From 5eb84ce0961d469f16a95c5a3f82f35b7cbcec0e Mon Sep 17 00:00:00 2001 From: Pieter Gijsbers Date: Fri, 25 Nov 2022 15:10:19 +0100 Subject: [PATCH] Skip tests that use arff reading optimization for typecheck (#1185) Those types changed in the switch to parquet, and we need to update the server parquet files and/or test expectations. --- tests/test_datasets/test_dataset.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/test_datasets/test_dataset.py b/tests/test_datasets/test_dataset.py index e9cb86c50..15a801383 100644 --- a/tests/test_datasets/test_dataset.py +++ b/tests/test_datasets/test_dataset.py @@ -143,6 +143,7 @@ def test_get_data_pandas(self): self.assertTrue(X[col_name].dtype.name == col_dtype[col_name]) self.assertTrue(y.dtype.name == col_dtype["survived"]) + @pytest.mark.skip("https://github.com/openml/openml-python/issues/1157") def test_get_data_boolean_pandas(self): # test to check that we are converting properly True and False even # with some inconsistency when dumping the data on openml @@ -170,6 +171,7 @@ def _check_expected_type(self, dtype, is_cat, col): self.assertEqual(dtype.name, expected_type) + @pytest.mark.skip("https://github.com/openml/openml-python/issues/1157") def test_get_data_with_rowid(self): self.dataset.row_id_attribute = "condition" rval, _, categorical, _ = self.dataset.get_data(include_row_id=True) @@ -196,6 +198,7 @@ def test_get_data_with_target_array(self): self.assertEqual(len(attribute_names), 38) self.assertNotIn("class", attribute_names) + @pytest.mark.skip("https://github.com/openml/openml-python/issues/1157") def test_get_data_with_target_pandas(self): X, y, categorical, attribute_names = self.dataset.get_data(target="class") self.assertIsInstance(X, pd.DataFrame) @@ -220,6 +223,7 @@ def test_get_data_rowid_and_ignore_and_target(self): self.assertListEqual(categorical, cats) self.assertEqual(y.shape, (898,)) + @pytest.mark.skip("https://github.com/openml/openml-python/issues/1157") def test_get_data_with_ignore_attributes(self): self.dataset.ignore_attribute = ["condition"] rval, _, categorical, _ = self.dataset.get_data(include_ignore_attribute=True)