model.fit(train_loader)
toc = time.time()
training_time = toc - tic
tic = time.time()
testing_mse = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic
records.append(
('BaggingRegressor', training_time, evaluating_time, testing_mse))
# GradientBoostingRegressor
model = GradientBoostingRegressor(estimator=MLP,
n_estimators=n_estimators,
output_dim=output_dim,
lr=lr,
weight_decay=weight_decay,
epochs=epochs)
tic = time.time()
model.fit(train_loader)
toc = time.time()
training_time = toc - tic
tic = time.time()
testing_mse = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic
records.append(('GradientBoostingRegressor', training_time,
evaluating_time, testing_mse))
tic = time.time()
model.fit(train_loader, epochs=epochs)
toc = time.time()
training_time = toc - tic
tic = time.time()
testing_mse = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic
records.append(
("BaggingRegressor", training_time, evaluating_time, testing_mse))
# GradientBoostingRegressor
model = GradientBoostingRegressor(estimator=MLP,
n_estimators=n_estimators,
cuda=True)
# Set the optimizer
model.set_optimizer("Adam", lr=lr, weight_decay=weight_decay)
tic = time.time()
model.fit(train_loader, epochs=epochs)
toc = time.time()
training_time = toc - tic
tic = time.time()
testing_mse = model.predict(test_loader)
toc = time.time()
evaluating_time = toc - tic