{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ca825e7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Treniranje modela: Logistic regression...\n", "Predikcija na Test 1...\n", "Predikcija na Test 2...\n", "Predikcija na Test 3...\n", "\n", "Treniranje modela: SVM RBF kernel...\n", "Predikcija na Test 1...\n", "Predikcija na Test 2...\n", "Predikcija na Test 3...\n", "\n", "| # | method | algorithm | skup | Test 1 | Test 2 | Test 3 |\n", "|--------|------------------|--------------------------|--------------|---------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------|\n", "| 1.a.i | Machine learning | Logistic regression | Train combined | Precision=0.640, Recall=0.614, F1=0.625, Accuracy=0.614 | Precision=0.632, Recall=0.630, F1=0.626, Accuracy=0.630 | Precision=0.717, Recall=0.691, F1=0.686, Accuracy=0.691 |\n", "| 1.b.i | Machine learning | SVM RBF kernel | Train combined | Precision=0.652, Recall=0.632, F1=0.640, Accuracy=0.632 | Precision=0.621, Recall=0.626, F1=0.620, Accuracy=0.626 | Precision=0.764, Recall=0.741, F1=0.735, Accuracy=0.741 |\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "import matplotlib.pyplot as plt\n", "import os\n", "\n", "# Folder za spremanje confusion matrica\n", "os.makedirs('confusion_matrices', exist_ok=True)\n", "\n", "# Definicije datoteka\n", "train_files = ['train-1.csv', 'train-2.csv', 'train-3.csv']\n", "test_files = ['test-1.csv', 'test-2.csv', 'test-3.csv']\n", "test_names = ['Test 1', 'Test 2', 'Test 3']\n", "\n", "text_column = 'Sentence'\n", "target_column = 'Label'\n", "\n", "# Funkcija za učitavanje podataka\n", "def load_data(file):\n", " df = pd.read_csv(file)\n", " X_text = df[text_column].astype(str)\n", " y = df[target_column]\n", " return X_text, y\n", "\n", "# Funkcija za učitavanje i spajanje više train setova\n", "def load_data_combined(files):\n", " X_all = []\n", " y_all = []\n", " for file in files:\n", " X_text, y = load_data(file)\n", " X_all.extend(X_text)\n", " y_all.extend(y)\n", " return X_all, y_all\n", "\n", "# Ažurirani modeli\n", "models = [\n", " ('1.a', 'Machine learning', 'Logistic regression', LogisticRegression(max_iter=5000, solver='liblinear', class_weight='balanced')),\n", " ('1.b', 'Machine learning', 'SVM RBF kernel', SVC(class_weight='balanced', kernel='rbf', random_state=42))\n", "]\n", "\n", "# Priprema tablice za rezultate\n", "table = []\n", "\n", "# Učitavanje kombiniranih trening podataka\n", "X_train_text, y_train = load_data_combined(train_files)\n", "\n", "# TF-IDF vektorizacija s proširenim parametrima\n", "vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1, 3))\n", "X_train = vectorizer.fit_transform(X_train_text)\n", "\n", "# Treniranje i evaluacija\n", "for code, method, algorithm, model in models:\n", " print(f\"\\nTreniranje modela: {algorithm}...\")\n", " model.fit(X_train, y_train)\n", " \n", " row_train = [f\"{code}.i\", method, algorithm, \"Train combined\"]\n", " for idx, test_file in enumerate(test_files):\n", " print(f\"Predikcija na {test_names[idx]}...\")\n", " X_test_text, y_test = load_data(test_file)\n", " X_test = vectorizer.transform(X_test_text)\n", " \n", " y_pred = model.predict(X_test)\n", " precision = precision_score(y_test, y_pred, average='weighted', zero_division=0)\n", " recall = recall_score(y_test, y_pred, average='weighted', zero_division=0)\n", " f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " metric_str = f\"Precision={precision:.3f}, Recall={recall:.3f}, F1={f1:.3f}, Accuracy={accuracy:.3f}\"\n", " row_train.append(metric_str)\n", " \n", " # Confusion matrix\n", " cm = confusion_matrix(y_test, y_pred)\n", " disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n", " disp.plot(cmap=plt.cm.Blues)\n", " plt.title(f'Confusion Matrix: {algorithm}\\nTrain: Combined Train Test: {test_names[idx]}')\n", " plt.savefig(f'confusion_matrices/cm_{algorithm.replace(\" \", \"_\")}_TrainCombined_{test_names[idx].replace(\" \", \"\")}.png')\n", " plt.close()\n", " table.append(row_train)\n", "\n", "# Ispis tablice u markdown formatu\n", "header = \"| # | method | algorithm | skup | Test 1 | Test 2 | Test 3 |\"\n", "sep = \"|--------|------------------|--------------------------|--------------|---------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------|\"\n", "print(\"\\n\" + header)\n", "print(sep)\n", "for row in table:\n", " print(f\"| {row[0]:<6} | {row[1]:<16} | {row[2]:<24} | {row[3]:<12} | {row[4]:<55} | {row[5]:<55} | {row[6]:<55} |\")\n", "\n", "# Spremi rezultate u .md datoteku\n", "with open('results_group2.md', 'w', encoding='utf-8') as f:\n", " f.write(header + \"\\n\")\n", " f.write(sep + \"\\n\")\n", " for row in table:\n", " f.write(f\"| {row[0]:<6} | {row[1]:<16} | {row[2]:<24} | {row[3]:<12} | {row[4]:<55} | {row[5]:<55} | {row[6]:<55} |\\n\")\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }