{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_data(file_path):\n",
    "    texts = []\n",
    "    labels = []\n",
    "    with open(file_path, 'r', encoding='utf-8') as file:\n",
    "        for line in file:\n",
    "            parts = line.strip().split('\\t')\n",
    "            texts.append(parts[1])\n",
    "            labels.append(int(parts[0]))\n",
    "    return texts, labels\n",
    "\n",
    "train_texts, train_labels = load_train_data('train.tsv')\n",
    "sentences = [text.split() for text in train_texts]\n",
    "word2vec_model = Word2Vec(sentences=sentences, vector_size=100, window=5, min_count=1, workers=4)\n",
    "word2vec_model.save(\"word2vec.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_test_data(file_name):\n",
    "    with open(file_name, 'r', encoding='utf-8') as file:\n",
    "        file_content = []\n",
    "        lines = file.readlines()\n",
    "        for line in lines:\n",
    "            file_content.append(line.strip().split('\\t')[0])\n",
    "        return file_content\n",
    "\n",
    "test_texts = load_test_data(\"dev-0/in.tsv\")\n",
    "test_labels = np.array(load_test_data(\"dev-0/expected.tsv\")).astype(int)\n",
    "test_a_texts = load_test_data(\"test-A/in.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9601980924431401\n"
     ]
    }
   ],
   "source": [
    "def text_to_vector(text):\n",
    "    words = text.split()\n",
    "    vector = np.zeros(word2vec_model.vector_size)\n",
    "    count = 0\n",
    "    for word in words:\n",
    "        if word in word2vec_model.wv:\n",
    "            vector += word2vec_model.wv.get_vector(word)\n",
    "            count += 1\n",
    "    if count != 0:\n",
    "        vector /= count\n",
    "    return vector\n",
    "\n",
    "\n",
    "word2vec_model_path = \"word2vec.model\"\n",
    "word2vec_model_loaded = Word2Vec.load(word2vec_model_path)\n",
    "\n",
    "train_word2vec = np.array([text_to_vector(text) for text in train_texts])\n",
    "test_word2vec = np.array([text_to_vector(text) for text in test_texts])\n",
    "test_a_word2vec = np.array([text_to_vector(text) for text in test_a_texts])\n",
    "\n",
    "classifier = RandomForestClassifier()\n",
    "\n",
    "classifier.fit(train_word2vec, train_labels)\n",
    "\n",
    "predictions = classifier.predict(test_word2vec)\n",
    "predictions_test_a = classifier.predict(test_a_word2vec)\n",
    "\n",
    "accuracy = accuracy_score(test_labels, predictions)\n",
    "print(\"Test Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dev-0/out.tsv', 'w', newline='', encoding='utf-8') as f:\n",
    "    writer = csv.writer(f, delimiter='\\t')\n",
    "    print(\"Accuracy: \" + str(accuracy), file=f)\n",
    "    for prediction in predictions:\n",
    "        writer.writerow([prediction])\n",
    "\n",
    "with open('test-A/out.tsv', 'w', newline='', encoding='utf-8') as f:\n",
    "    writer = csv.writer(f, delimiter='\\t')\n",
    "    for prediction in predictions_test_a:\n",
    "        writer.writerow([prediction])"
   ]
  }
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