s452714/DL_projekt
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
main
DL_projekt/projekt.ipynb

435 lines
13 KiB
Plaintext
Raw Permalink Normal View History

Projekt
2024-06-10 21:53:20 +02:00
{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-06-10T19:23:48.168059Z",
"start_time": "2024-06-10T19:23:48.163587Z"
}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"import gensim\n",
"from gensim.models import Word2Vec\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score"
],
"outputs": [],
"execution_count": 17
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:28.230207Z",
"start_time": "2024-06-10T19:14:10.971636Z"
}
},
"cell_type": "code",
"source": [
"train_df = pd.read_csv('train.csv', header=None, names=['polarity', 'title', 'text'])\n",
"test_df = pd.read_csv('test.csv', header=None, names=['polarity', 'title', 'text'])"
],
"id": "6b5f49c11e98d496",
"outputs": [],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:28.820334Z",
"start_time": "2024-06-10T19:14:28.231165Z"
}
},
"cell_type": "code",
"source": [
"train_df = train_df.sample(n=40000, random_state=1)\n",
"test_df = test_df.sample(n=10000, random_state=1)"
],
"id": "d318521180cd6b02",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:28.992617Z",
"start_time": "2024-06-10T19:14:28.820334Z"
}
},
"cell_type": "code",
"source": [
"train_df['text'] = train_df['title'].fillna('') + ' ' + train_df['text'].fillna('')\n",
"test_df['text'] = test_df['title'].fillna('') + ' ' + test_df['text'].fillna('')\n",
"\n",
"train_df.drop(columns=['title'], inplace=True)\n",
"test_df.drop(columns=['title'], inplace=True)"
],
"id": "cc04ff0b71bc8ea4",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:28.998503Z",
"start_time": "2024-06-10T19:14:28.993619Z"
}
},
"cell_type": "code",
"source": [
"train_df['polarity'] = train_df['polarity'] - 1\n",
"test_df['polarity'] = test_df['polarity'] - 1"
],
"id": "6422603a3655706f",
"outputs": [],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:29.010697Z",
"start_time": "2024-06-10T19:14:28.999510Z"
}
},
"cell_type": "code",
"source": "train_df",
"id": "730673ebe3d09e3f",
"outputs": [
{
"data": {
"text/plain": [
" polarity text\n",
"3281328 1 Excellent home help for parents Volume 1 of Do...\n",
"2662721 0 Stay far, far away. I made it through about 6,...\n",
"1600544 0 Lost Woods Lost WoodsI didn't really understan...\n",
"815246 0 Renaissance -12, Women's brown suead shoes Rec...\n",
"1254178 1 Best Novel I've Read This Year Intrigued by th...\n",
"... ... ...\n",
"1132008 1 Pleasant, eclectic mix of coffee-shop favorite...\n",
"1712954 1 A Valuable Text This is not light reading. It ...\n",
"3191827 0 NOT GOOD TO USE A WORKOUT TO MAKE ADVERTICING ...\n",
"1692342 1 Good Read David Wellington brings a new twist ...\n",
"1944752 1 Edge of Paradise: America in Micronesia B.C. h...\n",
"\n",
"[40000 rows x 2 columns]"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\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>polarity</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3281328</th>\n",
" <td>1</td>\n",
" <td>Excellent home help for parents Volume 1 of Do...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2662721</th>\n",
" <td>0</td>\n",
" <td>Stay far, far away. I made it through about 6,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1600544</th>\n",
" <td>0</td>\n",
" <td>Lost Woods Lost WoodsI didn't really understan...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>815246</th>\n",
" <td>0</td>\n",
" <td>Renaissance -12, Women's brown suead shoes Rec...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1254178</th>\n",
" <td>1</td>\n",
" <td>Best Novel I've Read This Year Intrigued by th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1132008</th>\n",
" <td>1</td>\n",
" <td>Pleasant, eclectic mix of coffee-shop favorite...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1712954</th>\n",
" <td>1</td>\n",
" <td>A Valuable Text This is not light reading. It ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3191827</th>\n",
" <td>0</td>\n",
" <td>NOT GOOD TO USE A WORKOUT TO MAKE ADVERTICING ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1692342</th>\n",
" <td>1</td>\n",
" <td>Good Read David Wellington brings a new twist ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1944752</th>\n",
" <td>1</td>\n",
" <td>Edge of Paradise: America in Micronesia B.C. h...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>40000 rows × 2 columns</p>\n",
"</div>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:31.756539Z",
"start_time": "2024-06-10T19:14:29.011700Z"
