counts())\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "terms = ['Uninformative', 'Informative']\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "ax = sns.countplot(x=\"Label\", data=test, label = terms)\n",
    "ax.set_xticklabels(terms)\n",
    "weightage = [len(test[test['Label'] == 0]),len(test[test['Label'] == 1])]\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.pie(weightage,labels=terms, autopct=\"%1.1f%%\")\n",
    "plt.suptitle(\"Test Dataset label distribution\")\n",
    "plt.savefig('./images/fig_eda_distribution3.png')\n",
    "plt.show()\n",
    "\n",
    "print('Number of uninformative (0) and informative (1) tweets contained in the test dataset:')\n",
    "print(test['Label'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "train.drop(drop_indices, inplace = True)\n",
    "\n",
    "pickle.dump( train, open( \"covid_train.p\", \"wb\" ) )\n",
    "pickle.dump( valid, open( \"covid_valid.p\", \"wb\" ) )\n",
    "pickle.dump( test, open( \"covid_test.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165,
     "referenced_widgets": [
      "c906c2e8df774ca1aa66cdafab8d5d5f",
      "3ea81e73d24c4de9b781ed548b9c3bfd",
      "b748a443fc084f0bb74978c4b3155d63",
      "3e4c57e61d494ecb8e6731faa8190387",
      "cfe5fd2c256543869bc8d3b14023c95f",
      "252b660c9baa4d2cac964189a8d2921e",
      "5e35779cf5894993854f40c935bfbeb2",
      "25d246dd65674eb0b4e16d4edd302522",
      "c25b18ace3d547da8540fb5046d64304",
      "34817f0991164b54b6a682e7e2c97e30",
      "3dcdae3253814a15b2ae1f16b72b9a8a",
      "33eb999d90384a6c8e98e1634e6a7e62",
      "ef6242eae2ed42fe9213f0cba155a152",
      "64d2c384a5e84234b7ed3168c615eb0f",
      "8db344ed5e3f44478f347775c64c9718",
      "c498db9d44244ba6b4ac75a243dba627",
      "9b1a6f30dec743619d5b8bd469f91f9c",
      "e564f0d0a2e840d2b4960b6a52533385",
      "a592cc110db547c9b78cffaae5746e63",
      "bb1f706378054a1a9cd97376df0dd907",
      "813e0cf6b0f34c9aadf09b1408d1666c",
      "98a7bedd7bd54e738165d7d0b7be8777",
      "b011181e36c943198f9b5cfce5f1a8a7",
      "22ef42c388c94bca86b5d5b2080e75e7"
     ]
    },
    "id": "FoWJWAHxrnxM",
    "outputId": "386a7951-7a8d-462a-c78c-6d581014c79f"
   },
   "outputs": [],
   "source": [
    "# Get the lists of sentences and their labels.\n",
    "train_sentences_initial = train.Text.values\n",
    "#train_labels_initial = train.Label.values\n",
    "\n",
    "valid_sentences_initial = valid.Text.values\n",
    "#valid_labels_initial = valid.Label.values\n",
    "\n",
    "test_sentences_initial = test.Text.values\n",
    "#test_labels_initial = test.Label.values\n",
    "\n",
    "# Load the GPT-2 tokenizer.\n",
    "#To feed our text to BERT, it must be split into tokens, and then these tokens must be \n",
    "#mapped to their index in the tokenizer vocabulary.\n",
    "#The tokenization must be performed by the tokenizer included with BERT\n",
    "\n",
    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2', do_lower_case = True)\n",
    "tokenizer.padding_side = \"left\" # Very Important\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "#tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cqGOVC9SrriS",
    "outputId": "330aa066-b9fc-4362-be89-98ff459d43bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Original:  official death toll from #covid19 in the united kingdom is now greater than: germany + poland + switzerland + austria + portugal + greece + sweden + finland + norway + ireland... combined. uk: 67.5 million (233 dead) above group: 185 million (230 dead) httpurl\n",
      "valid Original:  for those saying pakistan isn’t italy; after 3 weeks of the first confirmed case, pakistan has a higher #coronavirus growth rate than italy. experts on the issue say italy was too slow to lockdown. even after lockdown, it took time for it to properly have an impact. #corona\n",
      "test Original:  @user pa hospitals don’t have the capacity. latest from our local hospital is they will only test those with symptoms who returned from china, italy, iran, japan , or south korea in last 14 days or those who have been in contact with someone with confirmed covid-19 in the last 14 days.\n",
      "Tokenized train:  ['official', 'Ġdeath', 'Ġtoll', 'Ġfrom', 'Ġ#', 'c', 'ov', 'id', '19', 'Ġin', 'Ġthe', 'Ġunited', 'Ġkingdom', 'Ġis', 'Ġnow', 'Ġgreater', 'Ġthan', ':', 'Ġg', 'erman', 'y', 'Ġ+', 'Ġpol', 'and', 'Ġ+', 'Ġsw', 'itzerland', 'Ġ+', 'Ġaust', 'ria', 'Ġ+', 'Ġport', 'ugal', 'Ġ+', 'Ġg', 'ree', 'ce', 'Ġ+', 'Ġsw', 'eden', 'Ġ+', 'Ġfin', 'land', 'Ġ+', 'Ġnor', 'way', 'Ġ+', 'Ġire', 'land', '...', 'Ġcombined', '.', 'Ġu', 'k', ':', 'Ġ67', '.', '5', 'Ġmillion', 'Ġ(', '233', 'Ġdead', ')', 'Ġabove', 'Ġgroup', ':', 'Ġ185', 'Ġmillion', 'Ġ(', '230', 'Ġdead', ')', 'Ġhttp', 'url']\n",
      "Tokenized valid:  ['for', 'Ġthose', 'Ġsaying', 'Ġp', 'ak', 'istan', 'Ġisn', 'âĢ', 'Ļ', 't', 'Ġit', 'aly', ';', 'Ġafter', 'Ġ3', 'Ġweeks', 'Ġof', 'Ġthe', 'Ġfirst', 'Ġconfirmed', 'Ġcase', ',', 'Ġp', 'ak', 'istan', 'Ġhas', 'Ġa', 'Ġhigher', 'Ġ#', 'cor', 'on', 'av', 'irus', 'Ġgrowth', 'Ġrate', 'Ġthan', 'Ġit', 'aly', '.', 'Ġexperts', 'Ġon', 'Ġthe', 'Ġissue', 'Ġsay', 'Ġit', 'aly', 'Ġwas', 'Ġtoo', 'Ġslow', 'Ġto', 'Ġlockdown', '.', 'Ġeven', 'Ġafter', 'Ġlockdown', ',', 'Ġit', 'Ġtook', 'Ġtime', 'Ġfor', 'Ġit', 'Ġto', 'Ġproperly', 'Ġhave', 'Ġan', 'Ġimpact', '.', 'Ġ#', 'cor', 'ona']\n",
      "Tokenized test:  ['@', 'user', 'Ġpa', 'Ġhospitals', 'Ġdon', 'âĢ', 'Ļ', 't', 'Ġhave', 'Ġthe', 'Ġcapacity', '.', 'Ġlatest', 'Ġfrom', 'Ġour', 'Ġlocal', 'Ġhospital', 'Ġis', 'Ġthey', 'Ġwill', 'Ġonly', 'Ġtest', 'Ġthose', 'Ġwith', 'Ġsymptoms', 'Ġwho', 'Ġreturned', 'Ġfrom', 'Ġch', 'ina', ',', 'Ġit', 'aly', ',', 'Ġir', 'an', ',', 'Ġj', 'apan', 'Ġ,', 'Ġor', 'Ġsouth', 'Ġk', 'orea', 'Ġin', 'Ġlast', 'Ġ14', 'Ġdays', 'Ġor', 'Ġthose', 'Ġwho', 'Ġhave', 'Ġbeen', 'Ġin', 'Ġcontact', 'Ġwith', 'Ġsomeone', 'Ġwith', 'Ġconfirmed', 'Ġcov', 'id', '-', '19', 'Ġin', 'Ġthe', 'Ġlast', 'Ġ14', 'Ġdays', '.']\n",
      "train Token IDs:  [16841, 1918, 13592, 422, 1303, 66, 709, 312, 1129, 287, 262, 16503, 13239, 318, 783, 3744, 621, 25, 308, 2224, 88, 1343, 755, 392, 1343, 1509, 13947, 1343, 38132, 7496, 1343, 2493, 43778, 1343, 308, 631, 344, 1343, 1509, 31829, 1343, 957, 1044, 1343, 4249, 1014, 1343, 35918, 1044, 986, 5929, 13, 334, 74, 25, 8275, 13, 20, 1510, 357, 25429, 2636, 8, 2029, 1448, 25, 22855, 1510, 357, 19214, 2636, 8, 2638, 6371]\n",
      "valid Token IDs:  [1640, 883, 2282, 279, 461, 4103, 2125, 447, 247, 83, 340, 3400, 26, 706, 513, 2745, 286, 262, 717, 4999, 1339, 11, 279, 461, 4103, 468, 257, 2440, 1303, 10215, 261, 615, 19397, 3349, 2494, 621, 340, 3400, 13, 6154, 319, 262, 2071, 910, 340, 3400, 373, 1165, 3105, 284, 47955, 13, 772, 706, 47955, 11, 340, 1718, 640, 329, 340, 284, 6105, 423, 281, 2928, 13, 1303, 10215, 4450]\n",
      "test Token IDs:  [31, 7220, 14187, 11301, 836, 447, 247, 83, 423, 262, 5339, 13, 3452, 422, 674, 1957, 4436, 318, 484, 481, 691, 1332, 883, 351, 7460, 508, 4504, 422, 442, 1437, 11, 340, 3400, 11, 4173, 272, 11, 474, 2674, 837, 393, 5366, 479, 46215, 287, 938, 1478, 1528, 393, 883, 508, 423, 587, 287, 2800, 351, 2130, 351, 4999, 39849, 312, 12, 1129, 287, 262, 938, 1478, 1528, 13]\n"
     ]
    }
   ],
   "source": [
    "# Print the original sentence.\n",
    "print('train Original: ', train_sentences_initial[0])\n",
    "print('valid Original: ', valid_sentences_initial[0])\n",
    "print('test Original: ', test_sentences_initial[0])\n",
    "\n",
    "# Print the sentence split into tokens.\n",
    "print('Tokenized train: ', tokenizer.tokenize(train_sentences_initial[0]))\n",
    "print('Tokenized valid: ', tokenizer.tokenize(valid_sentences_initial[0]))\n",
    "print('Tokenized test: ', tokenizer.tokenize(test_sentences_initial[0]))\n",
    "\n",
    "# Print the sentence mapped to token ids.\n",
    "print('train Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(train_sentences_initial[0])))\n",
    "print('valid Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(valid_sentences_initial[0])))\n",
