refactor: reorganize project structure and enhance model verbosity

research/models/lightgbm_model.py

@@ -22,7 +22,7 @@ class LightGBMModel(TraditionalModel):
             subsample=params.get("subsample", 0.8),
             colsample_bytree=params.get("colsample_bytree", 0.8),
             random_state=self.config.random_seed,
-            verbose=-1,
+            verbose=2,
         )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray:

research/models/logistic_regression_model.py

@@ -20,7 +20,9 @@ class LogisticRegressionModel(TraditionalModel):
         )
 
         classifier = LogisticRegression(
-            max_iter=params.get("max_iter", 1000), random_state=self.config.random_seed
+            max_iter=params.get("max_iter", 1000),
+            random_state=self.config.random_seed,
+            verbose=2
         )
 
         return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])

research/models/random_forest_model.py

@@ -18,6 +18,7 @@ class RandomForestModel(TraditionalModel):
             n_estimators=params.get("n_estimators", 100),
             max_depth=params.get("max_depth", None),
             random_state=self.config.random_seed,
+            verbose=2
         )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray:

research/models/svm_model.py

@@ -25,6 +25,7 @@ class SVMModel(TraditionalModel):
             gamma=params.get("gamma", "scale"),
             probability=True,  # Enable probability prediction
             random_state=self.config.random_seed,
+            verbose=2
         )
 
         return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])

research/models/xgboost_model.py

@@ -22,6 +22,7 @@ class XGBoostModel(TraditionalModel):
             colsample_bytree=params.get("colsample_bytree", 0.8),
             random_state=self.config.random_seed,
             eval_metric="logloss",
+            verbosity=2
         )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray: