neural_model.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/m-barati/NLP_Shared_Task/blob/main/neural_model.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": "GDoYGqzAasXn",
        "outputId": "707cdbd9-8cd2-4c8d-b7c0-e8b4d8f323ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "--2022-05-14 08:20:13--  https://nlp.stanford.edu/data/glove.twitter.27B.zip\n",
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            "\n",
            "Archive:  glove.twitter.27B.zip\n",
            "  inflating: glove.twitter.27B.25d.txt  \n",
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            "  inflating: glove.twitter.27B.200d.txt  \n"
          ]
        }
      ],
      "source": [
        "####### IMPORTING AND DOWNLOADING THE NECESSARY LIBARARIES AND MODULES\n",
        "\n",
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!apt install git-lfs\n",
        "!pip install huggingface-hub\n",
        "!pip install -q -U keras-tuner\n",
        "!pip install emoji\n",
        "!wget https://nlp.stanford.edu/data/glove.twitter.27B.zip\n",
        "!unzip glove.twitter.27B.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RdmIvE4zAkK",
        "outputId": "c191b214-4ae2-4814-ef46-b521641d552a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import string\n",
        "import numpy as np\n",
        "import csv\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.optim import Adam\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "from tqdm import tqdm\n",
        "from imblearn import under_sampling\n",
        "\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import f1_score, classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import keras_tuner as kt\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.layers import LSTM, Dense, TimeDistributed, Embedding, Bidirectional, Input, SimpleRNN, GlobalMaxPool1D, Dropout, Conv1D, Flatten, BatchNormalization\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "\n",
        "import datasets\n",
        "from datasets import Dataset, load_metric, load_dataset\n",
        "\n",
        "from transformers import pipeline, BertTokenizer, BertModel, AutoTokenizer, AutoConfig\n",
        "\n",
        "import emoji \n",
        "\n",
        "from gensim.models.fasttext import FastText\n",
        "from gensim.test.utils import datapath, get_tmpfile\n",
        "from gensim.models import KeyedVectors\n",
        "from gensim.scripts.glove2word2vec import glove2word2vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
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        "outputId": "5e41cc73-c1ec-40ec-ccac-fd9214080504"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "from huggingface_hub import push_to_hub_keras\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2Dpp4JQs06Z-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb311817-c4fe-4969-bb70-598b329e1431"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "gD4K4f1c06_F"
      },
      "outputs": [],
      "source": [
        "data_path = 'gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/NeuralModel/olid-training-v1.0.tsv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FzPHXjUq1n8L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f8d14f4f-c97e-4665-9793-7935457a0b64"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               tweet label\n",
              "0  @USER She should ask a few native Americans wh...   OFF\n",
              "1  @USER @USER Go home you’re drunk!!! @USER #MAG...   OFF\n",
              "2  Amazon is investigating Chinese employees who ...   NOT\n",
              "3  @USER Someone should'veTaken\" this piece of sh...   OFF\n",
              "4  @USER @USER Obama wanted liberals &amp; illega...   NOT"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\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>tweet</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
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              "      <td>OFF</td>\n",
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              "      <td>Amazon is investigating Chinese employees who ...</td>\n",
              "      <td>NOT</td>\n",
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              "      <th>3</th>\n",
              "      <td>@USER Someone should'veTaken\" this piece of sh...</td>\n",
              "      <td>OFF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@USER @USER Obama wanted liberals &amp;amp; illega...</td>\n",
              "      <td>NOT</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-4ce90ac3-e9f2-45fa-a775-eadcd8df5b9c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4ce90ac3-e9f2-45fa-a775-eadcd8df5b9c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train = pd.read_csv(data_path, sep='\\t')\n",
