Inicjalizacja projektu
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commit
b5771dc4b2
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.gitignore
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.gitignore
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*
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!Projekt*
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!Raport*
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!IMDB*
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50001
IMDB_reviews.csv
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50001
IMDB_reviews.csv
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400
Projekt.ipynb
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Projekt.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "LHtKZx0myNWa"
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},
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"source": [
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"### Import bibliotek"
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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": 20,
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"metadata": {
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"id": "ZTlYCCtCyNWc"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.pipeline import Pipeline\n",
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"from gensim.models import Word2Vec\n",
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"from sklearn.base import BaseEstimator, TransformerMixin\n",
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"import numpy as np\n",
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"import re"
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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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"id": "v16vUmROyNWc"
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},
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"source": [
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"### Przygotowanie danych"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_str_cleaned(str_dirty):\n",
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" punctuation = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'\n",
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" new_str = str_dirty.lower()\n",
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" new_str = re.sub(' +', ' ', new_str)\n",
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" for char in punctuation:\n",
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" new_str = new_str.replace(char, '')\n",
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" return new_str"
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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": 22,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "d4Kuyx7JyNWd",
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"outputId": "0c9de8ef-4e90-44fd-9af4-d5e5833994aa"
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},
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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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" review sentiment\n",
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"0 One of the other reviewers has mentioned that ... positive\n",
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"1 A wonderful little production. <br /><br />The... positive\n",
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"2 I thought this was a wonderful way to spend ti... positive\n",
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"3 Basically there's a family where a little boy ... negative\n",
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"4 Petter Mattei's \"Love in the Time of Money\" is... positive\n",
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" review sentiment \\\n",
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"0 One of the other reviewers has mentioned that ... 1 \n",
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"1 A wonderful little production. <br /><br />The... 1 \n",
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"2 I thought this was a wonderful way to spend ti... 1 \n",
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"3 Basically there's a family where a little boy ... 0 \n",
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"4 Petter Mattei's \"Love in the Time of Money\" is... 1 \n",
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"\n",
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" cleaned_review \n",
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"0 one of the other reviewers has mentioned that ... \n",
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"1 a wonderful little production br br the filmin... \n",
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"2 i thought this was a wonderful way to spend ti... \n",
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"3 basically theres a family where a little boy j... \n",
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"4 petter matteis love in the time of money is a ... \n"
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]
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}
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],
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"source": [
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"data = pd.read_csv('IMDB_reviews.csv')\n",
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"print(data.head())\n",
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"\n",
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"# Czyszczenie danych\n",
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"data['cleaned_review'] = data['review'].apply(get_str_cleaned)\n",
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"\n",
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"# Przekształcenie etykiet na format numeryczny\n",
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"label_encoder = LabelEncoder()\n",
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"data['sentiment'] = label_encoder.fit_transform(data['sentiment'])\n",
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"\n",
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"print(data.head())\n",
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"\n",
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"# Podział danych na zbiór treningowy i testowy\n",
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"X = data['cleaned_review']\n",
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"y = data['sentiment']\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\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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"id": "8Lz-Y4ZCyNWd"
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},
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"source": [
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"### TF-IDF + SVM"
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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": 23,
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"metadata": {
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"id": "ES_5Q4BEyNWd"
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},
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"outputs": [],
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"source": [
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"tfidf_svm_pipeline = Pipeline([\n",
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" ('tfidf', TfidfVectorizer(max_features=200)),\n",
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" ('svm', SVC(kernel='linear'))\n",
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"])\n",
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"tfidf_svm_pipeline.fit(X_train, y_train)\n",
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"y_pred_tfidf_svm = tfidf_svm_pipeline.predict(X_test)"
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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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"id": "fadLd3cEyNWd"
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},
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"source": [
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"### TF-IDF + RandomForest"
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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": 24,
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"metadata": {
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"id": "xUq30-FryNWe"
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},
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"outputs": [],
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"source": [
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"tfidf_rf_pipeline = Pipeline([\n",
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" ('tfidf', TfidfVectorizer(max_features=200)),\n",
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" ('rf', RandomForestClassifier(n_estimators=100))\n",
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"])\n",
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"tfidf_rf_pipeline.fit(X_train, y_train)\n",
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"y_pred_tfidf_rf = tfidf_rf_pipeline.predict(X_test)"
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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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"id": "d08OJrCnyNWe"
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},
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"source": [
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"### Model Word2Vec i transformator dokumentów do postaci wektorowej"
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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": 25,
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"metadata": {
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"id": "J5agaWJFyNWe"
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},
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"outputs": [],
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"source": [
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"w2v_model = Word2Vec(sentences=[doc.split() for doc in X_train], vector_size=200, window=5, min_count=5, workers=4)\n",
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"class Word2VecTransformer(BaseEstimator, TransformerMixin):\n",
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" def __init__(self, w2v_model):\n",
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" self.w2v_model = w2v_model\n",
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"\n",
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" def fit(self, X, y=None):\n",
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" return self\n",
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"\n",
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" def transform(self, X):\n",
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" return np.array([\n",
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" np.mean([self.w2v_model.wv[word] for word in doc.split() if word in self.w2v_model.wv]\n",
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" or [np.zeros(self.w2v_model.vector_size)], axis=0)\n",
