networksecurity/utils/main_utils/utils.py

from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
import yaml
from networksecurity.exception.exception import NetworkSecurityException
from networksecurity.logging.logger import logging
import os,sys
import numpy as np
import dill 
import pickle

def read_yaml_file(file_path: str) -> dict:
    """
    Reads a YAML file and returns its contents as a dictionary.
    
    Args:
        file_path (str): The path to the YAML file.
    Returns:
        dict: The contents of the YAML file.
    """
    try:
        with open(file_path, 'rb') as yaml_file:
            return yaml.safe_load(yaml_file)
    except Exception as e:
        raise NetworkSecurityException(e, sys)
    
def write_yaml_file(file_path: str, content: object, replace: bool = False) -> None:
    try:
        if replace:
            if os.path.exists(file_path):
                os.remove(file_path)
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with open(file_path, 'w') as file:
            yaml.dump(content, file)
    except Exception as e:
        raise NetworkSecurityException(e, sys)

def save_numpy_array_data(file_path: str, array: np.array):
    """
    Save numpy array data to file
    file_path : str : file path to save the numpy array
    array : np.array : numpy array data 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:
            np.save(file_obj, array)
    except Exception as e:
        raise NetworkSecurityException(e, sys)

def save_object(file_path: str, obj: object) -> None:
    try:
        logging.info("Entered the save_object method of Main Utils")
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with open(file_path, 'wb') as file_obj:
            pickle.dump(obj, file_obj)
        logging.info("Exited the save_object method of Main Utils")
    except Exception as e:
        raise NetworkSecurityException(e, sys)
    
def load_object(file_path: str) -> object:
    try:
        if not os.path.exists(file_path):
            raise Exception(f"The file: {file_path} does not exist")
        with open(file_path, 'rb') as file_obj:
            print(file_obj)
            return pickle.load(file_obj)
    except Exception as e:
        raise NetworkSecurityException(e, sys)
    
def load_numpy_array_data(file_path: str) -> np.array:
    """
    Load numpy array data from file
    file_path : str : file path to load the numpy array
    return : np.array : numpy array data loaded
    """
    try:
        with open(file_path, 'rb') as file_obj:
            return np.load(file_obj)
    except Exception as e:
        raise NetworkSecurityException(e, sys)
    
def evaluate_models(X_train, y_train, X_test, y_test, models: dict, params: dict) -> dict:
    """
    Evaluate multiple machine learning models and return their performance scores.

    Args:
        X_train: Training feature data.
        y_train: Training target data.
        X_test: Testing feature data.
        y_test: Testing target data.
        models (dict): A dictionary of model names and their corresponding model instances.
        params (dict): A dictionary of model names and their corresponding hyperparameter grids.
    Returns:
        dict: A dictionary containing model names and their corresponding performance scores.
    """
    try:
        report = {}
        for i in range(len(list(models))):
            model = list(models.values())[i]
            param = params[list(models.keys())[i]]

            gs = GridSearchCV(model, param, cv=3)
            gs.fit(X_train, y_train)

            model.set_params(**gs.best_params_)
            model.fit(X_train, y_train)
            
            y_train_pred = model.predict(X_train)
            y_test_pred = model.predict(X_test)
            train_model_score = r2_score(y_train, y_train_pred)
            test_model_score = r2_score(y_test, y_test_pred)
            report[list(models.keys())[i]] = test_model_score
        return report

    except Exception as e:
        raise NetworkSecurityException(e, sys)