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d3b5bd8 f228efb eae2854 d3b5bd8 f228efb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | 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) |