}
},
"cell_type": "code",
"source": [
"# TF-IDF\n",
"tfidf_vectorizer = TfidfVectorizer(max_features=10000)\n",
"X_train_tfidf = tfidf_vectorizer.fit_transform(train_df['text'])\n",
"X_test_tfidf = tfidf_vectorizer.transform(test_df['text'])"
],
"id": "97c3ff76588ab69c",
"outputs": [],
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:43.754163Z",
"start_time": "2024-06-10T19:14:31.756932Z"
}
},
"cell_type": "code",
"source": [
"# Word2Vec\n",
"def tokenize(text):\n",
" return text.split()\n",
"\n",
"train_df['tokens'] = train_df['text'].apply(tokenize)\n",
"test_df['tokens'] = test_df['text'].apply(tokenize)\n",
"\n",
"w2v_model = Word2Vec(sentences=train_df['tokens'], vector_size=100, window=5, min_count=5, workers=4)\n",
"\n",
"def get_avg_w2v(tokens, model):\n",
" vectors = [model.wv[token] for token in tokens if token in model.wv]\n",
" if len(vectors) == 0:\n",
" return np.zeros(model.vector_size)\n",
" return np.mean(vectors, axis=0)\n",
"\n",
"X_train_w2v = np.array([get_avg_w2v(tokens, w2v_model) for tokens in train_df['tokens']])\n",
"X_test_w2v = np.array([get_avg_w2v(tokens, w2v_model) for tokens in test_df['tokens']])"
],
"id": "d5d352229d623c1a",
"outputs": [],
"execution_count": 8
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:14:43.760194Z",
"start_time": "2024-06-10T19:14:43.755159Z"
}
},
"cell_type": "code",
"source": [
"# Klasyfikatory\n",
"log_reg = LogisticRegression(max_iter=1000)\n",
"rf_clf = RandomForestClassifier(n_estimators=100, random_state=1)"
],
"id": "e3879b5ac92d0ad",
"outputs": [],
"execution_count": 9
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:16:03.926892Z",
"start_time": "2024-06-10T19:14:43.761179Z"
}
},
"cell_type": "code",
"source": [
"# TF-IDF\n",
"log_reg.fit(X_train_tfidf, train_df['polarity'])\n",
"rf_clf.fit(X_train_tfidf, train_df['polarity'])\n",
"\n",
"y_pred_log_reg_tfidf = log_reg.predict(X_test_tfidf)\n",
"y_pred_rf_clf_tfidf = rf_clf.predict(X_test_tfidf)"
],
"id": "163ddf27adbeecdf",
"outputs": [],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:16:46.644728Z",
"start_time": "2024-06-10T19:16:03.928399Z"
}
},
"cell_type": "code",
"source": [
"# Word2Vec\n",
"log_reg.fit(X_train_w2v, train_df['polarity'])\n",
"rf_clf.fit(X_train_w2v, train_df['polarity'])\n",
"\n",
"y_pred_log_reg_w2v = log_reg.predict(X_test_w2v)\n",
"y_pred_rf_clf_w2v = rf_clf.predict(X_test_w2v)"
],
"id": "5f3ed725746bfeee",
"outputs": [],
"execution_count": 11
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:23:29.664360Z",
"start_time": "2024-06-10T19:23:29.659366Z"
}
},
"cell_type": "code",
"source": [
"def display_metrics(y_true, y_pred, name):\n",
" accuracy = accuracy_score(y_true, y_pred)\n",
" precision = precision_score(y_true, y_pred)\n",
" recall = recall_score(y_true, y_pred)\n",
" f1 = f1_score(y_true, y_pred)\n",
" print(f\"{name} Metrics:\")\n",
" print(f\"Accuracy: {accuracy:.4f}\")\n",
" print(f\"Precision: {precision:.4f}\")\n",
" print(f\"Recall: {recall:.4f}\")\n",
" print(f\"F1-score: {f1:.4f}\")\n",
" print()\n"
],
"id": "427a5c4c145ab33f",
"outputs": [],
"execution_count": 15
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-06-10T19:23:51.473027Z",
"start_time": "2024-06-10T19:23:51.420072Z"
}
},
"cell_type": "code",
"source": [
"# TF-IDF + Regresja Logistyczna\n",
"display_metrics(test_df['polarity'], y_pred_log_reg_tfidf, \"TF-IDF + Logistic Regression\")\n",
"\n",
"# TF-IDF + Random Forest\n",
"display_metrics(test_df['polarity'], y_pred_rf_clf_tfidf, \"TF-IDF + Random Forest\")\n",
"\n",
"# Word2Vec + Regresja Logistyczna\n",
"display_metrics(test_df['polarity'], y_pred_log_reg_w2v, \"Word2Vec + Logistic Regression\")\n",
"\n",
"# Word2Vec + Random Forest\n",
"display_metrics(test_df['polarity'], y_pred_rf_clf_w2v, \"Word2Vec + Random Forest\")"
],
"id": "118be083db9e3ed2",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TF-IDF + Logistic Regression Metrics:\n",
"Accuracy: 0.8865\n",
"Precision: 0.8851\n",
"Recall: 0.8898\n",
"F1-score: 0.8874\n",
"\n",
"TF-IDF + Random Forest Metrics:\n",
"Accuracy: 0.8504\n",
"Precision: 0.8668\n",
"Recall: 0.8300\n",
"F1-score: 0.8480\n",
"\n",
"Word2Vec + Logistic Regression Metrics:\n",
"Accuracy: 0.7906\n",
"Precision: 0.7964\n",
"Recall: 0.7840\n",
"F1-score: 0.7901\n",
"\n",
"Word2Vec + Random Forest Metrics:\n",
"Accuracy: 0.7546\n",
"Precision: 0.7643\n",
"Recall: 0.7403\n",
"F1-score: 0.7521\n",
"\n"
]
}
],
"execution_count": 18
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "572452dcd6ba100b"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink