Refactor data loading and hyperparameter tuning for improved model performance

train.py

@@ -17,7 +17,7 @@ from cfg import DROP_LIST
 
 # Rest of the imports and class definitions remain unchanged...
 
-def load_and_balance_data(filename, ratio=1/30):
+def load_and_balance_data(filename, ratio=1/60):
     """
     Loads data from a CSV file and balances classes to address potential imbalance.
     
@@ -55,10 +55,10 @@ def create_stock_prediction_pipeline(params=None):
     
     # Use default parameters if none provided
     if params is None:
-        classifier = XGBClassifier(booster="dart", n_jobs=-1)
+        classifier = XGBClassifier(n_jobs=-1)
     else:
         classifier = XGBClassifier(
-            booster="dart",
+            # booster="dart",
             n_jobs=-1,
             **params
         )
@@ -74,9 +74,10 @@ def objective(trial, X, y):
     
     # Define the hyperparameters to tune
     params = {
-        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),
-        'max_depth': trial.suggest_int('max_depth', 3, 10),
-        'n_estimators': trial.suggest_int('n_estimators', 50, 300),
+        # 'booster': trial.suggest_categorical('booster', ['gbtree', 'dart']),
+        'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.3, log=True),
+        'max_depth': trial.suggest_int('max_depth', 3, 140),
+        'n_estimators': trial.suggest_int('n_estimators', 50, 3000),
         'subsample': trial.suggest_float('subsample', 0.5, 1.0),
         'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),
         'gamma': trial.suggest_float('gamma', 0, 5),
@@ -97,7 +98,7 @@ def objective(trial, X, y):
 def main():
     # Load and preprocess training data
     print("Loading and preprocessing training data...")
-    combined = load_and_balance_data('./cleaned_training.csv', ratio=1/30)
+    combined = load_and_balance_data('./cleaned_training.csv', ratio=1/90)
     
     # Define features and target
     X = combined.drop(DROP_LIST)
@@ -109,7 +110,7 @@ def main():
     # Hyperparameter tuning with Optuna
     print("Starting hyperparameter optimization with Optuna...")
     study = optuna.create_study(direction='maximize')  # We want to maximize the F1 score
-    study.optimize(lambda trial: objective(trial, X_train, y_train), n_trials=20)
+    study.optimize(lambda trial: objective(trial, X_train, y_train), n_trials=100, show_progress_bar=True)
     
     # Print the best parameters
     best_params = study.best_params