fake_news_semi_supervised_self_training_naive_bayes.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/safa-abidi/NLP-sentimentAnalysis-AraBert/blob/main/fake_news_semi_supervised_self_training_naive_bayes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "L0D-38rHTB7k"
      },
      "id": "L0D-38rHTB7k"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "523f70b1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "import plotly.express as px\n",
        "\n",
        "import xml.etree.ElementTree as ET\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.svm import SVC\n",
        "from sklearn.semi_supervised import SelfTrainingClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import f1_score"
      ],
      "id": "523f70b1"
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Any, Dict, List, Callable, Optional, Tuple, Union\n",
        "import numpy as np\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "import torch"
      ],
      "metadata": {
        "id": "mxik8dt3S8RE"
      },
      "id": "mxik8dt3S8RE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "id": "ENOVRcdlVV00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcf5dd4e-c244-4edb-da24-a2d8bba4aa15"
      },
      "id": "ENOVRcdlVV00",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.27.3-py3-none-any.whl (6.8 MB)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.24.2)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.3 tokenizers-0.13.2 transformers-4.27.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from transformers import BertModel, BertTokenizer\n",
        "import torch\n",
        "import transformers"
      ],
      "metadata": {
        "id": "dnnqHVVPVWJe"
      },
      "id": "dnnqHVVPVWJe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive') "
      ],
      "metadata": {
        "id": "0PGSFbx9XXcl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db094dd5-9c1f-4c98-b9e3-07eb74435d44"
      },
      "id": "0PGSFbx9XXcl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset: labelled and unlabled"
      ],
      "metadata": {
        "id": "XbMQ_lKBSR9k"
      },
      "id": "XbMQ_lKBSR9k"
    },
    {
      "cell_type": "code",
      "source": [
        "DATASETS_PATH = '/content/drive/MyDrive/'\n",
        "\n",
        "%cd /content/drive/MyDrive/datasets\n",
        "\n",
        "# Labeled Dataset\n",
        "labeled_dataset_path = 'dataset_arabic_translated/Merged_Labelled_Arabic_fake_news_Dataset.xlsx'\n",
        "labeled_dataset = pd.read_excel(labeled_dataset_path)\n",
        "\n",
        "# Unlabeled Dataset\n",
        "unlabeled_dataset_path = 'SemEval2016_arabic/AR_Hotels_Train_SB1.xml'\n",
        "tree = ET.parse(unlabeled_dataset_path)\n",
        "root = tree.getroot()\n",
        "\n",
        "# Extract reviews from XML and add to list of tuples\n",
        "reviews = []\n",
        "for review in root.iter('Review'):\n",
        "    for sentence in review.iter('sentence'):\n",
        "        text = sentence.find('text').text.strip()\n",
        "        label = -1\n",
        "        reviews.append((text, label))\n",
        "\n",
        "# Create a pandas DataFrame from the list of tuples\n",
        "unlabeled_dataset = pd.DataFrame(reviews, columns=['Review', 'Class'])\n",
        "print(\"Unlabeled Dataset\",unlabeled_dataset.shape)\n"
      ],
      "metadata": {
        "id": "PTNtkC8bVxiT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54c45392-13b8-4f4a-9590-5553e1bc36eb"
      },
      "id": "PTNtkC8bVxiT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1yQmug2Km3lcyLzCJRQKL27azhrH4dHz5/datasets\n",
            "Unlabeled Dataset (4802, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data split labeled_training and test\n",
        "df_train_labeled, df_test = train_test_split(labeled_dataset, train_size=0.7, random_state=0)\n",
        "print('Size of train dataframe: ', df_train_labeled.shape[0])\n",
        "print('Size of test dataframe: ', df_test.shape[0])\n",
        "\n"
      ],
      "metadata": {
        "id": "ry29_UAiV20-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9018921a-cedf-4666-e82f-dd407f799a8e"
      },
      "id": "ry29_UAiV20-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of train dataframe:  1120\n",
            "Size of test dataframe:  480\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.concat([df_train_labeled, unlabeled_dataset], axis=0)\n",
        "\n",
        "# Reset the index of the concatenated DataFrame\n",
        "df_train = df_train.reset_index(drop=True)\n",
        "\n",
        "## Encode the dataset labels\n",
        "df_test['Label'] =  df_test.apply(lambda x: 0 if x['Class'] == 'خادع' else 1 if x['Class'] == 'صادقة' else -1, axis=1)\n",
        "df_train['Label'] = df_train.apply(lambda x: 0 if x['Class'] == 'خادع' else 1 if x['Class'] == 'صادقة' else -1, axis=1)"
      ],
      "metadata": {
        "id": "ZzjeYA03WG9O"
      },
      "id": "ZzjeYA03WG9O",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BERT transformer"
      ],
      "metadata": {
        "id": "Avg8-cR5UG-F"
      },
      "id": "Avg8-cR5UG-F"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0c861dac"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "class BertTransformer(BaseEstimator, TransformerMixin):\n",
        "    def __init__(\n",
        "        self,\n",
        "        tokenizer,\n",
        "        model,\n",
        "        max_length: int = 60,\n",
        "        embedding_func: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,\n",
