Final proj_IMDB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, LSTM, Dense, SpatialDropout1D\n",
    "from keras.callbacks import EarlyStopping\n",
    "import glob\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bromwell High is a cartoon comedy. It ran at t...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Homelessness (or Houselessness as George Carli...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brilliant over-acting by Lesley Ann Warren. Be...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>This is easily the most underrated film inn th...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This is not the typical Mel Brooks film. It wa...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>I occasionally let my kids watch this garbage ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>When all we have anymore is pretty much realit...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>The basic genre is a thriller intercut with an...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>Four things intrigued me as to this film - fir...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>David Bryce's comments nearby are exceptionall...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review sentiment\n",
       "0      Bromwell High is a cartoon comedy. It ran at t...  positive\n",
       "1      Homelessness (or Houselessness as George Carli...  positive\n",
       "2      Brilliant over-acting by Lesley Ann Warren. Be...  positive\n",
       "3      This is easily the most underrated film inn th...  positive\n",
       "4      This is not the typical Mel Brooks film. It wa...  positive\n",
       "...                                                  ...       ...\n",
       "49995  I occasionally let my kids watch this garbage ...  negative\n",
       "49996  When all we have anymore is pretty much realit...  negative\n",
       "49997  The basic genre is a thriller intercut with an...  negative\n",
       "49998  Four things intrigued me as to this film - fir...  negative\n",
       "49999  David Bryce's comments nearby are exceptionall...  negative\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the IMDb dataset\n",
    "\n",
    "file_paths_pos = glob.glob('./aclImdb/train/pos/*.txt')\n",
    "file_paths_neg = glob.glob('./aclImdb/train/neg/*.txt')\n",
    "file_paths_test_pos = glob.glob('./aclImdb/test/pos/*.txt')\n",
    "file_paths_test_neg = glob.glob('./aclImdb/test/neg/*.txt')\n",
    "\n",
    "trainPos = []\n",
    "trainNeg = []\n",
    "testPos = []\n",
    "testNeg = []\n",
    "\n",
    "for path in file_paths_pos:\n",
    "    with open(path, 'r') as file:\n",
    "        content = file.read()\n",
    "        trainPos.append(content)\n",
    "\n",
    "for path in file_paths_neg:\n",
    "    with open(path, 'r') as file:\n",
    "        content = file.read()\n",
    "        trainNeg.append(content)\n",
    "\n",
    "for path in file_paths_test_pos:\n",
    "    with open(path, 'r') as file:\n",
    "        content = file.read()\n",
    "        testPos.append(content)\n",
    "\n",
    "for path in file_paths_test_neg:\n",
    "    with open(path, 'r') as file:\n",
    "        content = file.read()\n",
    "        testNeg.append(content)\n",
    "\n",
    "trainPos = pd.DataFrame(trainPos, columns=['review'])\n",
    "trainNeg = pd.DataFrame(trainNeg, columns=['review'])\n",
    "testPos = pd.DataFrame(testPos, columns=['review'])\n",
    "testNeg = pd.DataFrame(testNeg, columns=['review'])\n",
    "\n",
    "trainPos['sentiment'] = 'positive'\n",
    "trainNeg['sentiment'] = 'negative'\n",
    "testPos['sentiment'] = 'positive'\n",
    "testNeg['sentiment'] = 'negative'\n",
    "\n",
    "imdb_data = pd.concat([trainPos, trainNeg, testPos, testNeg], ignore_index=True)\n",
    "imdb_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        bromwell high is a cartoon comedy it ran at th...\n",
       "1        homelessness or houselessness as george carlin...\n",
       "2        brilliant overacting by lesley ann warren best...\n",
       "3        this is easily the most underrated film inn th...\n",
       "4        this is not the typical mel brooks film it was...\n",
       "                               ...                        \n",
       "49995    i occasionally let my kids watch this garbage ...\n",
       "49996    when all we have anymore is pretty much realit...\n",
       "49997    the basic genre is a thriller intercut with an...\n",
       "49998    four things intrigued me as to this film  firs...\n",
       "49999    david bryces comments nearby are exceptionally...\n",
       "Name: review, Length: 50000, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning the reviews\n",
    "\n",
    "import string\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub('\\[.*?\\]', '', text)\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\n",
    "    text = re.sub('[''\"\",,,]', '', text)\n",
    "    text = re.sub('\\n', '', text)\n",
    "    return text\n",
    "\n",
    "imdb_data['review'] = imdb_data['review'].apply(clean_text)\n",
    "imdb_data.review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenize and pad sequences\n",
    "max_features = 5000\n",
    "tokenizer = Tokenizer(num_words=max_features, split=' ')\n",
    "tokenizer.fit_on_texts(imdb_data['review'].values)\n",
    "X = tokenizer.texts_to_sequences(imdb_data['review'].values)\n",
    "X = pad_sequences(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the target labels\n",
    "label_encoder = LabelEncoder()\n",
    "Y = label_encoder.fit_transform(imdb_data['sentiment'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTM"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_5 (Embedding)     (None, 2046, 32)          160000    \n",
      "                                                                 \n",
      " spatial_dropout1d_5 (Spatia  (None, 2046, 32)         0         \n",
      " lDropout1D)                                                     \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 32)                8320      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 168,353\n",
      "Trainable params: 168,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/3\n",
      "196/196 [==============================] - 577s 3s/step - loss: 0.4967 - accuracy: 0.7476 - val_loss: 0.3150 - val_accuracy: 0.8707\n",
      "Epoch 2/3\n",
      "196/196 [==============================] - 575s 3s/step - loss: 0.3027 - accuracy: 0.8774 - val_loss: 0.2803 - val_accuracy: 0.8835\n",
      "Epoch 3/3\n",
      "196/196 [==============================] - 561s 3s/step - loss: 0.2740 - accuracy: 0.8936 - val_loss: 0.2842 - val_accuracy: 0.8858\n",
      "Test loss: 0.28416669368743896\n",
      "Test accuracy: 0.8858000040054321\n"
     ]
    }
   ],
   "source": [
    "# Build the model\n",
    "\n",
    "embed_dim = 32\n",
    "lstm_out = 32\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(max_features, embed_dim, input_length=X.shape[1]))\n",
    "model.add(SpatialDropout1D(0.4))\n",
    "model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "\n",
    "# Train the model\n",
    "batch_size = 128\n",
    "epochs = 3\n",
    "\n",
    "train_history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=epochs, batch_size=batch_size)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.plot(train_history.history['loss'], label='Training Loss')\n",
    "plt.plot(train_history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()\n",
    "\n",
    "# Plot training and validation accuracy\n",
    "plt.plot(train_history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(train_history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}