DL_Project
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AnielaWalczak
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Raport.docx
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import re\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
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"\n",
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"# Wczytywanie danych\n",
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"data = pd.read_csv('spam.csv')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def preprocess_text(text):\n",
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" text = re.sub(r'\\W', ' ', text)\n",
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" text = text.lower()\n",
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" text = text.split()\n",
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" text = ' '.join(text)\n",
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" return text\n",
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"\n",
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"data['Message'] = data['Message'].apply(preprocess_text)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"TF-IDF + Naive Bayes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Naive Bayes TF-IDF:\n",
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"Accuracy: 0.97847533632287\n",
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"Precision: 1.0\n",
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"Recall: 0.85\n",
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"F1-score: 0.918918918918919\n"
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]
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}
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],
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"source": [
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"# Podział danych na zbiór treningowy i testowy\n",
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"X_train, X_test, y_train, y_test = train_test_split(data['Message'], data['Category'], test_size=0.2, random_state=0)\n",
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"\n",
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"# Wektoryzacja TF-IDF\n",
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"tfidf = TfidfVectorizer(max_features=3000)\n",
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"X_train_tfidf = tfidf.fit_transform(X_train)\n",
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"X_test_tfidf = tfidf.transform(X_test)\n",
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"\n",
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"# Model Naive Bayes\n",
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"nb_model = MultinomialNB()\n",
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"nb_model.fit(X_train_tfidf, y_train)\n",
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"y_pred = nb_model.predict(X_test_tfidf)\n",
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"\n",
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"# Ewaluacja\n",
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"print('Naive Bayes TF-IDF:')\n",
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"print(f'Accuracy: {accuracy_score(y_test, y_pred)}')\n",
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"print(f'Precision: {precision_score(y_test, y_pred, pos_label=\"spam\")}')\n",
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"print(f'Recall: {recall_score(y_test, y_pred, pos_label=\"spam\")}')\n",
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"print(f'F1-score: {f1_score(y_test, y_pred, pos_label=\"spam\")}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"RNN (LSTM)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/5\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\walcz\\Desktop\\studia\\uczenie\\projekt\\myenv\\lib\\site-packages\\keras\\src\\layers\\core\\embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1m126/126\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 54ms/step - accuracy: 0.9047 - loss: 0.3002 - val_accuracy: 0.9843 - val_loss: 0.0670\n",
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"Epoch 2/5\n",
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"\u001b[1m126/126\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 53ms/step - accuracy: 0.9902 - loss: 0.0401 - val_accuracy: 0.9865 - val_loss: 0.0522\n",
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"Epoch 3/5\n",
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"\u001b[1m126/126\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 52ms/step - accuracy: 0.9972 - loss: 0.0149 - val_accuracy: 0.9843 - val_loss: 0.0582\n",
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"Epoch 4/5\n",
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"\u001b[1m126/126\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 47ms/step - accuracy: 0.9983 - loss: 0.0078 - val_accuracy: 0.9865 - val_loss: 0.0601\n",
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"Epoch 5/5\n",
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"\u001b[1m126/126\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 49ms/step - accuracy: 0.9974 - loss: 0.0071 - val_accuracy: 0.9865 - val_loss: 0.0628\n",
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"\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step\n",
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"LSTM:\n",
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"Accuracy: 0.9856502242152466\n",
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"Precision: 0.9615384615384616\n",
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"Recall: 0.9375\n",
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"F1-score: 0.9493670886075949\n"
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]
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}
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],
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"source": [
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"from tensorflow.keras.preprocessing.text import Tokenizer\n",
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"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
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"\n",
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"# Wczytanie danych\n",
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"data = pd.read_csv('spam.csv')\n",
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"\n",
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"# Tokenizacja i padding\n",
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"tokenizer = Tokenizer(num_words=5000)\n",
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"tokenizer.fit_on_texts(data['Message'])\n",
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"X = tokenizer.texts_to_sequences(data['Message'])\n",
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"X = pad_sequences(X, maxlen=100)\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, data['Category'], test_size=0.2, random_state=0)\n",
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"\n",
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"# Przekształcenie etykiet 'ham' i 'spam' na wartości liczbowe\n",
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"label_mapping = {'ham': 0, 'spam': 1}\n",
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"y_train = y_train.map(label_mapping)\n",
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"y_test = y_test.map(label_mapping)\n",
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"\n",
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"# Model LSTM\n",
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"model = Sequential()\n",
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"model.add(Embedding(input_dim=5000, output_dim=128, input_length=100))\n",
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"model.add(LSTM(128))\n",
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"model.add(Dense(1, activation='sigmoid'))\n",
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"\n",
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"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
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"model.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.1)\n",
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"\n",
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"y_pred = (model.predict(X_test) > 0.5).astype(\"int32\")\n",
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"\n",
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"# Ewaluacja\n",
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"print('LSTM:')\n",
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"print(f'Accuracy: {accuracy_score(y_test, y_pred)}')\n",
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"print(f'Precision: {precision_score(y_test, y_pred)}')\n",
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"print(f'Recall: {recall_score(y_test, y_pred)}')\n",
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"print(f'F1-score: {f1_score(y_test, y_pred)}')\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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