| @@ -1,98 +0,0 @@ | |||
| import unittest | |||
| from d3m import container, utils | |||
| from d3m.metadata import base as metadata_base | |||
| from tods.timeseries_processing import SubsequenceClustering | |||
| class SubsequenceClusteringTest(unittest.TestCase): | |||
| def test_basic(self): | |||
| self.maxDiff = None | |||
| main = container.DataFrame({'a': [1., 2., 3.], 'b': [2., 3., 4.], 'c': [3., 4., 5.],}, | |||
| # columns=['a', 'b', 'c'], | |||
| generate_metadata=True) | |||
| print(main) | |||
| self.assertEqual(utils.to_json_structure(main.metadata.to_internal_simple_structure()), [{ | |||
| 'selector': [], | |||
| 'metadata': { | |||
| # 'top_level': 'main', | |||
| 'schema': metadata_base.CONTAINER_SCHEMA_VERSION, | |||
| 'structural_type': 'd3m.container.pandas.DataFrame', | |||
| 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Table'], | |||
| 'dimension': { | |||
| 'name': 'rows', | |||
| 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularRow'], | |||
| 'length': 3, | |||
| }, | |||
| }, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__'], | |||
| 'metadata': { | |||
| 'dimension': { | |||
| 'name': 'columns', | |||
| 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularColumn'], | |||
| 'length': 3, | |||
| }, | |||
| }, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__', 0], | |||
| 'metadata': {'structural_type': 'numpy.float64', 'name': 'a'}, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__', 1], | |||
| 'metadata': {'structural_type': 'numpy.float64', 'name': 'b'}, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__', 2], | |||
| 'metadata': {'structural_type': 'numpy.float64', 'name': 'c'} | |||
| }]) | |||
| self.assertIsInstance(main, container.DataFrame) | |||
| hyperparams_class = SubsequenceClustering.SubsequenceClustering.metadata.get_hyperparams() | |||
| primitive = SubsequenceClustering.SubsequenceClustering(hyperparams=hyperparams_class.defaults()) | |||
| new_main = primitive.produce(inputs=main).value | |||
| print(new_main) | |||
| print(new_main.shape) | |||
| self.assertEqual(utils.to_json_structure(main.metadata.to_internal_simple_structure()), [{ | |||
| 'selector': [], | |||
| 'metadata': { | |||
| # 'top_level': 'main', | |||
| 'schema': metadata_base.CONTAINER_SCHEMA_VERSION, | |||
| 'structural_type': 'd3m.container.pandas.DataFrame', | |||
| 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Table'], | |||
| 'dimension': { | |||
| 'name': 'rows', | |||
| 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularRow'], | |||
| 'length': 3, | |||
| }, | |||
| }, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__'], | |||
| 'metadata': { | |||
| 'dimension': { | |||
| 'name': 'columns', | |||
| 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/TabularColumn'], | |||
| 'length': 3, | |||
| }, | |||
| }, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__', 0], | |||
| 'metadata': {'structural_type': 'numpy.float64', 'name': 'a'}, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__', 1], | |||
| 'metadata': {'structural_type': 'numpy.float64', 'name': 'b'}, | |||
| }, { | |||
| 'selector': ['__ALL_ELEMENTS__', 2], | |||
| 'metadata': {'structural_type': 'numpy.float64', 'name': 'c'} | |||
| }]) | |||
| if __name__ == '__main__': | |||
| unittest.main() | |||