def test_squared_loss_staged_predict(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
# Test whether staged decision function eventually gives
# the same prediction.
model = GradientBoostingSurvivalAnalysis(loss="squared", n_estimators=100, max_depth=3, random_state=0)
model.fit(whas500_data.x, whas500_data.y)
y_pred = model.predict(whas500_data.x)
# test if prediction for last stage equals ``predict``
for y in model.staged_predict(whas500_data.x):
assert y.shape == y_pred.shape
assert_array_equal(y_pred, y)
model.set_params(dropout_rate=0.03)
model.fit(whas500_data.x, whas500_data.y)
y_pred = model.predict(whas500_data.x)
# test if prediction for last stage equals ``predict``
for y in model.staged_predict(whas500_data.x):
assert y.shape == y_pred.shape
assert_array_equal(y_pred, y)
def test_squared_loss_staged_predict(self):
# Test whether staged decision function eventually gives
# the same prediction.
model = GradientBoostingSurvivalAnalysis(loss="squared",
n_estimators=100,
max_depth=3,
random_state=0)
model.fit(self.x, self.y)
y_pred = model.predict(self.x)
# test if prediction for last stage equals ``predict``
for y in model.staged_predict(self.x):
self.assertTupleEqual(y.shape, y_pred.shape)
assert_array_equal(y_pred, y)
model.set_params(dropout_rate=0.03)
model.fit(self.x, self.y)
y_pred = model.predict(self.x)
# test if prediction for last stage equals ``predict``
for y in model.staged_predict(self.x):
self.assertTupleEqual(y.shape, y_pred.shape)
assert_array_equal(y_pred, y)
