notebooks/model_GRU.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add root path to system path\n",
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.append('../')\n",
    "\n",
    "os.chdir(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-21 08:51:27.315479: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "from utility.paths import DataPath\n",
    "from preprocessing import Preprocessing\n",
    "from models.gru import GRU\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Due to the complexity of implementing the GRU model, we have created a dedicated [gru.py](../models/gru.py) class to encapsulate all the necessary logic. This notebook is primarily used for presenting our results, ensuring both conciseness and clarity.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BrAp5TdkEWMm"
   },
   "source": [
    "# 2. Data loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will load the pre-processed datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "d_jnZ3v4GME_"
   },
   "outputs": [],
   "source": [
    "# Create training and testing preprocessing object.\n",
    "train_prep = Preprocessing([DataPath.TRAIN_NEG_FULL, DataPath.TRAIN_POS_FULL])\n",
    "test_prep = Preprocessing([DataPath.TEST], is_test=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "S6lXeFeEGhOB"
   },
   "outputs": [],
   "source": [
    "# Declare paramaterss for GRU.\n",
    "MAX_LEN = 256\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 10\n",
    "EMBEDDING_DIM = 100  # Since we're using GLoVe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "D4ub7sIUHWpP"
   },
   "outputs": [],
   "source": [
    "# Declare GRU model.\n",
    "gru = GRU(weight_path=DataPath.GRU_WEIGHT,\n",
    "          submission_path=DataPath.GRU_SUBMISSION,\n",
    "          max_length=MAX_LEN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_-wpqx-BIsIS"
   },
   "source": [
    "# 3. Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will preprocess the data to ensure it is clean before beginning the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 758,
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    "id": "ZLvxje63HFhK",
    "outputId": "539766a5-916a-488a-d58c-8d3d2b53a101"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "816d33445ee449d5b6e7d104ecf05bb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Preprocessing train data:   0%|          | 0/14 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `drop_duplicates`\n",
      "Executing: `remove_ending`\n",
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████| 2268591/2268591 [00:01<00:00, 1555423.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `remove_space_around_emoji`\n",
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████| 2268591/2268591 [00:01<00:00, 1620862.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `reconstruct_emoji`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████| 2268591/2268591 [00:15<00:00, 142863.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████| 2268591/2268591 [00:01<00:00, 1153637.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `emoji_to_tag`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████| 2268591/2268591 [00:27<00:00, 81485.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `reconstruct_last_emoji`\n",
      "Executing: `num_to_tag`\n",
      "Executing: `hashtag_to_tag`\n",
      "Executing: `repeat_symbols_to_tag`\n",
      "Executing: `elongate_to_tag`\n",
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████| 2268591/2268591 [00:01<00:00, 1328159.67it/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90e86a75dc114a3dbd6849ed839dd851",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Preprocessing test data:   0%|          | 0/13 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `remove_ending`\n",
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 985503.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `remove_space_around_emoji`\n",
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 1023450.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `reconstruct_emoji`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 125352.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 978856.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `emoji_to_tag`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 71049.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `reconstruct_last_emoji`\n",
      "Executing: `num_to_tag`\n",
      "Executing: `hashtag_to_tag`\n",
      "Executing: `repeat_symbols_to_tag`\n",
      "Executing: `elongate_to_tag`\n",
      "Executing: `remove_extra_space`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 1006238.51it/s]\n"
     ]
    }
   ],
   "source": [
    "# Retrieve preprocessing steps declared in GRU class for both train and test data.\n",
    "for step in tqdm(gru.preprocessing(), desc=\"Preprocessing train data\"):\n",
    "    getattr(train_prep, step)()\n",
    "\n",
    "for step in tqdm(gru.preprocessing(is_train=False), desc=\"Preprocessing test data\"):\n",
    "    getattr(test_prep, step)()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "TMgnjzGNIDvf"
   },
   "outputs": [],
   "source": [
    "# Retrieve the preprocessed df.\n",
    "train_data = train_prep.__get__()\n",
    "test_data = test_prep.__get__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "N2db1FJsIRru"
   },
   "outputs": [],
   "source": [
    "# Export the dataframes. For training frames, shuffles.\n",
    "train_data = train_data.sample(frac=1)\n",
    "train_data.to_csv(DataPath.GRU_TRAIN, index=False)\n",
    "\n",
    "test_data.to_csv(DataPath.GRU_TEST, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "prsfNT71IcsA",
