def test_fit_int_param_as_float(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
if _sklearn_version_under_0p21:
max_depth = 3
else:
# Account for https://github.com/scikit-learn/scikit-learn/pull/12344
max_depth = 4
model = GradientBoostingSurvivalAnalysis(n_estimators=100.0,
max_depth=float(max_depth),
min_samples_split=10.0,
random_state=0)
params = model.get_params()
assert 100 == params["n_estimators"]
assert max_depth == params["max_depth"]
assert 10 == params["min_samples_split"]
model.set_params(max_leaf_nodes=15.0)
assert 15 == model.get_params()["max_leaf_nodes"]
model.fit(whas500_data.x, whas500_data.y)
p = model.predict(whas500_data.x)
assert_cindex_almost_equal(whas500_data.y['fstat'],
whas500_data.y['lenfol'], p,
(0.90256690042449006, 67826, 7321, 2, 14))
def test_fit_int_param_as_float(self):
model = GradientBoostingSurvivalAnalysis(n_estimators=100.0, max_depth=3.0, min_samples_split=10.0,
random_state=0)
params = model.get_params()
self.assertEqual(100, params["n_estimators"])
self.assertEqual(3, params["max_depth"])
self.assertEqual(10, params["min_samples_split"])
model.set_params(max_leaf_nodes=15.0)
self.assertEqual(15, model.get_params()["max_leaf_nodes"])
model.fit(self.x, self.y)
p = model.predict(self.x)
expected_cindex = numpy.array([0.90256690042449006, 67826, 7321, 2, 119])
result = concordance_index_censored(self.y['fstat'], self.y['lenfol'], p)
assert_array_almost_equal(expected_cindex, numpy.array(result))
def test_fit_int_param_as_float(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = GradientBoostingSurvivalAnalysis(n_estimators=100.0, max_depth=3.0, min_samples_split=10.0,
random_state=0)
params = model.get_params()
assert 100 == params["n_estimators"]
assert 3 == params["max_depth"]
assert 10 == params["min_samples_split"]
model.set_params(max_leaf_nodes=15.0)
assert 15 == model.get_params()["max_leaf_nodes"]
model.fit(whas500_data.x, whas500_data.y)
p = model.predict(whas500_data.x)
assert_cindex_almost_equal(whas500_data.y['fstat'], whas500_data.y['lenfol'], p,
(0.90256690042449006, 67826, 7321, 2, 119))
