{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biblioteki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wczytanie danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('Train.csv')\n",
    "valid_data = pd.read_csv('Valid.csv')\n",
    "test_data = pd.read_csv('Test.csv')\n",
    "\n",
    "X_train = train_data['text']\n",
    "y_train = train_data['label']\n",
    "X_valid = valid_data['text']\n",
    "y_valid = valid_data['label']\n",
    "X_test = test_data['text']\n",
    "y_test = test_data['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naiwny klasyfikator bayesowski z wektoryzacją TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dokładność na zbiorze walidacyjnym: 0.8616\n",
      "Dokładność na zbiorze testowym: 0.8670\n"
     ]
    }
   ],
   "source": [
    "# Stworzenie pipeline do przetwarzania tekstu i uczenia modelu\n",
    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
    "\n",
    "# Trenowanie modelu\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Ewaluacja modelu\n",
    "y_valid_pred = model.predict(X_valid)\n",
    "valid_accuracy = accuracy_score(y_valid, y_valid_pred)\n",
    "\n",
    "y_test_pred = model.predict(X_test)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "\n",
    "print(f'Dokładność na zbiorze walidacyjnym: {valid_accuracy:.4f}')\n",
    "print(f'Dokładność na zbiorze testowym: {test_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naiwny klasyfikator bayesowski z osadzeniami słów (Word2Vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dokładność na zbiorze walidacyjnym (Word2Vec): 0.7584\n",
      "Dokładność na zbiorze testowym (Word2Vec): 0.7644\n"
     ]
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "def tokenize(text):\n",
    "    tokens = word_tokenize(text.lower())\n",
    "    tokens = [word for word in tokens if word.isalnum()]\n",
    "    tokens = [word for word in tokens if word not in stop_words]\n",
    "    return tokens\n",
    "\n",
    "X_train_tokens = [tokenize(review) for review in X_train]\n",
    "X_valid_tokens = [tokenize(review) for review in X_valid]\n",
    "X_test_tokens = [tokenize(review) for review in X_test]\n",
    "\n",
    "w2v_model = Word2Vec(sentences=X_train_tokens, vector_size=100, window=5, min_count=1, workers=4)\n",
    "\n",
    "# Funkcja do konwersji recenzji na osadzenia słów\n",
    "def document_vector(tokens, model):\n",
    "    vec = [model.wv[word] for word in tokens if word in model.wv]\n",
    "    return np.mean(vec, axis=0) if len(vec) > 0 else np.zeros(model.vector_size)\n",
    "\n",
    "X_train_vectors = np.array([document_vector(tokens, w2v_model) for tokens in X_train_tokens])\n",
    "X_valid_vectors = np.array([document_vector(tokens, w2v_model) for tokens in X_valid_tokens])\n",
    "X_test_vectors = np.array([document_vector(tokens, w2v_model) for tokens in X_test_tokens])\n",
    "\n",
    "# Klasyfikator Naive Bayes\n",
    "model_w2v = GaussianNB()\n",
    "\n",
    "# Trenowanie modelu\n",
    "model_w2v.fit(X_train_vectors, y_train)\n",
    "\n",
    "# Ewaluacja modelu\n",
    "y_valid_pred_w2v = model_w2v.predict(X_valid_vectors)\n",
    "valid_accuracy_w2v = accuracy_score(y_valid, y_valid_pred_w2v)\n",
    "\n",
    "y_test_pred_w2v = model_w2v.predict(X_test_vectors)\n",
    "test_accuracy_w2v = accuracy_score(y_test, y_test_pred_w2v)\n",
    "\n",
    "print(f'Dokładność na zbiorze walidacyjnym (Word2Vec): {valid_accuracy_w2v:.4f}')\n",
    "print(f'Dokładność na zbiorze testowym (Word2Vec): {test_accuracy_w2v:.4f}')"
   ]
  }
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