LGBMRegressor on California Housing dataset is 0.68 >> 0.46

[fingertap]

I use the sample code to prepare the dataset:

device = 'cpu'
dataset = sklearn.datasets.fetch_california_housing()
task_type = 'regression'

X_all = dataset['data'].astype('float32')
y_all = dataset['target'].astype('float32')
n_classes = None

X = {}
y = {}
X['train'], X['test'], y['train'], y['test'] = sklearn.model_selection.train_test_split(
    X_all, y_all, train_size=0.8
)
X['train'], X['val'], y['train'], y['val'] = sklearn.model_selection.train_test_split(
    X['train'], y['train'], train_size=0.8
)

# not the best way to preprocess features, but enough for the demonstration
preprocess = sklearn.preprocessing.StandardScaler().fit(X['train'])
X = {
    k: torch.tensor(preprocess.fit_transform(v), device=device)
    for k, v in X.items()
}
y = {k: torch.tensor(v, device=device) for k, v in y.items()}

# !!! CRUCIAL for neural networks when solving regression problems !!!
y_mean = y['train'].mean().item()
y_std = y['train'].std().item()
y = {k: (v - y_mean) / y_std for k, v in y.items()}

y = {k: v.float() for k, v in y.items()}

And I train a LGBMRegressor with the default hyper parameters:

model = lgb.LGBMRegressor()
model.fit(X['train'], y['train'])

But when I evaluate on the test fold, I found the performance is 0.68:

>>> test_pred = model.predict(X['test'])
>>> test_pred = torch.from_numpy(test_pred)
>>> rmse = torch.nn.functional.mse_loss(
>>>     test_pred.view(-1), y['test'].view(-1)) ** 0.5 * y_std
>>> print(f'Test RMSE: {rmse:.2f}.')
Test RMSE: 0.68.

Even using the model from rtdl gives me 0.56 RMSE:

(epoch) 57 (batch) 0 (loss) 0.1885
(epoch) 57 (batch) 10 (loss) 0.1315
(epoch) 57 (batch) 20 (loss) 0.1735
(epoch) 57 (batch) 30 (loss) 0.1197
(epoch) 57 (batch) 40 (loss) 0.1952
(epoch) 57 (batch) 50 (loss) 0.1167
Epoch 057 | Validation score: 0.7334 | Test score: 0.5612 <<< BEST VALIDATION EPOCH

Is there anything I miss? How can I reproduce the performance in your paper? Thanks!

[fingertap]

Problem solved. There is a bug in the example code. Change

k: torch.tensor(preprocess.fit_transform(v), device=device)

into

k: torch.tensor(preprocess.transform(v), device=device)

[Yura52]

@fingertap thank you for reporting this!