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implementation1.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import csv
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, confusion_matrix
+
+file_path = 'Test-3.tsv'
+data = pd.read_csv(file_path, sep="\t", names=["Sentence", "Label"], skiprows=1, quoting=csv.QUOTE_NONE, encoding="utf-8")
+data.columns = data.columns.str.strip()
+
+data = data.dropna(subset=['Sentence', 'Label'])
+data['Sentence'] = data['Sentence'].astype(str)
+
+X = data['Sentence']
+y = data['Label']
+
+vectorizer = TfidfVectorizer(ngram_range=(1, 2), max_features=5000)
+X_tfidf = vectorizer.fit_transform(X)
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X_tfidf, y, test_size=0.3, random_state=42, stratify=y)
+
+plt.figure(figsize=(8, 6))
+y.value_counts().sort_index().plot(kind='bar', color='skyblue')
+plt.title('Class Distribution')
+plt.xlabel('Class')
+plt.ylabel('Frequency')
+plt.xticks(rotation=0)
+plt.tight_layout()
+plt.savefig('class_distribution.png')
+plt.close()
+
+svm_model = SVC(kernel='rbf', degree=3, random_state=42, class_weight='balanced')
+svm_model.fit(X_train, y_train)
+svm_predictions = svm_model.predict(X_test)
+
+print("SVM Model Performance:")
+print(f"Precision: {precision_score(y_test, svm_predictions, average='weighted', zero_division=0):.4f}")
+print(f"Recall:    {recall_score(y_test, svm_predictions, average='weighted', zero_division=0):.4f}")
+print(f"F1-Score:  {f1_score(y_test, svm_predictions, average='weighted', zero_division=0):.4f}")
+print(f"Accuracy:  {accuracy_score(y_test, svm_predictions):.4f}")
+
+param_grid = {'n_neighbors': list(range(3, 21, 2))}
+knn = KNeighborsClassifier()
+grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')
+grid_search.fit(X_train, y_train)
+
+best_knn = grid_search.best_estimator_
+knn_predictions = best_knn.predict(X_test)
+
+print("\nKNN Model Performance:")
+print(f"Best k:    {grid_search.best_params_['n_neighbors']}")
+print(f"Precision: {precision_score(y_test, knn_predictions, average='weighted', zero_division=0):.4f}")
+print(f"Recall:    {recall_score(y_test, knn_predictions, average='weighted', zero_division=0):.4f}")
+print(f"F1-Score:  {f1_score(y_test, knn_predictions, average='weighted', zero_division=0):.4f}")
+print(f"Accuracy:  {accuracy_score(y_test, knn_predictions):.4f}")
+
+def plot_conf_matrix(cm, title, filename):
+    plt.figure(figsize=(6, 5))
+    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = range(len(cm))
+    plt.xticks(tick_marks, tick_marks)
+    plt.yticks(tick_marks, tick_marks)
+    plt.xlabel('Predicted Label')
+    plt.ylabel('True Label')
+
+    thresh = cm.max() / 2.
+    for i in range(cm.shape[0]):
+        for j in range(cm.shape[1]):
+            plt.text(j, i, str(cm[i, j]),
+                     ha='center', va='center',
+                     color='white' if cm[i, j] > thresh else 'black')
+
+    plt.tight_layout()
+    plt.savefig(filename)
+    plt.close()
+
+plot_conf_matrix(confusion_matrix(y_test, svm_predictions), 'SVM Confusion Matrix', 'svm_conf_matrix.png')
+plot_conf_matrix(confusion_matrix(y_test, knn_predictions), 'KNN Confusion Matrix', 'knn_conf_matrix.png')

results.md

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+| # | method | algorithm | train | Test 1: group 1Precision, recall, f1, accuracy | Test 2: group 2 | Test 3: group 3 |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1.a.i | Machine learning (2 methods) | SVM | Train 1 or 2 or 3 / [respective own train set] | Precision: 0.6301 Recall: 0.6684 F1-Score: 0.5532 Accuracy: 0.6684 | Precision: 0.5402 Recall: 0.6099 F1-Score: 0.4935 Accuracy: 0.6099 | Precision: 0.4200 Recall: 0.6481 F1-Score: 0.5097 Accuracy: 0.6481 |
+| 1.a.ii |  | SVM | TRAIN | Precision: 0.6119 Recall: 0.6782 F1-Score: 0.6182 Accuracy: 0.6782 | Precision: 0.5699 Recall: 0.6222 F1-Score: 0.5624 Accuracy: 0.6222 | |
+| 1 b.i |  | K-Nearest Neighbors (KNN) | Train 1 or 2 or 3 / [respective own train] | Precision: 0.5941 Recall: 0.6684 F1-Score: 0.5544 Accuracy: 0.6684 | Precision: 0.4766 Recall: 0.5964 F1-Score: 0.4870 Accuracy: 0.5964 | Precision: 0.5098 Recall: 0.6567 F1-Score: 0.5366 Accuracy: 0.6567 |
+| 1.b.ii |  | KNN | TRAIN | Precision: 0.5066 Recall: 0.6398 F1-Score: 0.5233 Accuracy: 0.6398 | Precision: 0.5641 Recall: 0.6117 F1-Score: 0.5479 Accuracy: 0.6117 |  |

train.py

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+import csv
+
+filenames = ['Train-1.tsv', 'Train-2.tsv', 'Train-3.tsv']
+
+TRAIN = set()
+
+for filename in filenames:
+    with open(filename, 'r', encoding='utf-8') as file:
+        tsv_reader = csv.reader(file, delimiter='\t')
+        header = next(tsv_reader)
+        try:
+            sentence_idx = header.index("Sentence")
+            label_idx = header.index("Label")
+        except ValueError:
+            raise ValueError(f"Required columns not found in {filename}")
+
+        for row in tsv_reader:
+            if len(row) > max(sentence_idx, label_idx):  
+                TRAIN.add((row[sentence_idx], row[label_idx]))
+
+with open('Combined_Train.tsv', 'w', encoding='utf-8', newline='') as outfile:
+    tsv_writer = csv.writer(outfile, delimiter='\t')
+    tsv_writer.writerow(["Sentence", "Label"])  
+    for row in TRAIN:
+        tsv_writer.writerow(row)
+
+print(f"Combined TRAIN set written to 'Combined_Train.tsv' with {len(TRAIN)} unique rows.")