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1611644 401037a 1611644 d3b6432 1611644 401037a d3b6432 401037a d3b6432 401037a d3b6432 401037a 4851831 fd03cdf 4851831 1611644 | 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 | import os
import sys
import numpy as np
import pandas as pd
import dill
from sklearn.metrics import r2_score
from src.exception import CustomException
from sklearn.model_selection import GridSearchCV
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)
# 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:
abs_path = os.path.abspath(file_path)
print(f"🔍 Trying to load object from: {abs_path}")
if not os.path.exists(abs_path):
raise FileNotFoundError(f"File not found: {abs_path}")
with open(abs_path, "rb") as file_obj:
return dill.load(file_obj)
except Exception as e:
raise CustomException(e, sys) |