Transformers_DL.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-3m4KG7H6Ye"
      },
      "source": [
        "**Training Model on BERT**\n",
        "\n",
        "Requirements:\n",
        "1. Download transformers package\n",
        "\n",
        "\n",
        "Remarks:\n",
        "1. May need to rebalance dataset (it is currently imbalanced)\n",
        "2. May take a long time  - try with titles first"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMwznpZ_H5um"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import activations, optimizers, losses\n",
        "import transformers\n",
        "from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification, BertModel, BertTokenizer, BertForSequenceClassification, BertTokenizerFast\n",
        "from scipy.special import softmax\n",
        "from datetime import datetime\n",
        "from sklearn.utils import resample"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfRgIXb8IOIv"
      },
      "source": [
        "# Load training data\n",
        "\n",
        "# Use when I'm using Wharton account\n",
        "train = pd.read_csv('drive/MyDrive/CIS520 Project/data set/train.csv')\n",
        "test = pd.read_csv('drive/MyDrive/CIS520 Project/data set/test.csv')\n",
        "\n",
        "# Get top 25% vs bottom 75%\n",
        "train['top25pct'] = (train['percentile'] >= 0.75).astype(int)\n",
        "test['top25pct'] = (test['percentile'] >= 0.75).astype(int)\n",
        "\n",
        "train['published_date'] = train['published_date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n",
        "test['published_date'] = test['published_date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Caxjn19RQiX8"
      },
      "source": [
        "def upsample_minority(df):\r\n",
        "\r\n",
        "  # Upsample minority class in both the training and test data\r\n",
        "  df_majority = df.loc[df['top25pct'] == 0, :]\r\n",
        "  df_minority = df.loc[df['top25pct'] == 1, :]\r\n",
        "  df_minority_upsampled = resample(df_minority, replace = True, n_samples = len(df_majority), random_state = 42)\r\n",
        "\r\n",
        "  # Combine together to get the upsampled training data\r\n",
        "  df = pd.concat([df_majority, df_minority_upsampled])\r\n",
        "\r\n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_omraoQIQiZx"
      },
      "source": [
        "# Upsample the minority class\r\n",
        "train = upsample_minority(train)\r\n",
        "test = upsample_minority(test)\r\n",
        "\r\n",
        "train = train.reset_index(drop = True)\r\n",
        "test = test.reset_index(drop = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObCt1fif8vEf"
      },
      "source": [
        "# Get Peter's test data\r\n",
        "test_final = pd.read_csv('drive/MyDrive/CIS520 Project/data set/test_df_upsampled.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfGUaVk--yKd"
      },
      "source": [
        "test_final_data = pd.concat([test_final['title'], test_final['top25pct']], axis = 1)\r\n",
        "test_final_data.columns = ['text', 'labels']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wEVWG0ztaWj"
      },
      "source": [
        "**Using BERT Classification Model from SimpleTransformers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwKX2ngtuA9o"
      },
      "source": [
        "from simpletransformers.classification import ClassificationModel\n",
        "import logging\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbqEUWfhtZx9"
      },
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(train['title'], train['top25pct'], test_size = 0.2, random_state = 42, stratify = train['top25pct'])\n",
        "\n",
        "train_data = pd.concat([X_train, y_train], axis = 1)\n",
        "valid_data = pd.concat([X_valid, y_valid], axis = 1)\n",
        "test_data = pd.concat([test['title'], test['top25pct']], axis = 1)\n",
        "\n",
        "# Rename columns\n",
        "train_data.columns = ['text', 'labels']\n",
        "valid_data.columns = ['text', 'labels']\n",
        "test_data.columns = ['text', 'labels']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsU4iORyRlnB",
        "outputId": "44da1c90-6259-48d6-9785-2ea62bebd417"
      },
      "source": [
        "print(len(train_data))\r\n",
        "print(len(valid_data))\r\n",
        "print(len(test_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20067\n",
            "5017\n",
            "6240\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kc-qfK09t4W3",
        "outputId": "af55e359-0618-49a1-aec8-fc3ba8259896"
      },
      "source": [
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "args = {\n",
        "   'output_dir': 'drive/MyDrive/CIS520 Project/final bert model/',\n",
        "   'cache_dir': 'cache/',\n",
        "\n",
        "   'fp16': False,\n",
        "\n",
        "   'overwrite_output_dir': True,\n",
        "   'reprocess_input_data': True,\n",
        "}\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model = ClassificationModel('bert', 'bert-base-cased', args = args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
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            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:216: UserWarning: Please also save or load the state of the optimizer when saving or loading the scheduler.\n",
