Add data splitting and simple model

src/fnc2.py

@@ -0,0 +1,74 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import classification_report
+
+# Load parquet first, fall back to CSV if not available
+def load_split(file_prefix, split_name):
+    try:
+        print(f"📚 Loading data from Parquet file at '{file_prefix}_{split_name}.parquet'")
+        return pd.read_parquet(f"{file_prefix}_{split_name}.parquet")
+    except FileNotFoundError:
+        print(f"❌ Error: Neither Parquet nor CSV file found at {file_prefix}_{split_name}.parquet or {file_prefix}_{split_name}.csv")
+        exit()
+    except:
+        print(f"🔄 Parquet file not found, loading from CSV at '{file_prefix}_{split_name}.csv'")
+        return pd.read_csv(f"{file_prefix}_{split_name}.csv")
+
+train = load_split("../data/sampled_fakenews", "train")
+val = load_split("../data/sampled_fakenews", "valid") 
+test = load_split("../data/sampled_fakenews", "test")
+
+# "Political" and "bias" may not be inherently fake, and "unknown" is neutral
+print("🧮 Grouping into binary classes...")
+fake_labels = {'fake', 'conspiracy', 'rumor', 'unreliable', 'junksci', 'hate', 'satire', 'clickbait'}
+for df in [train, val, test]:
+    df['label'] = df['type'].apply(lambda x: 1 if x in fake_labels else 0)
+
+print("🪙 Preprocessing text...")
+tfidf = TfidfVectorizer(max_features=5000)
+
+X_train = tfidf.fit_transform(train['processed_text'])
+X_val = tfidf.transform(val['processed_text'])
+X_test = tfidf.transform(test['processed_text'])
+
+y_train = train['label']
+y_val = val['label']
+y_test = test['label']
+
+print("🔍 Training models...")
+lr = LogisticRegression(C=10, max_iter=100, class_weight=None, random_state=42)
+lr.fit(X_train, y_train)
+
+nb = MultinomialNB(alpha=0.1, fit_prior=True)
+nb.fit(X_train, y_train)
+
+y_test_pred_lr = lr.predict(X_test)
+print("\n📊 Logistic Regression Test Performance:")
+print(classification_report(y_test, y_test_pred_lr, target_names=['Reliable', 'Fake']))
+
+y_test_pred_nb = nb.predict(X_test)
+print("\n📊 Naïve Bayes Test Performance:")
+print(classification_report(y_test, y_test_pred_nb, target_names=['Reliable', 'Fake']))
+
+# Function to perform hyperparameter tuning, not used in the final script
+def hyperparameter_tuning():
+    print("🔍 Hyperparameter tuning...")
+    param_grid_lr = {
+        'C': [0.1, 1, 10],
+        'max_iter': [100, 500, 1000],
+        'class_weight': ['balanced', None]
+    }
+    grid = GridSearchCV(LogisticRegression(), param_grid_lr, cv=3)
+    grid.fit(X_train, y_train)
+    print("✅ Best Logistic Regression Parameters:", grid.best_params_)
+
+    param_grid_nb = {
+        'alpha': [0.1, 0.5, 1.0, 2.0],
+        'fit_prior': [True, False]
+    }
+    grid_nb = GridSearchCV(MultinomialNB(), param_grid_nb, cv=3) 
+    grid_nb.fit(X_val, y_val)
+    print("✅ Best Naïve Bayes Parameters:", grid_nb.best_params_)