        "    ):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.model = model\n",
        "        self.model.eval()\n",
        "        self.max_length = max_length\n",
        "        self.embedding_func = embedding_func\n",
        "\n",
        "        if self.embedding_func is None:\n",
        "            self.embedding_func = lambda x: x[0][:, 0, :].squeeze()\n",
        "\n",
        "    def _tokenize(self, text: str) -> Tuple[torch.Tensor, torch.Tensor]:\n",
        "        # Tokenize the text with the provided tokenizer\n",
        "        tokenized_text = self.tokenizer.encode_plus(\n",
        "            text, add_special_tokens=True, max_length=self.max_length,truncation=True\n",
        "        )[\"input_ids\"]\n",
        "        \n",
        "        # padding\n",
        "        padded_text = tokenized_text + [0]*(self.max_length-len(tokenized_text))\n",
        "\n",
        "        # Create an attention mask telling BERT to use all words and ignore padded values\n",
        "        attention_mask = np.where(np.array(padded_text) != 0, 1, 0)\n",
        "\n",
        "        # bert takes in a batch so we need to unsqueeze the rows\n",
        "        return (\n",
        "            torch.tensor(padded_text).unsqueeze(0),\n",
        "            torch.tensor(attention_mask).unsqueeze(0),\n",
        "        )\n",
        "\n",
        "    def _tokenize_and_predict(self, text: str) -> torch.Tensor:\n",
        "        tokenized, attention_mask = self._tokenize(text)\n",
        "\n",
        "        embeddings = self.model(tokenized, attention_mask)\n",
        "        return self.embedding_func(embeddings)\n",
        "\n",
        "    def transform(self, text: List[str]):\n",
        "        if isinstance(text, pd.Series):\n",
        "            text = text.tolist()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            return torch.stack([self._tokenize_and_predict(string) for string in text])\n",
        "\n",
        "    def fit(self, X, y=None):\n",
        "        \"\"\"No fitting necessary so we just return ourselves\"\"\"\n",
        "        return self\n",
        "    \n",
        "def calculate_classification_metrics(\n",
        "    y_true: np.array,\n",
        "    y_pred: np.array,\n",
        "    average: Optional[str] = None,\n",
        "    return_df: bool = True,\n",
        ") -> Union[Dict[str, float], pd.DataFrame]:\n",
        "    \"\"\"Computes f1, precision, recall, precision, kappa, accuracy, and support\n",
        "\n",
        "    Args:\n",
        "        y_true: The true labels\n",
        "        y_pred: The predicted labels\n",
        "        average: How to average multiclass results\n",
        "\n",
        "    Returns:\n",
        "        Either a dataframe of the performance metrics or a single dictionary\n",
        "    \"\"\"\n",
        "    labels = unique_labels(y_true, y_pred)\n",
        "    # get results\n",
        "    precision, recall, f_score, support = sk_metrics.precision_recall_fscore_support(\n",
        "        y_true, y_pred, labels=labels, average=average\n",
        "    )\n",
        "\n",
        "    kappa = sk_metrics.cohen_kappa_score(y_true, y_pred, labels=labels)\n",
        "    accuracy = sk_metrics.accuracy_score(y_true, y_pred)\n",
        "\n",
        "    # create a pandas DataFrame\n",
        "    if return_df:\n",
        "        results = pd.DataFrame(\n",
        "            {\n",
        "                \"class\": labels,\n",
        "                \"f_score\": f_score,\n",
        "                \"precision\": precision,\n",
        "                \"recall\": recall,\n",
        "                \"support\": support,\n",
        "                \"kappa\": kappa,\n",
        "                \"accuracy\": accuracy,\n",
        "            }\n",
        "        )\n",
        "    else:\n",
        "        results = {\n",
        "            \"f1\": f_score,\n",
        "            \"precision\": precision,\n",
        "            \"recall\": recall,\n",
        "            \"kappa\": kappa,\n",
        "            \"accuracy\": accuracy,\n",
        "        }\n",
        "\n",
        "    return results"
      ],
      "id": "0c861dac"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## AraBERT embedding"
      ],
      "metadata": {
        "id": "nH9bVOGsWTp0"
      },
      "id": "nH9bVOGsWTp0"
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained(\"aubmindlab/bert-base-arabertv2\")\n",
        "tokenized_dict = tokenizer.encode_plus(\n",
        "    df_train[\"Review\"].tolist(),\n",
        "    add_special_tokens=True,\n",
        "    max_length=512,  # Set maximum sequence length here\n",
        "    truncation=True,\n",
        "    )\n",
        "\n",
        "bert_model = BertModel.from_pretrained(\"aubmindlab/bert-base-arabertv2\")\n",
        "tokenized_text = torch.tensor(tokenized_dict[\"input_ids\"])\n",
        "with torch.no_grad():\n",
        "  embeddings = bert_model(torch.tensor(tokenized_text.unsqueeze(0)))"
      ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d6adb1e4bc649f1b34f58685c0e1985"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/543M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06a896a6e09c42379092ea93f0939fb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv2 were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "<ipython-input-10-37db532cadec>:12: UserWarning: 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",
