| | 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 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_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 pickle.load(file_obj)
|
| |
|
| | except Exception as e:
|
| | raise CustomException(e, sys) |
