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import os
import sys
import numpy as np
import pandas as pd
import dill
import pickle
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from src.exception import CustomException
def eval_model(true, predicted):
r2_square = r2_score(true, predicted)
return r2_square
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:
pickle.dump(obj,file_obj)
except Exception as e:
raise CustomException(e,sys)
def evaluate_model(X,Y,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,Y)
model.set_params(**gs.best_params_)
model.fit(X,Y)
# Make predictions
y_train_pred = model.predict(X)
y_test_pred = model.predict(X_test)
# Evaluate Train and Test dataset
model_test_r2 = eval_model(Y_test, y_test_pred)
report[(list(Models.keys())[i])] = model_test_r2
return report
except Exception as e:
raise CustomException(e,sys)
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