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a21e473 | 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 | import yaml
import pandas as pd
from src.exception.exception import NetworkSecurityException
from src.logging.logger import logging
import os, sys
import numpy as np
import pickle
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score
def read_yaml_file(file_path:str) -> dict:
try:
with open(file_path, "rb") as yaml_file:
return yaml.safe_load(yaml_file)
except Exception as e:
raise NetworkSecurityException(e,sys) from e
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_np_array(file_path:str, array:np.array):
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) from e
def save_object(file_path:str, obj:object) -> None:
try:
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(file_path, "wb") as file_obj:
pickle.dump(obj, file_obj)
except Exception as e:
raise NetworkSecurityException(e, sys) from e
def load_object(file_path:str)-> object:
try:
if not os.path.exists(file_path):
raise Exception(f"The file: {file_path} is not exists")
with open(file_path, "rb") as file_obj:
print(file_obj)
return pickle.load(file_obj)
except Exception as e:
raise NetworkSecurityException(e, sys) from e
def load_numpy_array_data(file_path:str) -> np.array:
try:
with open(file_path, "rb") as file_obj:
return np.load(file_obj)
except Exception as e:
raise NetworkSecurityException(e, sys) from e
def evaluate_models(x_train, y_train, x_test, y_test, models, params):
try:
report = {}
for i in range(len(list(models))):
model = list(models.values())[i]
param = params[list(models.keys())[i]]
grid = GridSearchCV(model, param, cv=3)
grid.fit(x_train, y_train)
model.set_params(**grid.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) from e
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