    "print('test Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(test_sentences_initial[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V3A5Z8KMrusc",
    "outputId": "52a5fbee-8f9f-498e-9a46-279cbfb643b9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (1116 > 1024). Running this sequence through the model will result in indexing errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset length:  6936\n",
      "validation dataset length:  1000\n",
      "test dataset length:  2000\n",
      "\n",
      "train max sentence length:  1527\n",
      "valid max sentence length:  106\n",
      "test max sentence length:  142\n"
     ]
    }
   ],
   "source": [
    "train_max_len = 0\n",
    "valid_max_len = 0\n",
    "test_max_len = 0\n",
    "\n",
    "train_length_ls = []\n",
    "valid_length_ls = []\n",
    "test_length_ls = []\n",
    "\n",
    "# Decide on a constant maximum sentence length for padding / truncating to \n",
    "# by choosing the max length of the sentences in the dataset.\n",
    "for sent in train_sentences_initial:\n",
    "  # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "  input_ids = tokenizer.encode(sent, add_special_tokens=True)\n",
    "  train_length_ls.append(len(input_ids))\n",
    "\n",
    "  # Update the maximum sentence length.\n",
    "  # train_max_len = max(train_max_len, len(input_ids))\n",
    "\n",
    "for sent in valid_sentences_initial:\n",
    "  # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "  input_ids = tokenizer.encode(sent, add_special_tokens=True)\n",
    "  valid_length_ls.append(len(input_ids))\n",
    "\n",
    "  # Update the maximum sentence length.\n",
    "  # valid_max_len = max(valid_max_len, len(input_ids))\n",
    "\n",
    "for sent in test_sentences_initial:\n",
    "  # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "  input_ids = tokenizer.encode(sent, add_special_tokens=True)\n",
    "  test_length_ls.append(len(input_ids))\n",
    "\n",
    "  # Update the maximum sentence length.\n",
    "  # test_max_len = max(test_max_len, len(input_ids))\n",
    "\n",
    "\n",
    "train_length_df = pd.DataFrame(train_length_ls, columns = ['length'])\n",
    "valid_length_df = pd.DataFrame(valid_length_ls, columns = ['length'])\n",
    "test_length_df = pd.DataFrame(test_length_ls, columns = ['length'])\n",
    "\n",
    "train_max_len = max(train_length_ls)\n",
    "valid_max_len = max(valid_length_ls)\n",
    "test_max_len = max(test_length_ls)\n",
    "\n",
    "print('train dataset length: ', len(train_length_ls))\n",
    "print('validation dataset length: ', len(valid_length_ls))\n",
    "print('test dataset length: ', len(test_length_ls))\n",
    "print()\n",
    "\n",
    "\n",
    "print('train max sentence length: ', train_max_len)\n",
    "print('valid max sentence length: ', valid_max_len)\n",
    "print('test max sentence length: ', test_max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x5IIDoJAr08M",
    "outputId": "7deea5f2-96f3-41e1-c9d7-fc8a1ecdb4a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "longest 5 train: [159, 696, 1116, 1262, 1527]\n",
      "longest 5 valid: [98, 99, 100, 102, 106]\n",
      "longest 5 test: [101, 102, 106, 131, 142]\n"
     ]
    }
   ],
   "source": [
    "train_length_ls.sort()\n",
    "valid_length_ls.sort()\n",
    "test_length_ls.sort()\n",
    "\n",
    "print('longest 5 train:', train_length_ls[len(train_length_ls) - 5:])\n",
    "print('longest 5 valid:', valid_length_ls[len(valid_length_ls) - 5:])\n",
    "print('longest 5 test:', test_length_ls[len(test_length_ls) - 5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GotT8hCnr4pR",
    "outputId": "390d2064-da92-466a-a005-221b35cfa658"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      length  Label\n",
      "0         74      1\n",
      "1         66      1\n",
      "2         73      1\n",
      "3         66      1\n",
      "4         48      0\n",
      "...      ...    ...\n",
      "6931      61      0\n",
      "6932      42      1\n",
      "6933      62      0\n",
      "6934      60      0\n",
      "6935      38      0\n",
      "\n",
      "[6936 rows x 2 columns]\n",
      "     length  Label\n",
      "0        70      0\n",
      "1        78      1\n",
      "2        61      0\n",
      "3        46      0\n",
      "4        65      0\n",
      "..      ...    ...\n",
      "995      35      0\n",
      "996      60      1\n",
      "997      32      0\n",
      "998      54      0\n",
      "999      61      1\n",
      "\n",
      "[1000 rows x 2 columns]\n",
      "      length  Label\n",
      "0         69      0\n",
      "1         25      1\n",
      "2         48      0\n",
      "3         70      0\n",
      "4         41      0\n",
      "...      ...    ...\n",
      "1995      53      0\n",
      "1996      43      0\n",
      "1997      57      1\n",
      "1998      75      1\n",
      "1999      48      1\n",
      "\n",
      "[2000 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "train_length_df['Label'] = train['Label']\n",
    "valid_length_df['Label'] = valid['Label']\n",
    "test_length_df['Label'] = test['Label']\n",
    "\n",
    "print(train_length_df)\n",
    "print(valid_length_df)\n",
    "print(test_length_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "DcVhwzHFr7xg",
    "outputId": "9b4ab327-58f9-4835-9ce3-e7954097e29b"
   },
   "outputs": [
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      "text/plain": [
       "      length  Label\n",
       "0         74      1\n",
       "1         66      1\n",
       "2         73      1\n",
       "3         66      1\n",
       "4         48      0\n",
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    "drop_indices = train_length_df[train_length_df['length'].isin(train_length_ls[len(train_length_ls) - 4:])].index\n",
    "\n",
    "train_length_df.drop(drop_indices, inplace = True)\n",
    "\n",
    "train_length_df"
   ]
  },
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   "metadata": {
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   ],
   "source": [
    "# comparison of length of tweets between uninformative and informative ones, after removing 4 outlier from training dataset\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "train_box_plot = [go.Box(y=train_length_df[train_length_df['Label']==0]['length'],name='Uninformative'),\n",
    "        go.Box(y=train_length_df[train_length_df['Label']==1]['length'],name='Informative')]\n",
    "layout = go.Layout(title = 'Comparison of text length in training dataset Tweets')\n",
    "fig = go.Figure(data=train_box_plot, layout=layout)\n",
    "fig.write_image('./images/fig_eda_text_lengs1.png')\n",
    "fig.show()\n",
    "\n",
    "valid_box_plot = [go.Box(y=valid_length_df[valid_length_df['Label']==0]['length'],name='Uninformative'),\n",
    "        go.Box(y=valid_length_df[valid_length_df['Label']==1]['length'],name='Informative')]\n",
    "layout = go.Layout(title = 'Comparison of text length in validation dataset Tweets')\n",
    "fig = go.Figure(data=valid_box_plot, layout=layout)\n",
    "fig.write_image('./images/fig_eda_text_lengs2.png')\n",
    "fig.show()\n",
    "\n",
    "test_box_plot = [go.Box(y=test_length_df[test_length_df['Label']==0]['length'],name='Uninformative'),\n",
    "        go.Box(y=test_length_df[test_length_df['Label']==1]['length'],name='Informative')]\n",
    "layout = go.Layout(title = 'Comparison of text length in test dataset Tweets')\n",
    "fig = go.Figure(data=test_box_plot, layout=layout)\n",
    "fig.write_image('./images/fig_eda_text_lengs3.png')\n",
    "fig.show()"
   ]
  },
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    "id": "yNOf60PNsCZ_",
    "outputId": "4710c4d8-818e-457f-bb29-07e0e4f717d3"
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   "outputs": [
