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| # Import necessary modules | |
| import os | |
| import sys | |
| import pickle | |
| from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, accuracy_score, precision_score, recall_score, f1_score | |
| from sklearn.model_selection import GridSearchCV | |
| import numpy as np | |
| from src.exception import CustomException | |
| def save_object(file_path, obj): | |
| """ | |
| Save an object to a file using pickle. | |
| Parameters: | |
| file_path (str): The file path where the object should be saved. | |
| obj (object): The object to be saved. | |
| """ | |
| try: | |
| dir_path = os.path.dirname(file_path) | |
| os.makedirs(dir_path, exist_ok=True) | |
| with open(file_path, "wb") as file_obj: | |
| pickle.dump(obj, file_obj) | |
| except Exception as e: | |
| raise CustomException(e, sys) | |
| def evaluate_models(X_train, y_train, X_test, y_test, models, param): | |
| """ | |
| Evaluate multiple models using GridSearchCV and return their performance on the test set. | |
| Parameters: | |
| X_train (array-like): Training feature data. | |
| y_train (array-like): Training target data. | |
| X_test (array-like): Testing feature data. | |
| y_test (array-like): Testing target data. | |
| models (dict): Dictionary of models to evaluate. | |
| param (dict): Dictionary of parameter grids for each model. | |
| Returns: | |
| dict: A dictionary containing the evaluation metrics for each model. | |
| """ | |
| try: | |
| report = {} | |
| for model_name, model in models.items(): | |
| param_grid = param[model_name] | |
| gs = GridSearchCV(model, param_grid, cv=5) | |
| gs.fit(X_train, y_train) | |
| best_model = gs.best_estimator_ | |
| best_model.fit(X_train, y_train) | |
| y_train_pred = best_model.predict(X_train) | |
| y_test_pred = best_model.predict(X_test) | |
| train_metrics = { | |
| "R2 Score": r2_score(y_train, y_train_pred), | |
| "Mean Absolute Error": mean_absolute_error(y_train, y_train_pred), | |
| "Mean Squared Error": mean_squared_error(y_train, y_train_pred), | |
| "Root Mean Squared Error": np.sqrt(mean_squared_error(y_train, y_train_pred)) | |
| } | |
| test_metrics = { | |
| "R2 Score": r2_score(y_test, y_test_pred), | |
| "Mean Absolute Error": mean_absolute_error(y_test, y_test_pred), | |
| "Mean Squared Error": mean_squared_error(y_test, y_test_pred), | |
| "Root Mean Squared Error": np.sqrt(mean_squared_error(y_test, y_test_pred)) | |
| } | |
| if len(np.unique(y_train)) <= 2: | |
| train_metrics.update({ | |
| "Accuracy": accuracy_score(y_train, y_train_pred), | |
| "Precision": precision_score(y_train, y_train_pred, zero_division=1), | |
| "Recall": recall_score(y_train, y_train_pred, zero_division=1), | |
| "F1 Score": f1_score(y_train, y_train_pred, zero_division=1) | |
| }) | |
| test_metrics.update({ | |
| "Accuracy": accuracy_score(y_test, y_test_pred), | |
| "Precision": precision_score(y_test, y_test_pred, zero_division=1), | |
| "Recall": recall_score(y_test, y_test_pred, zero_division=1), | |
| "F1 Score": f1_score(y_test, y_test_pred, zero_division=1) | |
| }) | |
| report[model_name] = { | |
| "Train Metrics": train_metrics, | |
| "Test Metrics": test_metrics | |
| } | |
| return report | |
| except Exception as e: | |
| raise CustomException(e, sys) | |
| def load_object(file_path): | |
| """ | |
| Load an object from a file using pickle. | |
| Parameters: | |
| file_path (str): The file path from which the object should be loaded. | |
| Returns: | |
| object: The loaded object. | |
| """ | |
| try: | |
| with open(file_path, "rb") as file_obj: | |
| return pickle.load(file_obj) | |
| except Exception as e: | |
| raise CustomException(e, sys) | |