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))