def test_transform_schema_Concat_irisDf(self): with EnableSchemaValidation(): from lale.datasets.data_schemas import to_schema data_X, data_y = self._irisDf["X"], self._irisDf["y"] s_in_X, s_in_y = to_schema(data_X), to_schema(data_y) def check(s_actual, n_expected, s_expected): assert s_actual["items"]["minItems"] == n_expected, str( s_actual) assert s_actual["items"]["maxItems"] == n_expected, str( s_actual) assert s_actual["items"]["items"] == s_expected, str(s_actual) s_out_X = ConcatFeatures.transform_schema({"items": [s_in_X]}) check(s_out_X, 4, {"type": "number"}) s_out_y = ConcatFeatures.transform_schema({"items": [s_in_y]}) check(s_out_y, 1, {"description": "target", "type": "integer"}) s_out_XX = ConcatFeatures.transform_schema( {"items": [s_in_X, s_in_X]}) check(s_out_XX, 8, {"type": "number"}) s_out_yy = ConcatFeatures.transform_schema( {"items": [s_in_y, s_in_y]}) check(s_out_yy, 2, {"type": "integer"}) s_out_Xy = ConcatFeatures.transform_schema( {"items": [s_in_X, s_in_y]}) check(s_out_Xy, 5, {"type": "number"}) s_out_XXX = ConcatFeatures.transform_schema( {"items": [s_in_X, s_in_X, s_in_X]}) check(s_out_XXX, 12, {"type": "number"})
def test_transform_schema_Concat_irisArr(self): from lale.datasets.data_schemas import to_schema existing_flag = disable_data_schema_validation set_disable_data_schema_validation(False) data_X, data_y = self._irisArr["X"], self._irisArr["y"] s_in_X, s_in_y = to_schema(data_X), to_schema(data_y) def check(s_actual, n_expected, s_expected): assert s_actual["items"]["minItems"] == n_expected, str(s_actual) assert s_actual["items"]["maxItems"] == n_expected, str(s_actual) assert s_actual["items"]["items"] == s_expected, str(s_actual) s_out_X = ConcatFeatures.transform_schema({"items": [s_in_X]}) check(s_out_X, 4, {"type": "number"}) s_out_y = ConcatFeatures.transform_schema({"items": [s_in_y]}) check(s_out_y, 1, {"type": "integer"}) s_out_XX = ConcatFeatures.transform_schema({"items": [s_in_X, s_in_X]}) check(s_out_XX, 8, {"type": "number"}) s_out_yy = ConcatFeatures.transform_schema({"items": [s_in_y, s_in_y]}) check(s_out_yy, 2, {"type": "integer"}) s_out_Xy = ConcatFeatures.transform_schema({"items": [s_in_X, s_in_y]}) check(s_out_Xy, 5, {"type": "number"}) s_out_XXX = ConcatFeatures.transform_schema( {"items": [s_in_X, s_in_X, s_in_X]}) check(s_out_XXX, 12, {"type": "number"}) set_disable_data_schema_validation(existing_flag)
def test_transform_schema_Concat_irisDf(self): from lale.datasets.data_schemas import to_schema data_X, data_y = self._irisDf['X'], self._irisDf['y'] s_in_X, s_in_y = to_schema(data_X), to_schema(data_y) def check(s_actual, n_expected, s_expected): assert s_actual['items']['minItems'] == n_expected, str(s_actual) assert s_actual['items']['maxItems'] == n_expected, str(s_actual) assert s_actual['items']['items'] == s_expected, str(s_actual) s_out_X = ConcatFeatures.transform_schema({'items': [s_in_X]}) check(s_out_X, 4, {'type': 'number'}) s_out_y = ConcatFeatures.transform_schema({'items': [s_in_y]}) check(s_out_y, 1, {'description': 'target', 'type': 'integer'}) s_out_XX = ConcatFeatures.transform_schema({'items': [s_in_X, s_in_X]}) check(s_out_XX, 8, {'type': 'number'}) s_out_yy = ConcatFeatures.transform_schema({'items': [s_in_y, s_in_y]}) check(s_out_yy, 2, {'type': 'integer'}) s_out_Xy = ConcatFeatures.transform_schema({'items': [s_in_X, s_in_y]}) check(s_out_Xy, 5, {'type': 'number'}) s_out_XXX = ConcatFeatures.transform_schema({ 'items': [s_in_X, s_in_X, s_in_X]}) check(s_out_XXX, 12, {'type': 'number'})