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import os
import sys
import numpy as np
import pandas as pd
import dill
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from src.exception import CustomException
def save_object(file_path, obj):
try:
dir_path = os.path.dirname(file_path)
os.makedirs(dir_path, exist_ok=True)
with open(file_path, "wb") as file_obj:
dill.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):
try:
report={}
for i in range(len(list(models))):
model=list(models.values())[i]
para=param[list(models.keys())[i]]
gs = GridSearchCV(model,para,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 CustomException(e, sys)
def load_object(file_path):
try:
with open(file_path, "rb") as file_obj:
return dill.load(file_obj)
except Exception as e:
raise CustomException(e, sys)
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