            "  embeddings = bert_model(torch.tensor(tokenized_text.unsqueeze(0)))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model classifier"
      ],
      "metadata": {
        "id": "YcMg_LcGT5Rz"
      },
      "id": "YcMg_LcGT5Rz"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.pipeline import FeatureUnion, Pipeline\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "fV_gZfa9W22d"
      },
      "id": "fV_gZfa9W22d",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=df_train['Review']\n",
        "y_train=df_train['Label'].values\n",
        "\n",
        "X_test=df_test['Review']\n",
        "y_test=df_test['Label'].values\n",
        "\n",
        "def model_generation(threshold):\n",
        "\n",
        "  bert_transformer = BertTransformer(tokenizer, bert_model)\n",
        "  ## Logistic Regression model\n",
        "  model_nb = GaussianNB()\n",
        "  # Self training classifier\n",
        "  classifier = SelfTrainingClassifier(base_estimator=model_nb,\n",
        "                                      threshold=threshold,\n",
        "                                      criterion='threshold',\n",
        "                                      max_iter=100, \n",
        "                                      verbose=True,)\n",
        "  model = Pipeline([\n",
        "       (\"vectorizer\", bert_transformer),\n",
        "       (\"classifier\", classifier),\n",
        "       ])\n",
        "  return model\n"
      ],
      "metadata": {
        "id": "O_gdAWGyW245"
      },
      "id": "O_gdAWGyW245",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def show_report(clf_ST):\n",
        "  print('')\n",
        "  print('---------- Self Training Model - Summary ----------')\n",
        "\n",
        "  print('Classes: ', clf_ST.classes_)\n",
        "\n",
        "  print('---------- Self Training Model - Evaluation on Test Data ----------')\n",
        "  y_pred = clf_ST.predict(X_test)\n",
        "  accuracy_score_ST = accuracy_score(y_test, y_pred)\n",
        "  print('Accuracy Score: ', accuracy_score_ST)\n",
        "\n",
        "  # Look at classification report to evaluate the model\n",
        "  report = classification_report(y_test, y_pred)\n",
        "  print(report)\n",
        "  return report, accuracy_score_ST"
      ],
      "metadata": {
        "id": "5caeXms28igk"
      },
      "id": "5caeXms28igk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clfs = []\n",
        "reports = []\n",
        "accuracies = []"
      ],
      "metadata": {
        "id": "W-Nyi1GaRCDA"
      },
      "id": "W-Nyi1GaRCDA",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "thresholds = [0.6,0.7,0.75,0.8,0.85,0.9, 0.95]\n",
        "\n",
        "for i, threshold  in enumerate(thresholds):\n",
        "  print('---------- ', threshold,' ----------')\n",
        "  clf_ST = model_generation(threshold).fit(X_train, y_train)\n",
        "  clfs.append(clf_ST)\n",
        "  report, accuracy = show_report(clf_ST)\n",
        "  reports.append(report)\n",
        "  accuracies.append(accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pWtBGW-6BA8-",
        "outputId": "1ef6f8ea-b366-4008-fe5c-9362c96b5b08"
      },
      "id": "pWtBGW-6BA8-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------  0.75  ----------\n",
            "End of iteration 1, added 4755 new labels.\n",
            "End of iteration 2, added 47 new labels.\n",
            "\n",
            "---------- Self Training Model - Summary ----------\n",
            "Classes:  [0 1]\n",
            "---------- Self Training Model - Evaluation on Test Data ----------\n",
            "Accuracy Score:  0.5666666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.83      0.66       239\n",
            "           1       0.65      0.30      0.41       241\n",
            "\n",
            "    accuracy                           0.57       480\n",
            "   macro avg       0.59      0.57      0.53       480\n",
            "weighted avg       0.59      0.57      0.53       480\n",
            "\n",
            "----------  0.95  ----------\n",
            "End of iteration 1, added 4671 new labels.\n",
            "End of iteration 2, added 130 new labels.\n",
            "End of iteration 3, added 1 new labels.\n",
            "\n",
            "---------- Self Training Model - Summary ----------\n",
            "Classes:  [0 1]\n",
            "---------- Self Training Model - Evaluation on Test Data ----------\n",
            "Accuracy Score:  0.5645833333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.54      0.82      0.65       239\n",
            "           1       0.64      0.31      0.41       241\n",
            "\n",
            "    accuracy                           0.56       480\n",
            "   macro avg       0.59      0.57      0.53       480\n",
            "weighted avg       0.59      0.56      0.53       480\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thresholds = [0.6,0.7,0.75,0.8,0.85,0.9, 0.95]\n",
        "accuracies = [0.56875, 0.56875, 0.5666666666666667, 0.5666666666666667, 0.56458333333, 0.5625, 0.5645833333333333]"
      ],
      "metadata": {
        "id": "KJoc7IahFSzz"
      },
      "id": "KJoc7IahFSzz",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(thresholds, accuracies)\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Qt-x3UMICLe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "b0cbe49a-6003-48f3-bf92-1a39f195e99d"
      },
      "id": "Qt-x3UMICLe6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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