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       "                            \n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display plot of sentence lengths after removing 4 outliers form training data set \n",
    "#!pip3 install psutil\n",
    "#!pip3 install -U kaleido\n",
    "from plotly import graph_objs as go\n",
    "\n",
    "train_text_length = train_length_df['length'].value_counts().sort_index()\n",
    "valid_text_length = valid_length_df['length'].value_counts().sort_index()\n",
    "test_text_length = test_length_df['length'].value_counts().sort_index()\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=train_text_length.index,\n",
    "    y=train_text_length.values,\n",
    "    name='train sent lengths',\n",
    "    fill='tozeroy',\n",
    "    marker_color=\"cyan\",\n",
    "))\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=valid_text_length.index,\n",
    "    y=valid_text_length.values,\n",
    "    name='validation sent lengths',\n",
    "    fill='tozeroy',\n",
    "    marker_color=\"darkblue\",\n",
    "))\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=test_text_length.index,\n",
    "    y=test_text_length.values,\n",
    "    name='test sent lengths',\n",
    "    fill='tozeroy',\n",
    "    marker_color=\"chocolate\",\n",
    "))\n",
    "fig.update_layout(\n",
    "    title='<span style=\"font-size:32px; font-family:Times New Roman\">Sentence Lengths</span>',\n",
    "    xaxis_title = \"Sentence length\",\n",
    "    yaxis_title = \"Frequency\"\n",
    ")\n",
    "\n",
    "fig.update_xaxes(range=[0, 150])\n",
    "fig.write_image('./images/fig_eda_sent_lengs.png')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "kH94bfOtsIye",
    "outputId": "93aab217-f9c0-40ed-93cd-2fa94bd3db55"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "\n",
    "#twitter_mask = np.array(Image.open('/content/drive/MyDrive/DL for NLP/Project/twitter_mask.jpg'))\n",
    "\n",
    "# wc = WordCloud(\n",
    "#    background_color='blue', \n",
    "#    max_words=200, \n",
    "#    mask=twitter_mask,\n",
    "#)\n",
    "\n",
    "wc = WordCloud(width=800, height=500, random_state=21, max_font_size=110) \n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.title('training data set', \n",
    "          fontdict={'size': 14,  'verticalalignment': 'bottom'})\n",
    "wc.generate(' '.join(text for text in train['Text']))\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.title('validation data set', \n",
    "          fontdict={'size': 14,  'verticalalignment': 'bottom'})\n",
    "wc.generate(' '.join(text for text in valid['Text']))\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.title('test data set', \n",
    "          fontdict={'size': 14,  'verticalalignment': 'bottom'})\n",
    "wc.generate(' '.join(text for text in test['Text']))\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.suptitle('Top words')\n",
    "plt.savefig('./images/fig_eda_top_words.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 415
    },
    "id": "SRGO-VGJsM63",
    "outputId": "b7bccf45-7ff1-4016-9499-116f8eee55c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/boney/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /home/boney/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package stopwords to /home/boney/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "\n",
    "train_unigrams = pd.DataFrame()\n",
    "train_unigrams = train[['Text', 'Label']]\n",
    "train_unigrams['Text_no_punc'] = train_unigrams['Text'].apply(lambda x: remove_punctuation(x))\n",
    "\n",
    "stop_words = set(stopwords.words('english'))\n",
    "stop_words.add('httpurl')\n",
    "\n",
    "tokens = pd.DataFrame()\n",
    "tokens['tokenized_minus_stop'] = train_unigrams['Text_no_punc'].apply(lambda x: [tokens.lower() for tokens in word_tokenize(x) if tokens.lower() not in stop_words])\n",
    "tokens['Label'] = train_unigrams['Label']\n",
    "token_list_ui = [j for i in tokens[tokens['Label'] == 0]['tokenized_minus_stop'] for j in i]\n",
    "token_list_i = [j for i in tokens[tokens['Label'] == 1]['tokenized_minus_stop'] for j in i]\n",
    "\n",
    "token_counts_ui = Counter(token_list_ui)\n",
    "top_token_counts_ui = token_counts_ui.most_common(10)\n",
    "top_token_counts_ui =sorted(top_token_counts_ui, key = lambda x: x[1], reverse=True)\n",
    "\n",
    "token_counts_i = Counter(token_list_i)\n",
    "top_token_counts_i = token_counts_i.most_common(10)\n",
    "top_token_counts_i =sorted(top_token_counts_i, key = lambda x: x[1], reverse=True)\n",
    "\n",
    "x_uninformative, y_uninformative = map(list, zip(*top_token_counts_ui))\n",
    "x_informative, y_informative = map(list, zip(*top_token_counts_i))\n",
    "\n",
    "\n",
    "fig_unigram, ax_unigram = plt.subplots(1,2, figsize=(10,4))\n",
    "sns.barplot(x=y_uninformative, y=x_uninformative, orient='h', palette=\"Reds_d\", ax=ax_unigram[0])\n",
    "sns.barplot(x=y_informative, y=x_informative, orient='h', palette=\"Blues_d\", ax=ax_unigram[1])\n",
    "\n",
    "\n",
    "ax_unigram[0].set_title(\"Top 10 unigrams - Uninformative Tweets\")\n",
    "ax_unigram[0].set_xlabel(\"Word Frequency\")\n",
    "ax_unigram[1].set_title(\"Top 10 unigrams - Informative Tweets\")\n",
    "ax_unigram[1].set_xlabel(\"Word Frequency\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('./images/fig_eda_unigrams.png')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "train.drop(drop_indices, inplace = True)\n",
    "\n",
    "pickle.dump( train, open( \"covid_train.p\", \"wb\" ) )\n",
    "pickle.dump( valid, open( \"covid_valid.p\", \"wb\" ) )\n",
    "pickle.dump( test, open( \"covid_test.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pickle.load( open( \"covid_train.p\", \"rb\" ) )\n",
    "valid = pickle.load( open( \"covid_valid.p\", \"rb\" ) )\n",
    "test = pickle.load( open( \"covid_test.p\", \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "C3kbE34ucJYZ"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "train_sentences = train.Text.values\n",
    "train_labels = train.Label.values\n",
    "\n",
    "valid_sentences = valid.Text.values\n",
    "valid_labels = valid.Label.values\n",
    "\n",
    "test_sentences = test.Text.values\n",
    "test_labels = test.Label.values\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CfcmzTYGhHDx",
    "outputId": "a183547a-acc0-41f2-8ad5-480a308536c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset length:  6932\n",
      "validation dataset length:  1000\n",
      "test dataset length:  2000\n",
      "\n",
      "train max sentence length:  159\n",
      "valid max sentence length:  106\n",
      "test max sentence length:  142\n"
     ]
    }
   ],
   "source": [
    "train_max_len_v2 = 0\n",
    "valid_max_len_v2 = 0\n",
    "test_max_len_v2 = 0\n",
    "\n",
    "train_length_ls_v2 = []\n",
    "valid_length_ls_v2 = []\n",
    "test_length_ls_v2 = []\n",
    "\n",
    "# Decide on a constant maximum sentence length for padding / truncating to \n",
    "# by choosing the max length of the sentences in the dataset.\n",
    "for sent in train_sentences:\n",
    "  # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "  input_ids = tokenizer.encode(sent, add_special_tokens=True)\n",
    "  train_length_ls_v2.append(len(input_ids))\n",
    "\n",
    "  # Update the maximum sentence length.\n",
    "  # train_max_len = max(train_max_len, len(input_ids))\n",
    "\n",
    "for sent in valid_sentences:\n",
    "  # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "  input_ids = tokenizer.encode(sent, add_special_tokens=True)\n",
    "  valid_length_ls_v2.append(len(input_ids))\n",
    "\n",
    "  # Update the maximum sentence length.\n",
    "  # valid_max_len = max(valid_max_len, len(input_ids))\n",
    "\n",
    "for sent in test_sentences:\n",
    "  # Tokenize the text and add `[CLS]` and `[SEP]` tokens.\n",
    "  input_ids = tokenizer.encode(sent, add_special_tokens=True)\n",
    "  test_length_ls_v2.append(len(input_ids))\n",
    "\n",
