assert all(predictor[0].nodes[0]['value'] == 0
for predictor in est._predictors)
# Still gives correct predictions thanks to the baseline prediction
assert_allclose(est.predict(X), y)
@pytest.mark.parametrize('Est, loss, X, y', [
(
HistGradientBoostingClassifier,
BinaryCrossEntropy(sample_weight=None),
X_classification,
y_classification
),
(
HistGradientBoostingRegressor,
LeastSquares(sample_weight=None),
X_regression,
y_regression
)
])
def test_custom_loss(Est, loss, X, y):
est = Est(loss=loss, max_iter=20)
est.fit(X, y)
@pytest.mark.parametrize('HistGradientBoosting, X, y', [
(HistGradientBoostingClassifier, X_classification, y_classification),
(HistGradientBoostingRegressor, X_regression, y_regression),
(HistGradientBoostingClassifier,
X_multi_classification, y_multi_classification),
])
'Est', (HistGradientBoostingClassifier, HistGradientBoostingRegressor))
def test_single_node_trees(Est):
# Make sure it's still possible to build single-node trees. In that case
# the value of the root is set to 0. That's a correct value: if the tree is
# single-node that's because min_gain_to_split is not respected right from
# the root, so we don't want the tree to have any impact on the
# predictions.
X, y = make_classification(random_state=0)
y[:] = 1 # constant target will lead to a single root node
est = Est(max_iter=20)
est.fit(X, y)
assert all(len(predictor[0].nodes) == 1 for predictor in est._predictors)
assert all(predictor[0].nodes[0]['value'] == 0
for predictor in est._predictors)
# Still gives correct predictions thanks to the baseline prediction
assert_allclose(est.predict(X), y)
@pytest.mark.parametrize(
'Est, loss, X, y',
[(HistGradientBoostingClassifier, BinaryCrossEntropy(sample_weight=None),
X_classification, y_classification),
(HistGradientBoostingRegressor, LeastSquares(sample_weight=None),
X_regression, y_regression)])
def test_custom_loss(Est, loss, X, y):
est = Est(loss=loss, max_iter=20)
est.fit(X, y)