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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m383s\u001b[0m 48ms/step - accuracy: 0.7637 - loss: 0.4815 - val_accuracy: 0.8195 - val_loss: 0.3929 - learning_rate: 0.0010\n",
"Epoch 2/10\n",
"\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m360s\u001b[0m 45ms/step - accuracy: 0.8561 - loss: 0.3267 - val_accuracy: 0.8256 - val_loss: 0.3854 - learning_rate: 0.0010\n",
"Epoch 3/10\n",
"\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m373s\u001b[0m 47ms/step - accuracy: 0.8937 - loss: 0.2503 - val_accuracy: 0.8250 - val_loss: 0.4444 - learning_rate: 0.0010\n",
"Epoch 4/10\n",
"\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m377s\u001b[0m 47ms/step - accuracy: 0.9269 - loss: 0.1794 - val_accuracy: 0.8173 - val_loss: 0.4580 - learning_rate: 0.0010\n",
"Epoch 5/10\n",
"\u001b[1m7964/7964\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m385s\u001b[0m 48ms/step - accuracy: 0.9496 - loss: 0.1284 - val_accuracy: 0.8147 - val_loss: 0.5704 - learning_rate: 0.0010\n",
"\u001b[1m2213/2213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 9ms/step - accuracy: 0.8228 - loss: 0.3848\n",
"Test Accuracy: 0.8214734792709351\n",
"\u001b[1m2213/2213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 11ms/step\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.84 0.90 0.87 46733\n",
" 1 0.77 0.68 0.72 24052\n",
"\n",
" accuracy 0.82 70785\n",
" macro avg 0.81 0.79 0.79 70785\n",
"weighted avg 0.82 0.82 0.82 70785\n",
"\n",
"\n",
"Confusion Matrix:\n",
"[[41892 4841]\n",
" [ 7796 16256]]\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
"from tensorflow.keras.utils import to_categorical\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"from tensorflow.keras.callbacks import ReduceLROnPlateau, TensorBoard, EarlyStopping\n",
"\n",
"# load data\n",
"df = pd.read_csv('combined_data.csv')\n",
"\n",
"# Tokenize the text\n",
"tokenizer = Tokenizer()\n",
"tokenizer.fit_on_texts(df['title'])\n",
"X = tokenizer.texts_to_sequences(df['title'])\n",
"X = pad_sequences(X)\n",
"\n",
"# Encode the target variable\n",
"encoder = LabelEncoder()\n",
"y = encoder.fit_transform(df['source'])\n",
"y = to_categorical(y)\n",
"\n",
"# Split the data\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Build the LSTM model\n",
"model = Sequential()\n",
"model.add(Embedding(len(tokenizer.word_index) + 1, 128))\n",
"model.add(LSTM(128, return_sequences=True))\n",
"model.add(Dropout(0.5))\n",
"model.add(LSTM(64))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(len(encoder.classes_), activation='softmax'))\n",
"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
"\n",
"# Learning rate scheduler\n",
"lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-5)\n",
"\n",
"# TensorBoard callback for logging\n",
"tensorboard_callback = TensorBoard(log_dir='./logs', histogram_freq=1)\n",
"\n",
"# Early stopping to prevent overfitting\n",
"early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
"\n",
"# Train the model with callbacks\n",
"model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.1, \n",
" callbacks=[lr_scheduler, tensorboard_callback, early_stopping])\n",
"\n",
"# Evaluate the model\n",
"loss, accuracy = model.evaluate(X_test, y_test)\n",
"print(f\"Test Accuracy: {accuracy}\")\n",
"\n",
"# Predictions and evaluation\n",
"y_pred = model.predict(X_test)\n",
"y_pred_classes = y_pred.argmax(axis=1)\n",
"y_test_classes = y_test.argmax(axis=1)\n",
"\n",
"print(\"\\nClassification Report:\")\n",
"print(classification_report(y_test_classes, y_pred_classes))\n",
"\n",
"print(\"\\nConfusion Matrix:\")\n",
"print(confusion_matrix(y_test_classes, y_pred_classes))\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
}
],
"source": [
"# save model\n",
"model.save('news_classifier.h5')\n",
"\n",
"# save tokenizer\n",
"import pickle\n",
"with open('tokenizer.pickle', 'wb') as handle:\n",
" pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
" \n",
"# save encoder\n",
"with open('encoder.pickle', 'wb') as handle:\n",
" pickle.dump(encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# deploy the model\n",
"# user give the title and the model will predict the source\n",
"# Load the model and tokenizer\n",
"from tensorflow.keras.models import load_model\n",
"import pickle\n",
"\n",
"# Load the tokenizer\n",
"with open('tokenizer.pickle', 'rb') as handle:\n",
" tokenizer = pickle.load(handle)\n",
"\n",
"# Load the encoder\n",
"with open('encoder.pickle', 'rb') as handle:\n",
" encoder = pickle.load(handle)\n",
"\n",
"\n",
"def predict_source(title):\n",
" # Load the model\n",
" model = load_model('news_classifier.h5')\n",
" # Tokenize the input\n",
" X = tokenizer.texts_to_sequences([title])\n",
" X = pad_sequences(X)\n",
" # Predict the source\n",
" y_pred = model.predict(X)\n",
" source = encoder.inverse_transform(y_pred.argmax(axis=1))\n",
" return source[0]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step\n",
"Predicted Source: foxnews\n"
]
}
],
"source": [
"# Test the function\n",
"# user input\n",
"title = input(\"Enter the title: \")\n",
"source = predict_source(title)\n",
"print(f\"Predicted Source: {source}\")"
]
}
],
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"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",
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