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# Import necessary modules
import sys
import os
import numpy as np
from dataclasses import dataclass
from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor, RandomForestRegressor, BaggingRegressor, HistGradientBoostingRegressor
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from xgboost import XGBRegressor
from sklearn.kernel_ridge import KernelRidge
from src.exception import CustomException
from src.logger import logging
from src.utils import save_object, evaluate_models
# Define a configuration class for model trainer settings
@dataclass
class ModelTrainerConfig:
trained_model_file_path: str = os.path.join("artifacts", "model.pkl")
# Define the main class responsible for model training
class ModelTrainer:
def __init__(self):
self.model_trainer_config = ModelTrainerConfig()
def initiate_model_trainer(self, train_array, test_array):
"""
Train and evaluate multiple models to find the best performing one based on a chosen metric.
Parameters:
train_array (array-like): Training data array with features and target.
test_array (array-like): Testing data array with features and target.
Returns:
tuple: A tuple containing the R2 score, MAE, MSE, RMSE of the best model on test data, and the name of the best model.
"""
try:
logging.info("Splitting Training and Testing Input Data.")
# Split the train and test arrays into features and target
X_train, y_train = train_array[:, :-1], train_array[:, -1]
X_test, y_test = test_array[:, :-1], test_array[:, -1]
# Initialize models
models = {
'Random Forest': RandomForestRegressor(random_state=42),
#'Gradient Boosting': GradientBoostingRegressor(random_state=6112024),
#'XGBoost': XGBRegressor(random_state=6112024),
#'AdaBoost': AdaBoostRegressor(random_state=6112024),
#'Bagging': BaggingRegressor(random_state=6112024),
#'Kernel Ridge': KernelRidge(),
#'Hist Gradient Boosting': HistGradientBoostingRegressor(random_state=6112024)
}
# Define parameter grids for hyperparameter tuning
param_grids = {
'Random Forest': {'n_estimators': [100, 200, 300],'max_depth': [None, 10, 20],'min_samples_split': [2, 5, 10],'min_samples_leaf': [1, 2, 4]}
#'Gradient Boosting': {'n_estimators': [100, 200, 300], 'learning_rate': [0.01, 0.1, 0.2], 'max_depth': [3, 4, 5]},
#'XGBoost': {'n_estimators': [100, 200, 300], 'learning_rate': [0.01, 0.1, 0.2], 'max_depth': [3, 4, 5]},
#'AdaBoost': {'n_estimators': [50, 100, 200], 'learning_rate': [0.01, 0.1, 1.0]},
#'Bagging': {'n_estimators': [10, 50, 100], 'max_samples': [0.5, 0.75, 1.0], 'max_features': [0.5, 0.75, 1.0]},
#'Kernel Ridge': {'alpha': [0.1, 1.0, 10.0], 'kernel': ['linear', 'rbf', 'poly'], 'gamma': [0.01, 0.1, 1.0, None]},
#'Hist Gradient Boosting': {'learning_rate': [0.01, 0.1, 0.2], 'max_iter': [100, 200, 300], 'max_depth': [None, 10, 20], 'min_samples_leaf': [20, 50, 100]}
}
# Evaluate models and get a report of their performance
model_report: dict = evaluate_models(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, models=models, param=param_grids)
logging.info("Report on All Models: {model_report}")
# Find the best model based on R2 score
best_model_name = max(model_report, key=lambda k: model_report[k]['Test Metrics']['R2 Score'])
best_model = models[best_model_name]
best_model.fit(X_train, y_train)
# Save the best model to a file
save_object(file_path=self.model_trainer_config.trained_model_file_path, obj=best_model)
logging.info(f"Best Model found: {best_model_name}")
# Fit the best model
predicted = best_model.predict(X_test)
r2 = r2_score(y_test, predicted)
mae = mean_absolute_error(y_test, predicted)
mse = mean_squared_error(y_test, predicted)
rmse = np.sqrt(mse)
return (r2, mae, mse, rmse, best_model_name)
except Exception as e:
# Raise a custom exception if an error occurs
raise CustomException(e, sys)
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