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          "output_type": "stream",
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            "\n"
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            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:simpletransformers.classification.classification_model: Training of bert model complete. Saved to drive/MyDrive/CIS520 Project/final bert model/.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50180, 0.09237574692887576)"
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      ]
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      "cell_type": "code",
      "metadata": {
        "id": "14Bi5CBd7ays"
      },
      "source": [
        "model = ClassificationModel('bert', 'drive/MyDrive/CIS520 Project/final bert model/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "1a4c0f4d-8f05-4c2c-c998-f4b9b592cf09"
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        "result, model_outputs, wrong_predictions = model.eval_model(test_data, acc = accuracy_score)\n",
        "test_probs = softmax(model_outputs, axis = 1)\n",
        "test_preds = np.argmax(test_probs, axis = 1)"
      ],
      "execution_count": null,
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        {
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          ],
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          "output_type": "stream",
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            "\n"
          ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44C32fva5ZDD"
      },
      "source": [
        "np.save('drive/MyDrive/CIS520 Project/final bert model/test_probs_titles.npy', test_probs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "outputId": "85e613af-309a-4040-8fd8-fa2c935ea2a0"
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      "source": [
        "confusion_matrix(test_data['labels'], test_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "array([[2657,  463],\n",
              "       [1520, 1600]])"
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        "result_final, model_outputs_final, wrong_predictions_final = model.eval_model(test_final_data, acc = accuracy_score)\r\n",
        "test_probs_final = softmax(model_outputs_final, axis = 1)\r\n",
        "test_preds_final = np.argmax(test_probs_final, axis = 1)"
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            "\n"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "6o388xhB_0jI",
        "outputId": "a2ebd2db-a7ee-4b00-83b9-20f8f1b684a0"
      },
      "source": [
        "confusion_matrix(test_final_data['labels'], test_preds_final)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "array([[2657,  463],\n",
              "       [1520, 1600]])"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9KUVO3N_iWm"
      },
      "source": [
        "test_final_out = pd.DataFrame(data = {'id': test_final['id'], 'bert_pred': test_probs_final[:,1]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiHvfD69_Yxf"
      },
      "source": [
        "test_final_out = test_final_out.to_csv('drive/MyDrive/CIS520 Project/bert_preds.csv', index = None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DNroB2F6y_1"
      },
      "source": [
        "**Transformers on Content**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2zRVJDt6m5l"
      },
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(train['content'], train['top_25pct'], test_size = 0.2, random_state = 42, stratify = train['top_25pct'])\n",
        "\n",
        "\n",
        "train_data = pd.concat([X_train, y_train], axis = 1)\n",
        "valid_data = pd.concat([X_valid, y_valid], axis = 1)\n",
        "test_data = pd.concat([test['content'], test['top_25pct']], axis = 1)\n",
        "\n",
        "# Rename columns\n",
        "train_data.columns = ['text', 'labels']\n",
        "valid_data.columns = ['text', 'labels']\n",
        "test_data.columns = ['text', 'labels']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Je4sxTw87AVU",
        "outputId": "6e8da776-e9fd-4e75-d81f-797082ee39d9"
      },
      "source": [
        "logging.basicConfig(level=logging.INFO)\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "args = {\n",
        "   'output_dir': 'drive/MyDrive/CIS520 Project/bert model output content',\n",
        "   'cache_dir': 'cache/',\n",
        "\n",
        "   'fp16': False,\n",
        "\n",
        "   'overwrite_output_dir': True,\n",
        "   'reprocess_input_data': True,\n",
        "}\n",
        "\n",
        "# Create a ClassificationModel\n",
        "model_content = ClassificationModel('bert', 'bert-base-cased', args = args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnWSeYYE_nQx",
        "outputId": "10c0d137-5fcf-44c9-b32e-0fa11e08589e"
      },
      "source": [
        "confusion_matrix(test_data['labels'], test_preds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2780,  340],\n",
              "       [1703, 1417]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWcd9mTT_cUI"
      },
      "source": [
        "np.save('drive/MyDrive/CIS520 Project/bert model output content/test_probs_content.npy', test_probs)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}