    "  # Update the maximum sentence length.\n",
    "  # test_max_len = max(test_max_len, len(input_ids))\n",
    "\n",
    "train_max_len_v2 = max(train_length_ls_v2)\n",
    "valid_max_len_v2 = max(valid_length_ls_v2)\n",
    "test_max_len_v2 = max(test_length_ls_v2)\n",
    "\n",
    "print('train dataset length: ', len(train_length_ls_v2))\n",
    "print('validation dataset length: ', len(valid_length_ls_v2))\n",
    "print('test dataset length: ', len(test_length_ls_v2))\n",
    "print()\n",
    "\n",
    "print('train max sentence length: ', train_max_len_v2)\n",
    "print('valid max sentence length: ', valid_max_len_v2)\n",
    "print('test max sentence length: ', test_max_len_v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Dt2RqlRsTti",
    "outputId": "87e3bc9e-789d-46a6-b8f4-3c7da3473cf2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Original sentence:  official death toll from #covid19 in the united kingdom is now greater than: germany + poland + switzerland + austria + portugal + greece + sweden + finland + norway + ireland... combined. uk: 67.5 million (233 dead) above group: 185 million (230 dead) httpurl\n",
      "train Token IDs list: tensor([16841,  1918, 13592,   422,  1303,    66,   709,   312,  1129,   287,\n",
      "          262, 16503, 13239,   318,   783,  3744,   621,    25,   308,  2224,\n",
      "           88,  1343,   755,   392,  1343,  1509, 13947,  1343, 38132,  7496,\n",
      "         1343,  2493, 43778,  1343,   308,   631,   344,  1343,  1509, 31829,\n",
      "         1343,   957,  1044,  1343,  4249,  1014,  1343, 35918,  1044,   986,\n",
      "         5929,    13,   334,    74,    25,  8275,    13,    20,  1510,   357,\n",
      "        25429,  2636,     8,  2029])\n",
      "validation Original sentence:  for those saying pakistan isn’t italy; after 3 weeks of the first confirmed case, pakistan has a higher #coronavirus growth rate than italy. experts on the issue say italy was too slow to lockdown. even after lockdown, it took time for it to properly have an impact. #corona\n",
      "validation Token IDs list: tensor([ 1640,   883,  2282,   279,   461,  4103,  2125,   447,   247,    83,\n",
      "          340,  3400,    26,   706,   513,  2745,   286,   262,   717,  4999,\n",
      "         1339,    11,   279,   461,  4103,   468,   257,  2440,  1303, 10215,\n",
      "          261,   615, 19397,  3349,  2494,   621,   340,  3400,    13,  6154,\n",
      "          319,   262,  2071,   910,   340,  3400,   373,  1165,  3105,   284,\n",
      "        47955,    13,   772,   706, 47955,    11,   340,  1718,   640,   329,\n",
      "          340,   284,  6105,   423])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-600ef6e66e66>:75: UserWarning:\n",
      "\n",
      "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "\n",
      "<ipython-input-56-600ef6e66e66>:76: UserWarning:\n",
      "\n",
      "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Tokenize all of the sentences and map the tokens to thier word IDs.\n",
    "train_input_ids = []\n",
    "train_attention_masks = []\n",
    "valid_input_ids = []\n",
    "valid_attention_masks = []\n",
    "\n",
    "# For every sentence...\n",
    "for sent in train_sentences:\n",
    "    # `encode_plus` will:\n",
    "    #   (1) Tokenize the sentence.\n",
    "    #   (2) Prepend the `[CLS]` token to the start.\n",
    "    #   (3) Append the `[SEP]` token to the end.\n",
    "    #   (4) Map tokens to their IDs.\n",
    "    #   (5) Pad or truncate the sentence to `max_length`\n",
    "    #   (6) Create attention masks for [PAD] tokens.\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        sent,                      # Sentence to encode.\n",
    "                        add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
    "                        max_length = 64,           # Pad & truncate all sentences.\n",
    "                        pad_to_max_length = True,\n",
    "                        return_attention_mask = True,   # Construct attn. masks.\n",
    "                        return_tensors = 'pt',     # Return pytorch tensors.\n",
    "                   )\n",
    "    \n",
    "    # Add the encoded sentence to the list.    \n",
    "    train_input_ids.append(encoded_dict['input_ids'])\n",
    "    \n",
    "    # And its attention mask (simply differentiates padding from non-padding).\n",
    "    train_attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "\n",
    "# For every sentence...\n",
    "for sent in valid_sentences:\n",
    "    # `encode_plus` will:\n",
    "    #   (1) Tokenize the sentence.\n",
    "    #   (2) Prepend the `[CLS]` token to the start.\n",
    "    #   (3) Append the `[SEP]` token to the end.\n",
    "    #   (4) Map tokens to their IDs.\n",
    "    #   (5) Pad or truncate the sentence to `max_length`\n",
    "    #   (6) Create attention masks for [PAD] tokens.\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        sent,                      # Sentence to encode.\n",
    "                        add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
    "                        max_length = 64,           # Pad & truncate all sentences.\n",
    "                        pad_to_max_length = True,\n",
    "                        return_attention_mask = True,   # Construct attn. masks.\n",
    "                        return_tensors = 'pt',     # Return pytorch tensors.\n",
    "                   )\n",
    "    \n",
    "    # Add the encoded sentence to the list.    \n",
    "    valid_input_ids.append(encoded_dict['input_ids'])\n",
    "    \n",
    "    # And its attention mask (simply differentiates padding from non-padding).\n",
    "    valid_attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "\n",
    "import pickle\n",
    "\n",
    "pickle.dump( train_input_ids, open( \"covid_train_input_ids.p\", \"wb\" ) )\n",
    "pickle.dump( train_attention_masks, open( \"covid_train_attention_masks.p\", \"wb\" ) )\n",
    "pickle.dump( train_labels, open( \"covid_train_labels.p\", \"wb\" ) )\n",
    "pickle.dump( valid_labels, open( \"covid_valid_labels.p\", \"wb\" ) )\n",
    "\n",
    "#train = pickle.load( open( \"covid_train.p\", \"rb\" ) )\n",
    "#valid = pickle.load( open( \"covid_valid.p\", \"rb\" ) )\n",
    "#test = pickle.load( open( \"covid_test.p\", \"rb\" ) )\n",
    "\n",
    "# Convert the lists into tensors.\n",
    "train_input_ids = torch.cat(train_input_ids, dim=0)\n",
    "train_attention_masks = torch.cat(train_attention_masks, dim=0)\n",
    "\n",
    "valid_input_ids = torch.cat(valid_input_ids, dim=0)\n",
    "valid_attention_masks = torch.cat(valid_attention_masks, dim=0)\n",
    "\n",
    "train_labels = torch.tensor(train_labels)\n",
    "valid_labels = torch.tensor(valid_labels)\n",
    "\n",
    "# Print sentence 0, now as a list of IDs.\n",
    "print('train Original sentence: ', train_sentences[0])\n",
    "print('train Token IDs list:', train_input_ids[0])\n",
    "\n",
    "print('validation Original sentence: ', valid_sentences[0])\n",
    "print('validation Token IDs list:', valid_input_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "7a_pjve5seoz"
   },
   "outputs": [],
   "source": [
    "# Combine the training inputs into a TensorDataset.\n",
    "train_dataset = TensorDataset(train_input_ids, train_attention_masks, train_labels)\n",
    "valid_dataset = TensorDataset(valid_input_ids, valid_attention_masks, valid_labels)\n",
    "\n",
    "# Create a 80-20 train-validation split.\n",
    "# Calculate the number of samples to include in each set.\n",
    "# train_size = int(0.8 * len(dataset))\n",
    "# val_size = len(dataset) - train_size\n",
    "\n",
    "# Divide the dataset by randomly selecting samples.\n",
    "# train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n",
    "\n",
    "# print('{:>5,} training samples'.format(train_size))\n",
    "# print('{:>5,} validation samples'.format(val_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "coOcc25FsjNJ"
   },
   "outputs": [],
   "source": [
    "# Define a batch size value for the DataLoader\n",
    "# It is not recommended to use a high value, usually batch size of 16 or 32 works perfectly\n",
    "batch_size = 32\n",
    "\n",
    "# Create the DataLoaders for train set with the training samples in random order\n",
    "train_dataloader = DataLoader(\n",
    "            train_dataset,  # The training samples.\n",
    "            sampler = RandomSampler(train_dataset), # Select batches randomly\n",
    "            batch_size = batch_size \n",
    "        )\n",
    "\n",
    "# For validation the order doesn't really matter, \n",
    "# so DataLoader will just read them sequentially.\n",
    "validation_dataloader = DataLoader(\n",
    "            valid_dataset, # The validation samples.\n",
    "            sampler = SequentialSampler(valid_dataset), # Pull out batches sequentially.\n",
    "            batch_size = batch_size # Evaluate with this batch size.\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "607024579253490291da1b7b23720d8f",
      "eb79b013afea486184764cb0af358584",
      "b7929be6c04d40329f2e9d6c7d0d873e",
      "3b7daecd35804a13905122e5605ae3d2",
      "4d3763c36f2644ebb0fc4ee40cef82b9",
      "7909924edbcf4e0a813a78cfccd40258",
      "b214af40b1244c21b0afa16a5752c3eb",
      "a60d1b91459f4700aa5338d3ab8a91ac",
      "dd62928afe51491bb30b3772af396e84",
      "e50b61a3ca4940d98c4666e4c9465733",
      "f9812472ba034453a7f3f1d9fef60ed9",
      "0b01227d87044d9f9519bb3b4d0aa228",
      "cce48b4df3f84f75952ad0b73b721d31",
      "f130782a0bae42289b70de0beccb9fec",
      "1d5b33fae5a046398c3ceedc497eff09",
      "4432cc92d6a646bf990ff5ae778a99a7"
     ]
    },
    "id": "LnFcjCUusmbS",
    "outputId": "4b96ffad-6921-4eaf-9ca3-096a4c5074df"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GPT2ForSequenceClassification(\n",
       "  (transformer): GPT2Model(\n",
       "    (wte): Embedding(50257, 768)\n",
       "    (wpe): Embedding(1024, 768)\n",
       "    (drop): Dropout(p=0.1, inplace=False)\n",
       "    (h): ModuleList(\n",
       "      (0): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (1): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (2): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (3): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (4): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (5): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (6): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (7): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (8): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (9): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (10): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (11): Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (score): Linear(in_features=768, out_features=2, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load BertForSequenceClassification from_pretrained()\n",
    "\n",
    "model_config = GPT2Config.from_pretrained('gpt2', num_labels=2, output_attentions = False, output_hidden_states = False) # Binary Classification\n",
    "model = GPT2ForSequenceClassification.from_pretrained('gpt2', config=model_config)\n",
    "\n",
    "# model = BertForSequenceClassification.from_pretrained(\n",
    "#    \"bert-base-uncased\", # Use the 12-layer BERT model, with an uncased vocab.\n",
    "#    num_labels = 2, # Number of output labels--2 for binary classification (1 and 0).\n",
    "#    output_attentions = False, # Whether the model returns attentions weights.\n",
    "#    output_hidden_states = False, # Whether the model returns all hidden-states.\n",
    "#)\n",
    "\n",
    "# model.resize_token_embeddings(len(tokenizer))\n",
    "model.config.pad_token_id = model.config.eos_token_id\n",
    "\n",
    "# Run this model on the GPU.\n",
    "model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "EyyIWTSdssxV"
   },
   "outputs": [],
   "source": [
    "# Initialize the AdamW optimizer \n",
    "\n",
    "optimizer = AdamW(model.parameters(),\n",
    "                  lr = 2e-5,\n",
    "                  eps = 1e-8\n",
    "                )\n",
    "\n",
    "# Number of training epochs.\n",
    "epochs = 4\n",
    "\n",
    "# Total number of training steps is [number of batches] x [number of epochs]. \n",
    "total_steps = len(train_dataloader) * epochs\n",
    "\n",
    "# Create the learning rate scheduler.\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
    "                                            num_warmup_steps = 0,\n",
    "                                            num_training_steps = total_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "RADEZz4RstWZ"
   },
   "outputs": [],
   "source": [
    "# Function to calculate the accuracy of our predictions vs labels\n",
    "def flat_accuracy(preds, labels):\n",
    "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    return np.sum(pred_flat == labels_flat) / len(labels_flat)\n",
    "\n",
    "# I will use this function to know how much time the model needed to train\n",
    "def format_time(elapsed):\n",
    "    '''\n",
    "    Takes a time in seconds and returns a string hh:mm:ss\n",
    "    '''\n",
    "    # Round to the nearest second.\n",
    "    elapsed_rounded = int(round((elapsed)))\n",
    "    \n",
    "    # Format as hh:mm:ss\n",
    "    return str(datetime.timedelta(seconds=elapsed_rounded))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mmxUDnoTsxk2",
    "outputId": "9151a96f-4e4c-4806-e893-ba00f148a346"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======== Epoch 1 / 4 ========\n",
      "Training...\n",
      "  Batch    40  of    217.    Elapsed: 0:00:07.\n",
      "  Batch    80  of    217.    Elapsed: 0:00:13.\n",
      "  Batch   120  of    217.    Elapsed: 0:00:20.\n",
      "  Batch   160  of    217.    Elapsed: 0:00:26.\n",
      "  Batch   200  of    217.    Elapsed: 0:00:33.\n",
      "\n",
      "  Average training loss: 0.68\n",
      "  Training epcoh took: 0:00:36\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.76\n",
      "  Validation Loss: 0.51\n",
      "  Validation took  0:00:01 time\n",
      "\n",
      "======== Epoch 2 / 4 ========\n",
      "Training...\n",
      "  Batch    40  of    217.    Elapsed: 0:00:07.\n",
      "  Batch    80  of    217.    Elapsed: 0:00:13.\n",
      "  Batch   120  of    217.    Elapsed: 0:00:20.\n",
      "  Batch   160  of    217.    Elapsed: 0:00:27.\n",
      "  Batch   200  of    217.    Elapsed: 0:00:34.\n",
      "\n",
      "  Average training loss: 0.39\n",
      "  Training epcoh took: 0:00:36\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.77\n",
      "  Validation Loss: 0.48\n",
      "  Validation took  0:00:02 time\n",
      "\n",
      "======== Epoch 3 / 4 ========\n",
      "Training...\n",
      "  Batch    40  of    217.    Elapsed: 0:00:07.\n",
      "  Batch    80  of    217.    Elapsed: 0:00:14.\n",
      "  Batch   120  of    217.    Elapsed: 0:00:21.\n",
      "  Batch   160  of    217.    Elapsed: 0:00:28.\n",
      "  Batch   200  of    217.    Elapsed: 0:00:35.\n",
      "\n",
      "  Average training loss: 0.33\n",
      "  Training epcoh took: 0:00:38\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.82\n",
      "  Validation Loss: 0.41\n",
      "  Validation took  0:00:02 time\n",
      "\n",
      "======== Epoch 4 / 4 ========\n",
      "Training...\n",
      "  Batch    40  of    217.    Elapsed: 0:00:07.\n",
      "  Batch    80  of    217.    Elapsed: 0:00:15.\n",
      "  Batch   120  of    217.    Elapsed: 0:00:22.\n",
      "  Batch   160  of    217.    Elapsed: 0:00:29.\n",
      "  Batch   200  of    217.    Elapsed: 0:00:37.\n",
      "\n",
      "  Average training loss: 0.28\n",
      "  Training epcoh took: 0:00:40\n",
      "\n",
      "Running Validation...\n",
      "  Accuracy: 0.82\n",