        "train = train.drop(columns=['subtask_b', 'subtask_c', 'id'])\n",
        "train.rename(columns = {'tweet':'tweet', 'subtask_a':'label'}, inplace = True)\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "xhcqNS5QkcLE"
      },
      "outputs": [],
      "source": [
        "def remove_noise(data):\n",
        "  noise = '@USER'\n",
        "  noise2 = '&amp'\n",
        "  stops = set(stopwords.words('english'))\n",
        "  eyes = '[8:=;]'\n",
        "  nose = \"['`\\-~]\"\n",
        "  # remove URLs\n",
        "  data['tweet'] = [re.sub(\"(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\\\".,<>?«»“”‘’]))\", \"\", tweet) for tweet in data['tweet']]\n",
        "  # remove '@USER'\n",
        "  data['tweet'] = [tweet.replace(noise, '') for tweet in data['tweet']]\n",
        "  data['tweet'] = [tweet.replace(noise2, '') for tweet in data['tweet']]\n",
        "  # remove punctuation, except apostrophes and hashes. keeping 2+ punctuation (only !, because including ? and . here makes other punctuation remain as well, idk why!)\n",
        "  data['tweet'] = [re.sub(r'[^\\w\\d\\s\\'\\#\\!{2,}]+', '', tweet) for tweet in data['tweet']]\n",
        "  # remove digits\n",
        "  data['tweet'] = data['tweet'].apply(lambda x: ' '.join([word for word in x.split() if not word.isdigit()]))\n",
        "  # remove stopwords\n",
        "  data['tweet'] = data['tweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stops)]))\n",
        "  # convert non emoji smiley faces & emoticons to words\n",
        "  data['tweet'] = [re.sub(f'{eyes}{nose}*[)]]+|[[(c]+{nose}*{eyes}', 'smile', tweet) for tweet in data['tweet']]\n",
        "  data['tweet'] = [re.sub(f'{eyes}{nose}*[pP]+', 'lolface', tweet) for tweet in data['tweet']]\n",
        "  data['tweet'] = [re.sub(f'{eyes}{nose}*[(c\\/]+|[)\\/]+{nose}*{eyes}', 'sadface', tweet) for tweet in data['tweet']]\n",
        "  data['tweet'] = [re.sub(f'{eyes}{nose}*[\\|l]+', 'neutralface', tweet) for tweet in data['tweet']]\n",
        "  data['tweet'] = [re.sub(f'{eyes}{nose}*[oO$s@&]+', 'otherface', tweet) for tweet in data['tweet']]\n",
        "  data['tweet'] = [re.sub(r'(<3)', 'heart', tweet) for tweet in data['tweet']]\n",
        "  data['tweet'] = [re.sub(r'(</3)', 'brokenheart', tweet) for tweet in data['tweet']]\n",
        "  return data\n",
        "\n",
        "def emojis_to_text(data): \n",
        "  # converting emojis to text before tokenizing; one emoji remains one token\n",
        "  data['tweet'] = [emoji.demojize(tweet) for tweet in data['tweet']]\n",
        "  return data\n",
        "\n",
        "def transform(data):\n",
        "  data['tweet'] = [tweet.lower() for tweet in data['tweet']]\n",
        "\n",
        "def token_pad(data):\n",
        "  tokenizer = Tokenizer(num_words= 10000, oov_token = \"<oov>\")\n",
        "  tokenizer.fit_on_texts(data)\n",
        "  txt2seq = tokenizer.texts_to_sequences(data)\n",
        "  word_index = tokenizer.word_index\n",
        "  pad_seq = pad_sequences(txt2seq, maxlen = 100, truncating = 'pre', padding = 'post')\n",
        "  return pad_seq, word_index\n",
        "\n",
        "def preprocessor(data = train):\n",
        "  transform(emojis_to_text(remove_noise(data)))\n",
        "  return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ehMFMxWAWqLK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f551b995-70fd-4df2-f02b-30dd965e5b1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: FutureWarning: Possible nested set at position 21\n"
          ]
        }
      ],
      "source": [
        "preprocessor()\n",
        "\n",
        "X = train['tweet']\n",
        "train['label'] = np.where(train['label'] == 'NOT', 0, 1)\n",
        "y = train['label']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(token_pad(X)[0], y, test_size = 0.20, stratify = y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Parsing the vocabulary and embeddings using the 100-dimension embedding glove\n",
        "word_index = token_pad(X)[1]\n",
        "max_length = 100\n",
        "\n",
        "embeddings_index = {}\n",
        "with open('./glove.twitter.27B.100d.txt','rt') as file: # Opening the file in Reading Text (rt) mode\n",
        "  for line in file:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "\n",
        "\n",
        "print('Found %s word vectors.' % len(embeddings_index))\n",
        "\n",
        "embedding_matrix = np.zeros((len(word_index) + 1, max_length))\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # words not found in embedding index will be all-zeros\n",
        "        embedding_matrix[i] = embedding_vector"
      ],
      "metadata": {
        "id": "hEcD6QTjXmUH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2996894b-3784-47ad-94a3-4fdf48196e3a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1193514 word vectors.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"def model_builder():\n",