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" for doc in X\n",
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" ])"
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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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"id": "KbKeeZBdyNWe"
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},
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"source": [
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"### Word2Vec + SVM"
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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": 26,
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"metadata": {
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"id": "FPBL7g75yNWe"
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},
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"outputs": [],
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"source": [
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"w2v_svm_pipeline = Pipeline([\n",
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" ('w2v_transform', Word2VecTransformer(w2v_model)),\n",
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" ('svm', SVC(kernel='linear'))\n",
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"])\n",
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"w2v_svm_pipeline.fit(X_train, y_train)\n",
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"y_pred_w2v_svm = w2v_svm_pipeline.predict(X_test)"
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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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"id": "KT-Cnwx7yNWe"
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},
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"source": [
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"### Word2Vec + RandomForest"
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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": 27,
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"metadata": {
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"id": "t9mCasDmyNWe"
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},
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"outputs": [],
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"source": [
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"w2v_rf_pipeline = Pipeline([\n",
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" ('w2v_transform', Word2VecTransformer(w2v_model)),\n",
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" ('rf', RandomForestClassifier(n_estimators=100))\n",
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"])\n",
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"w2v_rf_pipeline.fit(X_train, y_train)\n",
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"y_pred_w2v_rf = w2v_rf_pipeline.predict(X_test)"
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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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"id": "lkFzZ1MjyNWf"
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},
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"source": [
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"### Wyświetlanie metryk"
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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": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_scores(y_true, y_pred):\n",
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" # Funkcja zwraca trafność, precyzję, pokrycie i F1\n",
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" acc_score = 0\n",
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" acc_total = 0\n",
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" tp = 0\n",
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" fp = 0\n",
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" selected_items = 0\n",
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" relevant_items = 0\n",
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"\n",
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" for p, t in zip(y_pred, y_true):\n",
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" acc_total += 1\n",
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"\n",
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" if p == t:\n",
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" acc_score += 1\n",
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"\n",
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" if p > 0 and p == t:\n",
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" tp += 1\n",
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"\n",
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" if p > 0:\n",
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" selected_items += 1\n",
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"\n",
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" if t > 0:\n",
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" relevant_items += 1\n",
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"\n",
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" accuracy = acc_score / acc_total\n",
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"\n",
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" if selected_items == 0:\n",
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" precision = 1.0\n",
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" else:\n",
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" precision = tp / selected_items\n",
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"\n",
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" if relevant_items == 0:\n",
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" recall = 1.0\n",
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" else:\n",
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" recall = tp / relevant_items\n",
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"\n",
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" if precision + recall == 0.0:\n",
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" f1 = 0.0\n",
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" else:\n",
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" f1 = 2 * precision * recall / (precision + recall)\n",
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"\n",
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" return accuracy, precision, recall, f1"
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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": 29,
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"metadata": {
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"id": "W8RJkm0CyNWf"
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},
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"outputs": [],
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"source": [
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"def print_metrics(y_true, y_pred, model_name):\n",
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" accuracy, precision, recall, f1 = get_scores(y_true, y_pred)\n",
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" print(f'{model_name} Accuracy: {accuracy:.4f}')\n",
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" print(f'{model_name} Precision: {precision:.4f}')\n",
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" print(f'{model_name} Recall: {recall:.4f}')\n",
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" print(f'{model_name} F1-Score: {f1:.4f}')\n",
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" print('-' * 30)"
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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": 30,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "tPPkR8MOyNWf",
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"outputId": "ceae2217-10b0-4533-9f43-3c7add2d19b4"
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},
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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 + SVM Accuracy: 0.7764\n",
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"TF-IDF + SVM Precision: 0.7719\n",
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"TF-IDF + SVM Recall: 0.7896\n",
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"TF-IDF + SVM F1-Score: 0.7807\n",
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"------------------------------\n",
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"TF-IDF + Random Forest Accuracy: 0.7500\n",
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"TF-IDF + Random Forest Precision: 0.7626\n",
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"TF-IDF + Random Forest Recall: 0.7317\n",
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"TF-IDF + Random Forest F1-Score: 0.7468\n",
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"------------------------------\n",
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"Word2Vec + SVM Accuracy: 0.8584\n",
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"Word2Vec + SVM Precision: 0.8522\n",
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"Word2Vec + SVM Recall: 0.8698\n",
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"Word2Vec + SVM F1-Score: 0.8609\n",
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"------------------------------\n",
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"Word2Vec + Random Forest Accuracy: 0.8137\n",
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"Word2Vec + Random Forest Precision: 0.8106\n",
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"Word2Vec + Random Forest Recall: 0.8224\n",
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"Word2Vec + Random Forest F1-Score: 0.8165\n",
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"------------------------------\n"
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]
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}
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],
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"source": [
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"# Ocena modelu TF-IDF + SVM\n",
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"print_metrics(y_test, y_pred_tfidf_svm, 'TF-IDF + SVM')\n",
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"\n",
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"# Ocena modelu TF-IDF + Random Forest\n",
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"print_metrics(y_test, y_pred_tfidf_rf, 'TF-IDF + Random Forest')\n",
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"\n",
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"# Ocena modelu Word2Vec + SVM\n",
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"print_metrics(y_test, y_pred_w2v_svm, 'Word2Vec + SVM')\n",
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"\n",
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"# Ocena modelu Word2Vec + Random Forest\n",
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"print_metrics(y_test, y_pred_w2v_rf, 'Word2Vec + Random Forest')"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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BIN
Raport.docx
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BIN
Raport.docx
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