      "  Validation Loss: 0.40\n",
      "  Validation took  0:00:02 time\n",
      "\n",
      "Training complete!\n",
      "Total training took 0:02:36 (h:mm:ss)\n"
     ]
    }
   ],
   "source": [
    "# We'll store a number of quantities such as training and validation loss, \n",
    "# validation accuracy, and timings.\n",
    "training_stats = []\n",
    "\n",
    "# Measure the total training time for the whole run.\n",
    "total_t0 = time.time()\n",
    "\n",
    "# For each epoch...\n",
    "for epoch_i in range(0, epochs):\n",
    "    \n",
    "    # ========================================\n",
    "    #               Training\n",
    "    # ========================================\n",
    "    \n",
    "    # Perform one full pass over the training set.\n",
    "\n",
    "    print(\"\")\n",
    "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
    "    print('Training...')\n",
    "\n",
    "    # Measure how long the training epoch takes.\n",
    "    t0 = time.time()\n",
    "\n",
    "    # Reset the total loss for this epoch.\n",
    "    total_train_loss = 0\n",
    "\n",
    "    # Put the model into training mode. Don't be mislead--the call to \n",
    "    # `train` just changes the *mode*, it doesn't *perform* the training.\n",
    "    # `dropout` and `batchnorm` layers behave differently during training\n",
    "    # vs. test (source: https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch)\n",
    "    model.train()\n",
    "\n",
    "    # For each batch of training data...\n",
    "    for step, batch in enumerate(train_dataloader):\n",
    "\n",
    "        # Progress update every 40 batches.\n",
    "        if step % 40 == 0 and not step == 0:\n",
    "            # Calculate elapsed time in minutes.\n",
    "            elapsed = format_time(time.time() - t0)\n",
    "            \n",
    "            # Report progress.\n",
    "            print('  Batch {:>5,}  of  {:>5,}.    Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
    "\n",
    "        # Unpack this training batch from our dataloader. \n",
    "        #\n",
    "        # As we unpack the batch, we'll also copy each tensor to the GPU using the \n",
    "        # `to` method.\n",
    "        #\n",
    "        # `batch` contains three pytorch tensors:\n",
    "        #   [0]: input ids \n",
    "        #   [1]: attention masks\n",
    "        #   [2]: labels \n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        b_labels = batch[2].to(device)\n",
    "\n",
    "        # Always clear any previously calculated gradients before performing a\n",
    "        # backward pass. PyTorch doesn't do this automatically because \n",
    "        # accumulating the gradients is \"convenient while training RNNs\". \n",
    "        # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)\n",
    "        model.zero_grad()        \n",
    "\n",
    "        # Perform a forward pass (evaluate the model on this training batch).\n",
    "        # The documentation for this `model` function is here: \n",
    "        # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
    "        # It returns different numbers of parameters depending on what arguments\n",
    "        # arge given and what flags are set. For our useage here, it returns\n",
    "        # the loss (because we provided labels) and the \"logits\"--the model\n",
    "        # outputs prior to activation.\n",
    "        loss, logits, _ = model(b_input_ids, \n",
    "                             token_type_ids=None, \n",
    "                             attention_mask=b_input_mask, \n",
    "                             labels=b_labels, \n",
    "                             return_dict=False)\n",
    "\n",
    "\n",
    "        # Perform a backward pass to calculate the gradients.\n",
    "        loss.backward()\n",
    "\n",
    "        # Clip the norm of the gradients to 1.0.\n",
    "        # This is to help prevent the \"exploding gradients\" problem.\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "        # Update parameters and take a step using the computed gradient.\n",
    "        # The optimizer dictates the \"update rule\"--how the parameters are\n",
    "        # modified based on their gradients, the learning rate, etc.\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "        # Accumulate the training loss over all of the batches so that we can\n",
    "        # calculate the average loss at the end. `loss` is a Tensor containing a\n",
    "        # single value; the `.item()` function just returns the Python value \n",
    "        # from the tensor.\n",
    "        #print(type(loss))\n",
    "        #print(loss)\n",
    "\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "        # Update the learning rate.\n",
    "        scheduler.step()\n",
    "\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_train_loss = total_train_loss / len(train_dataloader)            \n",
    "    \n",
    "    # Measure how long this epoch took.\n",
    "    training_time = format_time(time.time() - t0)\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"  Average training loss: {0:.2f}\".format(avg_train_loss))\n",
    "    print(\"  Training epcoh took: {:}\".format(training_time))\n",
    "        \n",
    "    # ========================================\n",
    "    #               Validation\n",
    "    # ========================================\n",
    "    # After the completion of each training epoch, measure our performance on\n",
    "    # our validation set.\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"Running Validation...\")\n",
    "\n",
    "    t0 = time.time()\n",
    "\n",
    "    # Put the model in evaluation mode--the dropout layers behave differently\n",
    "    # during evaluation.\n",
    "    model.eval()\n",
    "\n",
    "    # Tracking variables \n",
    "    total_eval_accuracy = 0\n",
    "    total_eval_loss = 0\n",
    "    nb_eval_steps = 0\n",
    "\n",
    "    # Evaluate data for one epoch\n",
    "    for batch in validation_dataloader:\n",
    "        \n",
    "        # Unpack this training batch from our dataloader. \n",
    "        # As we unpack the batch, I'll also copy each tensor to the GPU using the `to` method.\n",
    "        #\n",
    "        # `batch` contains three pytorch tensors:\n",
    "        #   [0]: input ids \n",
    "        #   [1]: attention masks\n",
    "        #   [2]: labels \n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        b_labels = batch[2].to(device)\n",
    "        \n",
    "        # Tell pytorch not to bother with constructing the compute graph during\n",
    "        # the forward pass, since this is only needed for backprop (training).\n",
    "        with torch.no_grad():        \n",
    "\n",
    "            # Forward pass, calculate logit predictions.\n",
    "            # token_type_ids is the same as the \"segment ids\", which \n",
    "            # differentiates sentence 1 and 2 in 2-sentence tasks.\n",
    "            # Get the \"logits\" output by the model. The \"logits\" are the output\n",
    "            # values prior to applying an activation function like the softmax.\n",
    "            loss, logits, _ = model(b_input_ids, \n",
    "                                   token_type_ids=None, \n",
    "                                   attention_mask=b_input_mask,\n",
    "                                   labels=b_labels,\n",
    "                                   return_dict=False)\n",
    "            \n",
    "        # Accumulate the validation loss.\n",
    "        total_eval_loss += loss.item()\n",
    "\n",
    "        # Move logits and labels to CPU\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        label_ids = b_labels.to('cpu').numpy()\n",
    "\n",
    "        # Calculate the accuracy for this batch of test sentences, and\n",
    "        # accumulate it over all batches.\n",
    "        total_eval_accuracy += flat_accuracy(logits, label_ids)\n",
    "        \n",
    "\n",
    "    # Report the final accuracy for this validation run.\n",
    "    avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
    "    print(\"  Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
    "\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
    "    \n",
    "    # Measure how long the validation run took.\n",
    "    validation_time = format_time(time.time() - t0)\n",
    "    \n",
    "    print(\"  Validation Loss: {0:.2f}\".format(avg_val_loss))\n",
    "    print(\"  Validation took  {:} time\".format(validation_time))\n",
    "\n",
    "    # Record all statistics from this epoch.\n",
    "    training_stats.append(\n",
    "        {\n",
    "            'epoch': epoch_i + 1,\n",
    "            'Training Loss': avg_train_loss,\n",
    "            'Valid. Loss': avg_val_loss,\n",
    "            'Valid. Accur.': avg_val_accuracy,\n",
    "            'Training Time': training_time,\n",
    "            'Validation Time': validation_time\n",
    "        }\n",
    "    )\n",
    "\n",
    "print(\"\")\n",
    "print(\"Training complete!\")\n",
    "\n",
    "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "Q45_sXoDs9Lx",
    "outputId": "9d0a3fb7-a550-4664-e8ba-516803a841b9"
   },
   "outputs": [
    {
     "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>Training Loss</th>\n",
       "      <th>Valid. Loss</th>\n",
       "      <th>Valid. Accur.</th>\n",
       "      <th>Training Time</th>\n",
       "      <th>Validation Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>epoch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0:00:36</td>\n",
       "      <td>0:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0:00:36</td>\n",
       "      <td>0:00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0:00:38</td>\n",
       "      <td>0:00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0:00:40</td>\n",
       "      <td>0:00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Training Loss  Valid. Loss  Valid. Accur. Training Time Validation Time\n",
       "epoch                                                                         \n",
       "1               0.68         0.51           0.76       0:00:36         0:00:01\n",
       "2               0.39         0.48           0.77       0:00:36         0:00:02\n",
       "3               0.33         0.41           0.82       0:00:38         0:00:02\n",
       "4               0.28         0.40           0.82       0:00:40         0:00:02"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display floats with two decimal places.\n",
    "pd.set_option('precision', 2)\n",
    "\n",
    "# Create a DataFrame from our training statistics.\n",
    "df_statistics = pd.DataFrame(data=training_stats)\n",
    "\n",
    "# Use the 'epoch' as the row index.\n",
    "df_statistics = df_statistics.set_index('epoch')\n",
    "\n",
    "# A hack to force the column headers to wrap.\n",
    "#df = df.style.set_table_styles([dict(selector=\"th\",props=[('max-width', '70px')])])\n",
    "\n",
    "# Display the table.\n",
    "df_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "cyGdQzKItEbl",
    "outputId": "899bad00-b5fa-4272-8739-ff452ab9c18b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use plot styling from seaborn.\n",
    "sns.set(style='darkgrid')\n",
    "\n",
    "# Increase the plot size and font size.\n",
    "sns.set(font_scale=1.5)\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "\n",
    "# Plot the learning curve.\n",
    "plt.plot(df_statistics['Training Loss'], 'b-o', label=\"Training\")\n",
    "plt.plot(df_statistics['Valid. Loss'], 'g-o', label=\"Validation\")\n",
    "\n",
    "# Label the plot.\n",
    "plt.title(\"Training & Validation Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.xticks([1, 2, 3, 4])\n",
    "\n",
    "#plt.show()\n",
    "plt.savefig('./images/fig_gpt2_trainVal_loss.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0Skua029tIfC",
    "outputId": "63114c56-d37d-458f-e6ea-928502b7b1ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of test sentences: 2,000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/boney/.local/lib/python3.8/site-packages/transformers/tokenization_utils_base.py:2068: FutureWarning:\n",
      "\n",
      "The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Original sentence:  @user pa hospitals don’t have the capacity. latest from our local hospital is they will only test those with symptoms who returned from china, italy, iran, japan , or south korea in last 14 days or those who have been in contact with someone with confirmed covid-19 in the last 14 days.\n",
      "test Token IDs list: tensor([   31,  7220, 14187, 11301,   836,   447,   247,    83,   423,   262,\n",
      "         5339,    13,  3452,   422,   674,  1957,  4436,   318,   484,   481,\n",
      "          691,  1332,   883,   351,  7460,   508,  4504,   422,   442,  1437,\n",
      "           11,   340,  3400,    11,  4173,   272,    11,   474,  2674,   837,\n",
      "          393,  5366,   479, 46215,   287,   938,  1478,  1528,   393,   883,\n",
      "          508,   423,   587,   287,  2800,   351,  2130,   351,  4999, 39849,\n",
      "          312,    12,  1129,   287])\n"
     ]
    }
   ],
   "source": [
    "# Report the number of sentences.\n",
    "print('Number of test sentences: {:,}\\n'.format(test.shape[0]))\n",
    "\n",
    "# Tokenize all of the sentences and map the tokens to thier word IDs.\n",
    "test_input_ids = []\n",
    "test_attention_masks = []\n",
    "\n",
    "# For every sentence...\n",
    "for sent in test_sentences:\n",
    "    # `encode_plus` will:\n",
    "    #   (1) Tokenize the sentence.\n",
    "    #   (2) Prepend the `[CLS]` token to the start.\n",
    "    #   (3) Append the `[SEP]` token to the end.\n",
    "    #   (4) Map tokens to their IDs.\n",
    "    #   (5) Pad or truncate the sentence to `max_length`\n",
    "    #   (6) Create attention masks for [PAD] tokens.\n",
    "    encoded_dict = tokenizer.encode_plus(\n",
    "                        sent,                      # Sentence to encode.\n",
    "                        add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
    "                        max_length = 64,           # Pad & truncate all sentences.\n",
    "                        pad_to_max_length = True,\n",
    "                        return_attention_mask = True,   # Construct attn. masks.\n",
    "                        return_tensors = 'pt',     # Return pytorch tensors.\n",
    "                   )\n",
    "    \n",
    "    # Add the encoded sentence to the list.    \n",
    "    test_input_ids.append(encoded_dict['input_ids'])\n",
    "    \n",
    "    # And its attention mask (simply differentiates padding from non-padding).\n",
    "    test_attention_masks.append(encoded_dict['attention_mask'])\n",
    "\n",
    "# Convert the lists into tensors.\n",
    "test_input_ids = torch.cat(test_input_ids, dim=0)\n",
    "test_attention_masks = torch.cat(test_attention_masks, dim=0)\n",
    "\n",
    "test_labels = torch.tensor(test_labels)\n",
    "\n",
    "# Print sentence 0, now as a list of IDs.\n",
    "print('test Original sentence: ', test_sentences[0])\n",
    "print('test Token IDs list:', test_input_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "lWSZfvNgtOW7"
   },
   "outputs": [],
   "source": [
    "# Set test_data_set, the batch size and create DataLoader\n",
    "test_dataset = TensorDataset(test_input_ids, test_attention_masks, test_labels)\n",
    "\n",
    "batch_size = 32 # same as in training: 32  \n",
    "\n",
    "test_dataloader = DataLoader(\n",
    "            test_dataset, # The test samples.\n",
    "            sampler = SequentialSampler(test_dataset), # Pull out batches sequentially.\n",