        "  input = Input(shape = (max_length, ))\n",
        "  # create glove embedding layer\n",
        "  embedding_layer = Embedding(input_dim = len(word_index) + 1,\n",
        "                            output_dim = max_length,\n",
        "                            weights = [embedding_matrix],\n",
        "                            input_length = max_length,\n",
        "                            trainable = False)(input)\n",
        "  lstm1 = LSTM(units = 100, return_sequences = True)(embedding_layer)\n",
        "  drop1 = Dropout(0.6)(lstm1)\n",
        "  lstm2 = LSTM(units = 50, return_sequences = False)(drop1)\n",
        "  drop2 = Dropout(0.6)(lstm2)\n",
        "  flat = Flatten()(drop2)\n",
        "  dense = Dense(units = 8, activation = 'relu')(flat)\n",
        "  out = Dense(units = 1, activation = 'sigmoid')(dense)\n",
        "  model = Model(inputs = input, outputs = out)\n",
        "\n",
        "  model.compile(optimizer = keras.optimizers.Adam(learning_rate = 0.1),\n",
        "                loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "                metrics = ['accuracy'])\n",
        "  \n",
        "  return model\"\"\""
      ],
      "metadata": {
        "id": "FsU2IdQdZCHh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model_builder(hp):\n",
        "  # create glove embedding layer\n",
        "  embedding_layer = Embedding(input_dim=len(word_index) + 1,\n",
        "                            output_dim=max_length,\n",
        "                            weights=[embedding_matrix],\n",
        "                            input_length=max_length,\n",
        "                            trainable=False)\n",
        "  \n",
        "\n",
        "  # tune number of units in the first Dense layer\n",
        "  # choose  optimal value between 32-480\n",
        "  hp_units = hp.Int('units', min_value=32, max_value=480, step=64)\n",
        "\n",
        "  # create model\n",
        "  model = Sequential([\n",
        "    embedding_layer,\n",
        "    Bidirectional(LSTM(units=hp_units, return_sequences=True, recurrent_dropout=0.2)),\n",
        "    GlobalMaxPool1D(),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.5),\n",
        "    Dense(units=hp_units, activation = \"relu\"),\n",
        "    Dropout(0.5),\n",
        "    Dense(16, activation = \"relu\"),\n",
        "    Dropout(0.5),\n",
        "    Dense(2, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "\n",
        "  # tune  learning rate for  optimizer\n",
        "  # choose  optimal value from 0.01, or 0.001\n",
        "  hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3])\n",
        "\n",
        "  model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "                metrics=['accuracy'])\n",
        "  \n",
        "  return model"
      ],
      "metadata": {
        "id": "qBa-foXiDYmW"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuner = kt.Hyperband(model_builder,\n",
        "                     objective='val_accuracy',\n",
        "                     factor=3,\n",
        "                     max_epochs=4)\n",
        "\n",
        "stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
        "\n",
        "tuner.search(X_train, y_train, epochs=10, validation_split=0.2, callbacks=[stop_early])\n",
        "\n",
        "# Get the optimal hyperparameters\n",
        "best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]\n",
        "\n",
        "print(f\"\"\"\n",
        "The hyperparameter search is complete. The optimal number of units in the first densely-connected\n",
        "layer is {best_hps.get('units')} and the optimal learning rate for the optimizer\n",
        "is {best_hps.get('learning_rate')}.\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "0Dbr2sVzDkBU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75b407f8-7f33-4dda-994e-7ae932fc53b5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 10 Complete [00h 02m 42s]\n",
            "val_accuracy: 0.7635677456855774\n",
            "\n",
            "Best val_accuracy So Far: 0.7753657102584839\n",
            "Total elapsed time: 03h 00m 20s\n",
            "INFO:tensorflow:Oracle triggered exit\n",
            "\n",
            "The hyperparameter search is complete. The optimal number of units in the first densely-connected\n",
            "layer is 160 and the optimal learning rate for the optimizer\n",
            "is 0.001.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tuner.hypermodel.build(best_hps)\n",
        "history = model.fit(X_train, y_train, epochs=30, validation_split=0.2)\n",
        "\n",
        "valid_acc = history.history['val_accuracy']\n",
        "best_epoch = valid_acc.index(max(valid_acc)) + 1\n",
