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