    "            batch_size = batch_size # Evaluate with this batch size.\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JtNDwDMMtclK",
    "outputId": "cf8be7d6-95d3-436a-99e4-e8bcbbac780c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicting labels for 2,000 test sentences...\n",
      "  Accuracy: 0.80\n",
      "  Test Loss: 0.49\n",
      "  Test took  0:00:03 time\n"
     ]
    }
   ],
   "source": [
    "    print(\"\")\n",
    "    print('Predicting labels for {:,} test sentences...'.format(len(test_input_ids)))\n",
    "\n",
    "    test_stats = []\n",
    "\n",
    "    t0 = time.time()\n",
    "\n",
    "    # Put the model in evaluation mode--the dropout layers behave differently\n",
    "    # during evaluation.\n",
    "    model.eval()\n",
    "\n",
    "    # Tracking variables \n",
    "    total_eval_accuracy = 0\n",
    "    total_eval_loss = 0\n",
    "    nb_eval_steps = 0\n",
    "\n",
    "    # Evaluate data for one epoch\n",
    "    for batch in test_dataloader:\n",
    "        \n",
    "        # Unpack this training batch from our dataloader. \n",
    "        # As we unpack the batch, I'll also copy each tensor to the GPU using the `to` method.\n",
    "        #\n",
    "        # `batch` contains three pytorch tensors:\n",
    "        #   [0]: input ids \n",
    "        #   [1]: attention masks\n",
    "        #   [2]: labels \n",
    "        b_input_ids = batch[0].to(device)\n",
    "        b_input_mask = batch[1].to(device)\n",
    "        b_labels = batch[2].to(device)\n",
    "        \n",
    "        # Tell pytorch not to bother with constructing the compute graph during\n",
    "        # the forward pass, since this is only needed for backprop (training).\n",
    "        with torch.no_grad():        \n",
    "\n",
    "            # Forward pass, calculate logit predictions.\n",
    "            # token_type_ids is the same as the \"segment ids\", which \n",
    "            # differentiates sentence 1 and 2 in 2-sentence tasks.\n",
    "            # Get the \"logits\" output by the model. The \"logits\" are the output\n",
    "            # values prior to applying an activation function like the softmax.\n",
    "            loss, logits, _ = model(b_input_ids, \n",
    "                                   token_type_ids=None, \n",
    "                                   attention_mask=b_input_mask,\n",
    "                                   labels=b_labels,\n",
    "                                   return_dict=False)\n",
    "            \n",
    "        # Accumulate the validation loss.\n",
    "        total_eval_loss += loss.item()\n",
    "\n",
    "        # Move logits and labels to CPU\n",
    "        logits = logits.detach().cpu().numpy()\n",
    "        label_ids = b_labels.to('cpu').numpy()\n",
    "\n",
    "        # Calculate the accuracy for this batch of test sentences, and\n",
    "        # accumulate it over all batches.\n",
    "        total_eval_accuracy += flat_accuracy(logits, label_ids)\n",
    "        \n",
    "\n",
    "    # Report the final accuracy for this validation run.\n",
    "    avg_test_accuracy = total_eval_accuracy / len(test_dataloader)\n",
    "    print(\"  Accuracy: {0:.2f}\".format(avg_test_accuracy))\n",
    "\n",
    "    # Calculate the average loss over all of the batches.\n",
    "    avg_test_loss = total_eval_loss / len(test_dataloader)\n",
    "    \n",
    "    # Measure how long the validation run took.\n",
    "    test_time = format_time(time.time() - t0)\n",
    "    \n",
    "    print(\"  Test Loss: {0:.2f}\".format(avg_test_loss))\n",
    "    print(\"  Test took  {:} time\".format(test_time))\n",
    "\n",
    "    # Record all statistics from this epoch.\n",
    "    test_stats.append(\n",
    "        {\n",
    "            'Test. Loss': avg_val_loss,\n",
    "            'Test. Accur.': avg_val_accuracy,\n",
    "            'Test time': test_time\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the predictions for each batch into a single list of 0s and 1s.\n",
    "flat_predictions = [item for sublist in preds for item in sublist]\n",
    "flat_predictions = np.argmax(flat_predictions, axis=1).flatten()\n",
    "# Combine the correct labels for each batch into a single list.\n",
    "flat_true_labels = [item for sublist in true for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "comments1 = df_test.text.values\n",
    "\n",
    "indices1=tokenizer.batch_encode_plus(comments1,max_length=128,add_special_tokens=True, return_attention_mask=True,pad_to_max_length=True,truncation=True)\n",
    "input_ids1=indices1[\"input_ids\"]\n",
    "attention_masks1=indices1[\"attention_mask\"]\n",
    "\n",
    "prediction_inputs1= torch.tensor(input_ids1)\n",
    "prediction_masks1 = torch.tensor(attention_masks1)\n",
    "\n",
    "\n",
    "# Set the batch size.  \n",
    "batch_size = 32 \n",
    "\n",
    "# Create the DataLoader.\n",
    "prediction_data1 = TensorDataset(prediction_inputs1, prediction_masks1)\n",
    "prediction_sampler1 = SequentialSampler(prediction_data1)\n",
    "prediction_dataloader1 = DataLoader(prediction_data1, sampler=prediction_sampler1, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs1)))\n",
    "\n",
    "# Put model in evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# Tracking variables \n",
    "predictions = []\n",
    "\n",
    "# Predict \n",
    "for batch in prediction_dataloader1:\n",
    "  # Add batch to GPU\n",
    "  batch = tuple(t.to(device) for t in batch)\n",
    "  \n",
    "  # Unpack the inputs from our dataloader\n",
    "  b_input_ids1, b_input_mask1 = batch\n",
    "  \n",
    "  # Telling the model not to compute or store gradients, saving memory and \n",
    "  # speeding up prediction\n",
    "  with torch.no_grad():\n",
    "      # Forward pass, calculate logit predictions\n",
    "      outputs1 = model(b_input_ids1, token_type_ids=None, \n",
    "                      attention_mask=b_input_mask1)\n",
    "\n",
    "  logits1 = outputs1[0]\n",
    "\n",
    "  # Move logits and labels to CPU\n",
    "  logits1 = logits1.detach().cpu().numpy()\n",
    "  \n",
    "  \n",
    "  # Store predictions and true labels\n",
    "  predictions.append(logits1)\n",
    "\n",
    "flat_predictions = [item for sublist in predictions for item in sublist]\n",
    "flat_predictions = np.argmax(flat_predictions, axis=1).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_sub=pd.read_csv('./nlp_covid/data/kaggle_sample_submission.csv')\n",
    "submit=pd.DataFrame({'id':sample_sub['id'].values.tolist(),'target':flat_predictions})\n",
    "submit.to_csv('submit_bert.csv', index=False)\n",
    "print(submit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./nlp_covid/data/kaggle_train.csv\")\n",
    "test = pd.read_csv(\"./nlp_covid/data/kaggle_test.csv\")\n",
    "\n",
    "df_train= train\n",
    "df_test= test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    text=text.lower()\n",
    "    text = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', text)\n",
    "    text = re.sub(r'http?:\\/\\/.*[\\r\\n]*', '', text)\n",
    "    text=text.replace(r'&amp;?',r'and')\n",
    "    text=text.replace(r'&lt;',r'<')\n",
    "    text=text.replace(r'&gt;',r'>')\n",
    "    text = re.sub(r\"(?:\\@)\\w+\", '', text)\n",
    "    text=text.encode(\"ascii\",errors=\"ignore\").decode()\n",
    "    text=re.sub(r'[:\"#$%&\\*+,-/:;<=>@\\\\^_`{|}~]+','',text)\n",
    "    text=re.sub(r'[!]+','!',text)\n",
    "    text=re.sub(r'[?]+','?',text)\n",
    "    text=re.sub(r'[.]+','.',text)\n",
    "    text=re.sub(r\"'\",\"\",text)\n",
    "    text=re.sub(r\"\\(\",\"\",text)\n",
    "    text=re.sub(r\"\\)\",\"\",text)\n",
    "    text=\" \".join(text.split())\n",
    "    return text\n",
    "df_train['text'] = df_train['text'].apply(preprocess)\n",
    "df_test['text'] = df_test['text'].apply(preprocess)\n",
    "df_train=df_train[df_train[\"text\"]!='']"
   ]
  }
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