        "print('Best epoch: %d' % (best_epoch,))"
      ],
      "metadata": {
        "id": "DzdjigrVD1MU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efade95b-3304-4274-cefd-349ee8d22304"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "265/265 [==============================] - 176s 640ms/step - loss: 0.6784 - accuracy: 0.6491 - val_loss: 0.6423 - val_accuracy: 0.6692\n",
            "Epoch 2/30\n",
            "265/265 [==============================] - 182s 688ms/step - loss: 0.5674 - accuracy: 0.7018 - val_loss: 0.5155 - val_accuracy: 0.7291\n",
            "Epoch 3/30\n",
            "265/265 [==============================] - 177s 669ms/step - loss: 0.5335 - accuracy: 0.7263 - val_loss: 0.4760 - val_accuracy: 0.7673\n",
            "Epoch 4/30\n",
            "265/265 [==============================] - 200s 755ms/step - loss: 0.5088 - accuracy: 0.7458 - val_loss: 0.4872 - val_accuracy: 0.7763\n",
            "Epoch 5/30\n",
            "265/265 [==============================] - 188s 710ms/step - loss: 0.5054 - accuracy: 0.7500 - val_loss: 0.4699 - val_accuracy: 0.7820\n",
            "Epoch 6/30\n",
            "265/265 [==============================] - 205s 773ms/step - loss: 0.4901 - accuracy: 0.7573 - val_loss: 0.5274 - val_accuracy: 0.7655\n",
            "Epoch 7/30\n",
            "265/265 [==============================] - 247s 933ms/step - loss: 0.4825 - accuracy: 0.7624 - val_loss: 0.6052 - val_accuracy: 0.7183\n",
            "Epoch 8/30\n",
            "265/265 [==============================] - 203s 765ms/step - loss: 0.4876 - accuracy: 0.7674 - val_loss: 0.4610 - val_accuracy: 0.7763\n",
            "Epoch 9/30\n",
            "265/265 [==============================] - 174s 657ms/step - loss: 0.4745 - accuracy: 0.7708 - val_loss: 0.4601 - val_accuracy: 0.7876\n",
            "Epoch 10/30\n",
            "265/265 [==============================] - 172s 648ms/step - loss: 0.4659 - accuracy: 0.7732 - val_loss: 0.4631 - val_accuracy: 0.7876\n",
            "Epoch 11/30\n",
            "265/265 [==============================] - 174s 657ms/step - loss: 0.4620 - accuracy: 0.7765 - val_loss: 0.4614 - val_accuracy: 0.7782\n",
            "Epoch 12/30\n",
            "265/265 [==============================] - 169s 638ms/step - loss: 0.4519 - accuracy: 0.7845 - val_loss: 0.4594 - val_accuracy: 0.7801\n",
            "Epoch 13/30\n",
            "265/265 [==============================] - 169s 637ms/step - loss: 0.4442 - accuracy: 0.7865 - val_loss: 0.4651 - val_accuracy: 0.7758\n",
            "Epoch 14/30\n",
            "265/265 [==============================] - 170s 640ms/step - loss: 0.4349 - accuracy: 0.7900 - val_loss: 0.4995 - val_accuracy: 0.7518\n",
            "Epoch 15/30\n",
            "265/265 [==============================] - 170s 641ms/step - loss: 0.4302 - accuracy: 0.7920 - val_loss: 0.4983 - val_accuracy: 0.7456\n",
            "Epoch 16/30\n",
            "265/265 [==============================] - 170s 643ms/step - loss: 0.4336 - accuracy: 0.7916 - val_loss: 0.4874 - val_accuracy: 0.7725\n",
            "Epoch 17/30\n",
            "265/265 [==============================] - 171s 644ms/step - loss: 0.4206 - accuracy: 0.8040 - val_loss: 0.4925 - val_accuracy: 0.7872\n",
            "Epoch 18/30\n",
            "265/265 [==============================] - 170s 643ms/step - loss: 0.4175 - accuracy: 0.8166 - val_loss: 0.5748 - val_accuracy: 0.7518\n",
            "Epoch 19/30\n",
            "265/265 [==============================] - 168s 635ms/step - loss: 0.3979 - accuracy: 0.8304 - val_loss: 0.4769 - val_accuracy: 0.7773\n",
            "Epoch 20/30\n",
            "265/265 [==============================] - 167s 631ms/step - loss: 0.3845 - accuracy: 0.8338 - val_loss: 0.5511 - val_accuracy: 0.7744\n",
            "Epoch 21/30\n",
            "265/265 [==============================] - 171s 644ms/step - loss: 0.3852 - accuracy: 0.8384 - val_loss: 0.4849 - val_accuracy: 0.7763\n",
            "Epoch 22/30\n",
            "265/265 [==============================] - 170s 641ms/step - loss: 0.3607 - accuracy: 0.8448 - val_loss: 0.5711 - val_accuracy: 0.7848\n",
            "Epoch 23/30\n",
            "265/265 [==============================] - 169s 636ms/step - loss: 0.3564 - accuracy: 0.8503 - val_loss: 0.5282 - val_accuracy: 0.7721\n",
            "Epoch 24/30\n",
            "265/265 [==============================] - 169s 636ms/step - loss: 0.3472 - accuracy: 0.8538 - val_loss: 0.5249 - val_accuracy: 0.7296\n",
            "Epoch 25/30\n",
            "265/265 [==============================] - 168s 635ms/step - loss: 0.3304 - accuracy: 0.8616 - val_loss: 0.5648 - val_accuracy: 0.7782\n",
            "Epoch 26/30\n",
            "265/265 [==============================] - 168s 633ms/step - loss: 0.3125 - accuracy: 0.8688 - val_loss: 0.5553 - val_accuracy: 0.7669\n",
