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# Import necessary modules
import os
import sys
import pickle
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV
import numpy as np
from src.exception import CustomException
def save_object(file_path, obj):
"""
Save an object to a file using pickle.
Parameters:
file_path (str): The file path where the object should be saved.
obj (object): The object to be saved.
"""
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):
"""
Evaluate multiple models using GridSearchCV and return their performance on the test set.
Parameters:
X_train (array-like): Training feature data.
y_train (array-like): Training target data.
X_test (array-like): Testing feature data.
y_test (array-like): Testing target data.
models (dict): Dictionary of models to evaluate.
param (dict): Dictionary of parameter grids for each model.
Returns:
dict: A dictionary containing the evaluation metrics for each model.
"""
try:
report = {}
for model_name, model in models.items():
param_grid = param[model_name]
gs = GridSearchCV(model, param_grid, cv=5)
gs.fit(X_train, y_train)
best_model = gs.best_estimator_
best_model.fit(X_train, y_train)
y_train_pred = best_model.predict(X_train)
y_test_pred = best_model.predict(X_test)
train_metrics = {
"R2 Score": r2_score(y_train, y_train_pred),
"Mean Absolute Error": mean_absolute_error(y_train, y_train_pred),
"Mean Squared Error": mean_squared_error(y_train, y_train_pred),
"Root Mean Squared Error": np.sqrt(mean_squared_error(y_train, y_train_pred))
}
test_metrics = {
"R2 Score": r2_score(y_test, y_test_pred),
"Mean Absolute Error": mean_absolute_error(y_test, y_test_pred),
"Mean Squared Error": mean_squared_error(y_test, y_test_pred),
"Root Mean Squared Error": np.sqrt(mean_squared_error(y_test, y_test_pred))
}
if len(np.unique(y_train)) <= 2:
train_metrics.update({
"Accuracy": accuracy_score(y_train, y_train_pred),
"Precision": precision_score(y_train, y_train_pred, zero_division=1),
"Recall": recall_score(y_train, y_train_pred, zero_division=1),
"F1 Score": f1_score(y_train, y_train_pred, zero_division=1)
})
test_metrics.update({
"Accuracy": accuracy_score(y_test, y_test_pred),
"Precision": precision_score(y_test, y_test_pred, zero_division=1),
"Recall": recall_score(y_test, y_test_pred, zero_division=1),
"F1 Score": f1_score(y_test, y_test_pred, zero_division=1)
})
report[model_name] = {
"Train Metrics": train_metrics,
"Test Metrics": test_metrics
}
return report
except Exception as e:
raise CustomException(e, sys)
def load_object(file_path):
"""
Load an object from a file using pickle.
Parameters:
file_path (str): The file path from which the object should be loaded.
Returns:
object: The loaded object.
"""
try:
with open(file_path, "rb") as file_obj:
return pickle.load(file_obj)
except Exception as e:
raise CustomException(e, sys)
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