    "outputId": "65f09127-046d-4dbb-c5af-17e2577bfa65"
   },
   "outputs": [],
   "source": [
    "# Read the dataframe\n",
    "train_df = pd.read_csv(DataPath.GRU_TRAIN)\n",
    "train_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "I_qrq_BgI6ta"
   },
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X, y = train_df['text'].values, train_df['label'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jg-zzpVHIos1"
   },
   "source": [
    "# 4. Training GRU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now begin training the GRU model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `update_vocabulary`\n",
      "Vocabulary size: 439824\n",
      "Executing: `padding`\n",
      "Executing: `padding`\n",
      "Executing: `generate_embedding_matrix`\n"
     ]
    },
    {
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       "model_id": "cfaf854904654999abdbdf014e4e56e2",
       "version_major": 2,
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      },
      "text/plain": [
       "Loading GloVe: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1193514 word vectors\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc7bbf94b72f406abda0ac37928be824",
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      },
      "text/plain": [
       "Generating embedding matrix:   0%|          | 0/439822 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 172879 words (266943 missing)\n",
      "Executing: `build_model`\n",
      "Model summary\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 256, 100)          43982400  \n",
      "                                                                 \n",
      " bidirectional (Bidirection  (None, 200)               121200    \n",
      " al)                                                             \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100)               20100     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 44123801 (168.32 MB)\n",
      "Trainable params: 141401 (552.35 KB)\n",
      "Non-trainable params: 43982400 (167.78 MB)\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fitting model\n",
      "Epoch 1/10\n",
      "14179/14179 [==============================] - 232s 15ms/step - loss: 0.3709 - accuracy: 0.8268 - val_loss: 0.3389 - val_accuracy: 0.8457\n",
      "Epoch 2/10\n",
      "14179/14179 [==============================] - 216s 15ms/step - loss: 0.3405 - accuracy: 0.8447 - val_loss: 0.3280 - val_accuracy: 0.8517\n",
      "Epoch 3/10\n",
      "14179/14179 [==============================] - 218s 15ms/step - loss: 0.3312 - accuracy: 0.8502 - val_loss: 0.3251 - val_accuracy: 0.8532\n",
      "Epoch 4/10\n",
      "14179/14179 [==============================] - 215s 15ms/step - loss: 0.3261 - accuracy: 0.8529 - val_loss: 0.3207 - val_accuracy: 0.8563\n",
      "Epoch 5/10\n",
      "14179/14179 [==============================] - 215s 15ms/step - loss: 0.3230 - accuracy: 0.8547 - val_loss: 0.3214 - val_accuracy: 0.8565\n",
      "Epoch 6/10\n",
      "14179/14179 [==============================] - 211s 15ms/step - loss: 0.3209 - accuracy: 0.8559 - val_loss: 0.3191 - val_accuracy: 0.8573\n",
      "Epoch 7/10\n",
      "14179/14179 [==============================] - 218s 15ms/step - loss: 0.3193 - accuracy: 0.8566 - val_loss: 0.3183 - val_accuracy: 0.8576\n",
      "Epoch 8/10\n",
      "14179/14179 [==============================] - 222s 16ms/step - loss: 0.3184 - accuracy: 0.8572 - val_loss: 0.3201 - val_accuracy: 0.8566\n",
      "Epoch 9/10\n",
      "14179/14179 [==============================] - 215s 15ms/step - loss: 0.3174 - accuracy: 0.8574 - val_loss: 0.3182 - val_accuracy: 0.8573\n",
      "Epoch 10/10\n",
      "14179/14179 [==============================] - 227s 16ms/step - loss: 0.3166 - accuracy: 0.8583 - val_loss: 0.3197 - val_accuracy: 0.8583\n",
      "Saving model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving weights\n"
     ]
    }
   ],
   "source": [
    "# Update vocabulary for GRU embedding\n",
    "gru.update_vocabulary(X)\n",
    "\n",
    "# Start the training process\n",
    "gru.train(X, y, batch_size=BATCH_SIZE, epochs=EPOCHS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GRU model achieves an accuracy of `0.86` after `10 epochs`. It appears that 10 epochs are sufficient for the loss to converge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WADTPPo5kwQl"
   },
   "source": [
    "# 5. Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JkLdE9ohlu8x",
    "outputId": "88000b40-3b94-4bf5-83c5-50f8e251875d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing: `padding`\n",
      "313/313 [==============================] - 4s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "# Read preprocessed test data\n",
    "test_df = pd.read_csv(DataPath.GRU_TEST)\n",
    "\n",
    "# Retrieve `text` column for predicting\n",
    "X_test = test_df[\"text\"]\n",
    "\n",
    "# Make the prediction\n",
    "gru.predict(X_test)"
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    "This submission to AIcrowd achieved the following accuracy scores:\n",
    "    \n",
    "- First Score =`0.865`\n",
    "- Secondary Score = `0.866`\n",
    "\n",
    "You can access the results here:\n",
    "\n",
    "- csv output file : [test_predictions_GRU.csv](../submissions/gru/test_predictions_GRU.csv)\n",
    "- AIcrowd submission id : **[#247060](https://www.aicrowd.com/challenges/epfl-ml-text-classification/submissions/247060)**\n",
    "\n",
    "Up to this point, it's the model that has achieved the highest accuracy. We will now proceed to explore transformer models, specifically the : [Bidirectional Encoder Representations from Transformers (BERT)](model_BERT.ipynb)."
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