            "Epoch 27/30\n",
            "265/265 [==============================] - 169s 637ms/step - loss: 0.3051 - accuracy: 0.8744 - val_loss: 0.6625 - val_accuracy: 0.7867\n",
            "Epoch 28/30\n",
            "265/265 [==============================] - 169s 636ms/step - loss: 0.3056 - accuracy: 0.8735 - val_loss: 0.5825 - val_accuracy: 0.7673\n",
            "Epoch 29/30\n",
            "265/265 [==============================] - 169s 639ms/step - loss: 0.2958 - accuracy: 0.8827 - val_loss: 0.6265 - val_accuracy: 0.7796\n",
            "Epoch 30/30\n",
            "265/265 [==============================] - 170s 641ms/step - loss: 0.2803 - accuracy: 0.8894 - val_loss: 0.5966 - val_accuracy: 0.7730\n",
            "Best epoch: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hypermodel = tuner.hypermodel.build(best_hps)\n",
        "\n",
        "# Retrain the model\n",
        "hypermodel.fit(X_train, y_train, epochs=15, validation_split=0.2)"
      ],
      "metadata": {
        "id": "sQA23y9-D6L2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "973ea2d1-6209-4ada-ffa3-d59f091baeb8"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "265/265 [==============================] - 178s 640ms/step - loss: 0.6968 - accuracy: 0.6372 - val_loss: 0.6161 - val_accuracy: 0.6786\n",
            "Epoch 2/15\n",
            "265/265 [==============================] - 168s 635ms/step - loss: 0.5659 - accuracy: 0.7137 - val_loss: 0.5023 - val_accuracy: 0.7537\n",
            "Epoch 3/15\n",
            "265/265 [==============================] - 172s 650ms/step - loss: 0.5380 - accuracy: 0.7288 - val_loss: 0.4831 - val_accuracy: 0.7716\n",
            "Epoch 4/15\n",
            "265/265 [==============================] - 172s 647ms/step - loss: 0.5084 - accuracy: 0.7532 - val_loss: 0.4749 - val_accuracy: 0.7706\n",
            "Epoch 5/15\n",
            "265/265 [==============================] - 172s 650ms/step - loss: 0.5031 - accuracy: 0.7538 - val_loss: 0.4714 - val_accuracy: 0.7754\n",
            "Epoch 6/15\n",
            "265/265 [==============================] - 174s 657ms/step - loss: 0.4862 - accuracy: 0.7680 - val_loss: 0.5123 - val_accuracy: 0.7787\n",
            "Epoch 7/15\n",
            "265/265 [==============================] - 172s 650ms/step - loss: 0.4888 - accuracy: 0.7657 - val_loss: 0.4651 - val_accuracy: 0.7834\n",
            "Epoch 8/15\n",
            "265/265 [==============================] - 173s 654ms/step - loss: 0.4678 - accuracy: 0.7749 - val_loss: 0.4821 - val_accuracy: 0.7716\n",
            "Epoch 9/15\n",
            "265/265 [==============================] - 172s 651ms/step - loss: 0.4649 - accuracy: 0.7810 - val_loss: 0.4735 - val_accuracy: 0.7763\n",
            "Epoch 10/15\n",
            "265/265 [==============================] - 170s 643ms/step - loss: 0.4503 - accuracy: 0.7900 - val_loss: 0.4752 - val_accuracy: 0.7815\n",
            "Epoch 11/15\n",
            "265/265 [==============================] - 171s 646ms/step - loss: 0.4483 - accuracy: 0.7930 - val_loss: 0.4764 - val_accuracy: 0.7791\n",
            "Epoch 12/15\n",
            "265/265 [==============================] - 169s 639ms/step - loss: 0.4384 - accuracy: 0.7975 - val_loss: 0.4876 - val_accuracy: 0.7857\n",
            "Epoch 13/15\n",
            "265/265 [==============================] - 173s 652ms/step - loss: 0.4299 - accuracy: 0.8051 - val_loss: 0.4898 - val_accuracy: 0.7758\n",
            "Epoch 14/15\n",
            "265/265 [==============================] - 170s 642ms/step - loss: 0.4206 - accuracy: 0.8030 - val_loss: 0.5038 - val_accuracy: 0.7631\n",
            "Epoch 15/15\n",
            "265/265 [==============================] - 171s 646ms/step - loss: 0.4140 - accuracy: 0.8067 - val_loss: 0.4781 - val_accuracy: 0.7839\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f897d9e4e10>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "push_to_hub_keras(hypermodel, \"SharedTask_Group5_GloVeEmbeddings\")"
      ],
      "metadata": {
        "id": "8Jj-ndRgFhtt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "c48cca87-5821-4896-a5ce-2a1af32540c1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "HTTPError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-2249906421ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpush_to_hub_keras\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhypermodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"SharedTask_Group5_GloVeEmbeddings\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/huggingface_hub/keras_mixin.py\u001b[0m in \u001b[0;36mpush_to_hub_keras\u001b[0;34m(model, repo_path_or_name, repo_url, log_dir, commit_message, organization, private, api_endpoint, use_auth_token, git_user, git_email, config, include_optimizer, tags, plot_model, **model_save_kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m             \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprivate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m             \u001b[0mrepo_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m             \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    398\u001b[0m         )\n\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     30\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             args_msg = [\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, organization, private, repo_type, exist_ok, space_sdk, name)\u001b[0m\n\u001b[1;32m   1493\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1495\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1497\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, organization, private, repo_type, exist_ok, space_sdk, name)\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1484\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1485\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexist_ok\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m409\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 941\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    942\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create - You don't have the rights to create a model under this namespace"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_acc = history.history['accuracy']\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']"
      ],
      "metadata": {
        "id": "rrj0HBHxUff9"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig=plt.figure(figsize=(10,10))\n",
        "fig.add_subplot(2, 1, 1)\n",
        "plt.title('Loss')\n",
        "plt.grid()\n",
        "plt.plot(train_loss, color='blue', label='Train')\n",
        "plt.plot(val_loss, color='red', label='Validation')\n",
        "plt.legend()\n",
        "# plot accuracy\n",
        "fig.add_subplot(2, 1, 2)\n",
        "plt.title('Classification Accuracy')\n",
        "plt.grid()\n",
        "plt.plot(train_acc, color='blue', label='Train')\n",
        "plt.plot(valid_acc, color='red', label='Validation')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "Cg3UUXZHUfdG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "dfa523b7-c9d2-4505-b262-42a179e23d5b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8975549a50>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate = hypermodel.evaluate(X_test, y_test)\n",
        "\n",
        "print(\"Test Acuracy is : {:.2f} %\".format(evaluate[1]*100))\n",
        "print(\"Test Loss is : {:.4f}\".format(evaluate[0]))"
      ],
      "metadata": {
        "id": "Fec1xZylUfXO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d995a17-c170-4f8a-b31d-a01479a4d265"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 8s 90ms/step - loss: 0.4809 - accuracy: 0.7915\n",
            "Test Acuracy is : 79.15 %\n",
            "Test Loss is : 0.4809\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = hypermodel.predict(X_test)\n",
        "\n",
        "predict = []\n",
        "for i in predictions:\n",
        "    predict.append(np.argmax(i))\n",
        "\n",
        "print(predict)"
      ],
      "metadata": {
        "id": "PQfcMLbZqAfS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad3ac4de-cc38-4858-bb03-a086a9a273f9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(predict, y_test)\n",
        "acc = accuracy_score(predict, y_test)\n",
        "\n",
        "print(\"The Confusion matrix is: \\n\",cm)\n",
        "print(metrics.classification_report(y_test, predict))"
      ],
      "metadata": {
        "id": "O3YUyesJqAYT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "391a40ae-d432-428f-997c-bf43c092d9eb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Confusion matrix is: \n",
            " [[1622  406]\n",
            " [ 146  474]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.92      0.85      1768\n",
            "           1       0.76      0.54      0.63       880\n",
            "\n",
            "    accuracy                           0.79      2648\n",
            "   macro avg       0.78      0.73      0.74      2648\n",
            "weighted avg       0.79      0.79      0.78      2648\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def produce_results(filename, hypermodel = hypermodel):\n",
        "  test = pd.read_csv(filename)\n",
        "  preprocessor(data = test)\n",
        "  id = test['id'].to_list()\n",
        "  data = test['tweet']\n",
        "  preds = hypermodel.predict(token_pad(data)[0])\n",
        "  label = []\n",
        "  for pred in preds:\n",
        "    label.append(np.argmax(pred))\n",
        "  name, ext = os.path.splitext(os.path.basename(filename))\n",
        "  out_file = '/content/gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/Results/' + name + '_neural' + ext\n",
        "  output = pd.DataFrame(list(zip(id, label)), columns = ['id', 'label'])\n",
        "  output['label'] = np.where(output['label'] == 0, 'NOT', 'OFF')\n",
        "  output.to_csv(out_file, sep=',', encoding='utf-8', index = False)\n",
        "  gold_file_path = '/content/gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/Results/' + name + '_gold' + ext\n",
        "  gold = pd.read_csv(gold_file_path, sep=',')\n",
        "  y_pred = output['label']\n",
        "  y_test = gold['label']\n",
        "  print(\"Accuracy: {}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "  print(classification_report(y_test, y_pred))\n",
        "  cm = confusion_matrix(y_test, y_pred)\n",
        "  print('Confusion Matrix for ' + name + ': \\n' + str(cm))\n",
        "  ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
        "  plot = plt.show()\n",
        "  return"
      ],
      "metadata": {
        "id": "MMAiKaHYZ03m"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "produce_results(filename = 'gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/NeuralModel/test_IN.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "zI8a1P7rasKS",
        "outputId": "e5cb796f-be63-492d-c097-b0d76f3b556b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 62.2093023255814%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NOT       0.73      0.75      0.74       620\n",
            "         OFF       0.31      0.30      0.30       240\n",
            "\n",
            "    accuracy                           0.62       860\n",
            "   macro avg       0.52      0.52      0.52       860\n",
            "weighted avg       0.62      0.62      0.62       860\n",
            "\n",
            "Confusion Matrix for test_IN: \n",
            "[[464 156]\n",
            " [169  71]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "produce_results(filename = 'gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/NeuralModel/test_OUT1.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "HmsZO9nnaxZX",
        "outputId": "e8b79fbd-ce9d-4052-965e-52eefdd4d2b6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 54.681027340513666%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NOT       0.57      0.76      0.65       664\n",
            "         OFF       0.49      0.29      0.36       543\n",
            "\n",
            "    accuracy                           0.55      1207\n",
            "   macro avg       0.53      0.52      0.51      1207\n",
            "weighted avg       0.53      0.55      0.52      1207\n",
            "\n",
            "Confusion Matrix for test_OUT1: \n",
            "[[503 161]\n",
            " [386 157]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "produce_results(filename = 'gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/NeuralModel/test_OUT2.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "fXPyyWFeazNW",
        "outputId": "bf754b4f-8939-4e4a-dd9d-35491430e6c0"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 40.75%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NOT       0.43      0.55      0.48       600\n",
            "         OFF       0.37      0.26      0.31       600\n",
            "\n",
            "    accuracy                           0.41      1200\n",
            "   macro avg       0.40      0.41      0.39      1200\n",
            "weighted avg       0.40      0.41      0.39      1200\n",
            "\n",
            "Confusion Matrix for test_OUT2: \n",
            "[[331 269]\n",
            " [442 158]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "produce_results(filename = 'gdrive/MyDrive/ColabNotebooks/NLP_Files/SharedFolder/NLP/NeuralModel/test_TEXTGAIN.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "wB7eKZfQazwX",
        "outputId": "5b65756a-07a8-4efa-f73a-05dfef4e8501"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 64.57680250783699%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NOT       0.84      0.72      0.78      1088\n",
            "         OFF       0.11      0.21      0.15       188\n",
            "\n",
            "    accuracy                           0.65      1276\n",
            "   macro avg       0.48      0.46      0.46      1276\n",
            "weighted avg       0.73      0.65      0.68      1276\n",
            "\n",
            "Confusion Matrix for test_TEXTGAIN: \n",
            "[[785 303]\n",
            " [149  39]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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pGj41vbt27Up2dnadso8//pjExMRTupiIyLFM07fNaj5VCUeNGsWzzz7LqlWrqKqq4p577iE0NLROd72IyClrBEnQFz4lyujoaJ555hl+/PFH9u3bR0xMDBdeeCE2W9OYIklEGqdm9663aZrU1tYC4HK56i0gEWlBmlON8ueffyYzM5OamhrsdjsOh4OgoCAmTJjA+eefX88hikhzZfqh17sh+JQoX375ZQYMGMDAgQMxDAPTNFm2bBkvv/wyzz77bH3HKCLNVtNIlD49ZNy7dy9paWnuV4AMw+C6666jqKioXoMTkWbO9HGzmE+JMikpifXr19cpW79+PUlJSfUSlIi0EE0kUfo0zZrL5WLatGkkJCQQExNDaWkphYWFJCcnN1igItIMNfVe719Ps3bOOee4f46Pj+c3v/lN/UUlIi1CYxhM7gtNsyYi1mlOvd5w9N3uPXv2sH///jrlmmZNRE6X0dRrlMf64YcfeOGFF6ipqeHw4cOEhoZSVVVFTEwMM2fOrO8YRaS5ak6J8rXXXmPQoEEMHDiQ//7v/2b+/PksXryY4ODg+o5PRJqzJtKZ49PwoD179nDdddfVKRs8eDDLli2rl6BEpIVoIsODfEqUYWFh7hmEo6Ki2L17NwcPHqSqqqpegxORZs7l42Yxn5reV1xxBfn5+fTt25errrqKxx57jICAAHr16lXf8YlIc9ZEmt4+JcqRI0e6fx40aBCdOnWiqqpKYylF5Iw0q17vX+vatau/4xCRlqipJ8rJkyf7tA7uY4895teAREQam5Mmyquvvroh4/Cr36cNtTqEejHzjRjuHt787i0u/0urQ6g3QSMGETerGd7fjPF+OY0/m94ul4tJkyZht9uZNGkSxcXFTJs2jQMHDpCQkMDYsWMJDAykpqaGmTNnUlhYSJs2bbj33nuJi4vzeO6TJsr+/fv77w5ERE7Ej68wLl++nA4dOrhH6Lz55pukpaXRp08f/va3v5GTk8O1115LTk4OrVu3ZsaMGaxdu5aFCxe6l7I9GS16IyLW8dM4ytLSUjZu3Mg111xz9LSmyZYtW9wjc/r3709eXh5wdIrIXyqCvXr1YvPmzZheZudQohQRyximb5s3CxYsYNiwYe5+lQMHDhAWFkZAQACAewkbAIfDQUxMDAABAQGEhYVx4MABj+c/rV5vERG/OIVnlMcuj52amkpqaioAGzZsIDIykoSEBLZs2eLvCAElShGx0ikkyilTppywfNu2baxfv578/HyOHDnC4cOHWbBgAZWVlTidTgICAnA4HNjtduBo7bK0tJSYmBicTieVlZW0adPG47V9SpQ1NTUsXryYtWvXcuDAAV577TW+/fZb9u7dy+9//3vf71RE5Bj+6PUeMmQIQ4YMAWDLli0sWbKEcePG8cILL7Bu3Tr69OnD6tWr3SsyXHbZZaxevZrOnTuzbt06EhMTvQ6F9OkZ5WuvvcauXbsYN26c+4TnnHMOn3322Zncn4i0dC7Dt+00DB06lKVLlzJ27FgOHjzoHvJ49dVXc/DgQcaOHcvSpUsZOtT7kDufapRff/0106dPJyQkxJ0oj304KiJyOvz9CmNiYiKJiYkAtGvXjmeeeea4Y4KDg7nvvvtO6bw+JcrAwEBcrrpTeOzfv99ru15ExKMm8gqjT03vXr16MXPmTIqLiwEoKyvj1Vdf5be//W29BicizZu/hgfVN58S5ZAhQ4iLi+P++++nsrKScePGER0drQXIROTMNJGJe31ueo8cOZKRI0e6m9y+TJghIuKJ0Qgm5fWFT4nyP//5T53ff3mXEo4+MBURac58SpTjxo076b533nnHb8GISAvTCJrVvvApUf46GZaXl/Pee+9pAl8ROSONoaPGF6c1KUZUVBQjR47krbfe8nc8ItKSNKfOnBPZs2cP1dXV/oxFRFqaRpAEfeFTovz1shDV1dXs2rWLP/7xj/UWmIg0f82q1/vXy0KEhIRw3nnncdZZZ9VLUCLSMjSVZ5ReE6XL5WLz5s3ceeedBAUFNURMItJSNJdEabPZ2LRpkwaYi4j/NZFE6VOvd1paGu+++y61tbX1HY+ItCBN5V1vjzXKL774gr59+/LJJ59QXl7OsmXLiIiIqHPMyy+/XK8Bikgz1giSoC88JspXXnmFvn37Mnbs2IaKR0RakGbR6/3LEo4XX3xxgwQjIi1Mc6hR/tLj7ckll1zi14BEpOVoDM8ffeExUdbU1DB79uyTLg5uGAYzZ86sl8BEpAVoDokyJCREiVBE6k9zSJQiIvWpWTS9T9bkFhHxB38kyiNHjvDII49QW1uL0+mkV69epKenU1xczLRp0zhw4AAJCQmMHTuWwMBAampqmDlzJoWFhbRp04Z7772XuLg4j9fwOOD89ddfP/O7EBE5GT9MsxYUFMQjjzxCZmYmU6dO5ZtvvqGgoIA333yTtLQ0ZsyYQevWrcnJyQEgJyeH1q1bM2PGDNLS0li4cKHXME9rPkoREb/wQ6I0DIOQkBAAnE4nTqcTwzDYsmULvXr1AqB///7k5eUBsH79evr37w8cXWF28+bNXlvPekYpIpY5lab3pEmT3D+npqaSmprq/t3lcvHggw9SVFTEgAEDaNeuHWFhYQQEBABgt9txOBwAOBwOYmJiAAgICCAsLIwDBw4c99bhsZQoRcQ6p5Aop0yZctJ9NpuNzMxMDh06xHPPPceePXv8ENwx5/fr2UREToHh8m3zVevWrUlMTKSgoIDKykqcTidwtBZpt9uBo7XL0tJS4GhTvbKykjZt2ng8rxKliFjGH7MH7d+/n0OHDgFHe8A3bdpEhw4dSExMZN26dQCsXr2a5ORkAC677DJWr14NwLp160hMTPQ6jaSa3iJiHT8MDyorK2PWrFm4XC5M06R3795cdtllxMfHM23aNBYtWsQFF1zgXqnh6quvZubMmYwdO5bw8HDuvfder9dQohQR6/ghUZ533nlMnTr1uPJ27drxzDPPHFceHBzMfffdd0rXUKIUEcs0izdzRETqk+FqGplSiVJErNM08qQSpYhYR01vERFvlChFRDxTjVJExBslShERz5rFKowiIvVJTW8REW+ayCoKSpQiYhnVKOWUjL9nHVdc/m/Ky0O4a0xanX033rCVO0bnU1iWDkB4+BHG37OOs886yJEjNl54sRc//xxlRdjyv4JauXj+gx0EBZsEBJr8Y1kUbzzXntBW3zDz0wKCgky2bwrlhfvPweX0PFNNi6JEeWKlpaW8+uqr7N69G9M06dGjB8OHD2fbtm1MnTrVvchPREQEDz/8MO+++y6rVq1yzz7cvXt3hg4d2tBh17sVKxNYsrQzE+77Z53y2NhDXJa0l/8Uh0HQ0bI/pW+hsDCaJ57qR3x8BWP+vJ7/eegaC6KWX9RUGzxwU0eqKgMICDR5IXsHG1a3IS76ee684Tz+XdiKWycW8bt0B5++HWN1uI2GOnNOwDRNnnvuOa699loeeOABXC4Xc+bM4e2336ZHjx507dq1znTvv0hLS2PQoEENGWqD27wljnZxB48rv/P2jcydn8SjD69xl517bgXvvHcxALt3R9Ku3SGiog5TXh7aYPHKrxlUVR5ddiAwyCQgyOTonLGB/LuwFQAbc8O5eWyxEuUxmkqibNCJezdv3kxwcDBXXXXV0YvbbIwYMYLPP/+c6urqhgylSejVazelpaHs3Bldp7ywMIo+v90FQOfOJbSLO0Rs7GErQpRj2GwmL63YxjubtpC/Jpxt+WGAi06XVgLQd2AFbc+usTbIxsY0fdssZpgNuHj38uXLKS4uZuTIkXXKH3jgAfr3788777zjbnr37t2bG2+88bim99ChQ+nevXudz69cuZKVK1cCR9fVKPjev+tlNJRA217OCv8fdu1fgEEVHSLGs+dAJi4znPMib8aMWsy/fnZicIi2rWfQKmA71c4EggP+RfGhCRxxdrL6Fk5PZfNK8jbjIO1jnqCk/M+clRBObdkUDKOGyqoetA75ml3Fs6wO8Yx1Tu7ol/P0/cNzPh33xfsT/HK909WoOnNOt+n96xXZ7h7+t3qJr761izvIY4+UcfeYv3H+eeVMebqQ1kduBsBGJa7yP/DwuD6UlYUCduAKwOS1eV9y75jPqDz8uZXhnzYzf4vVIfjd0PH/ofrw41w1ah5jLg8EAumR8jX/dUspT911/H/jTc0K13v+OZH1lUWfNGjTOz4+np07d9Ypq6yspKSkhPbt2zdkKI3eTz9H8aehf2DEqOsZMep6SkrC2LX/b5SVhdK69RECA48umvT7AT/y3ea2VB4Osjjili3SXkvriKPfSXCIix79DrJrRwgBtnIAgoJdpGcUs/QNPZ88lj/WzGkIDVqj7NatG2+99Ra5ubmkpKTgcrl4/fXX6d+/P61atWrIUBqdSQ+s5dJu/yEiopo3Xvs7by68lE8/O3Hz5txzKrj/vnVgws//iiTrxV4NHK38mr1dDRNe/Bc2G9hssGZJJF+tjGB8m8W8kvsDhg2WvRbDt2s9r/bX0mji3hMwDIMJEyYwd+5c3n//fUzTJCkpiVtuuYWCgoKGDKXRmTK1j8f9I0Zdz8w3jo6V3PpDW0bf8f8aIizx0c6toYy59qLjyksrRjMmZYcFETURTSNPNvwzytjY2BM+h0xMTCQxMfG48vT09IYIS0Qs4I9mdUlJCbNmzaK8vBzDMEhNTeW6667j4MGDZGVlsW/fPtq2bcv48eMJDw/HNE3mz59Pfn4+rVq1IiMjg4SEBI/X0LreImIdl+nb5kFAQADDhw8nKyuLp556ik8//ZTdu3eTnZ1Nt27dmD59Ot26dSM7OxuA/Px8ioqKmD59OnfccQdz5871GqYSpYhYx/Rx8yA6OtpdIwwNDaVDhw44HA7y8vJISUkBICUlhby8PADWr19Pv379MAyDzp07c+jQIcrKyjxeo1ENDxKRluVUmt7HPrL79ZDAXxQXF7Nz504uvPBCKioqiI4++rJGVFQUFRUVADg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