def test_time_series_input_name_type_mismatch(): (x, _), _1 = utils.dataframe_dataframe() column_types = copy.copy(utils.COLUMN_TYPES_FROM_CSV) column_types['age_'] = column_types.pop('age') with pytest.raises(ValueError) as info: input_node = input_adapter.TimeseriesInputAdapter( lookback=2, column_types=column_types) input_node.transform(x) assert 'Column_names and column_types are mismatched.' in str(info.value)
def test_structured_data_input_name_type_mismatch(): (x, _), _1 = utils.dataframe_dataframe() column_types = copy.copy(utils.COLUMN_TYPES_FROM_CSV) column_types['age_'] = column_types.pop('age') with pytest.raises(ValueError) as info: adapter = input_adapter.StructuredDataInputAdapter( column_types=column_types) adapter.transform(x) assert 'Column_names and column_types are mismatched.' in str(info.value)
def test_time_series_input_transform(): x = utils.generate_data(shape=(32, )) input_node = input_adapter.TimeseriesInputAdapter(2) x = input_node.transform(x) for row in x.as_numpy_iterator(): assert row.ndim == 2 (x, _), _1 = utils.dataframe_dataframe() input_node = input_adapter.TimeseriesInputAdapter(lookback=2) x = input_node.fit_transform(x) assert input_node.column_names[0] == 'sex' for row in x.as_numpy_iterator(): assert row.ndim == 2
def test_structured_data_input_dataset(): (x, _), _1 = utils.dataframe_dataframe() x = tf.data.Dataset.from_tensor_slices(x.to_numpy().astype(np.unicode)) adapter = input_adapter.StructuredDataInputAdapter() x = adapter.fit_transform(x) assert isinstance(x, tf.data.Dataset)
def test_structured_data_input_transform(): (x, _), _1 = utils.dataframe_dataframe() adapter = input_adapter.StructuredDataInputAdapter() adapter.fit_transform(x) assert adapter.column_names[0] == 'sex' assert adapter.column_types == utils.COLUMN_TYPES_FROM_CSV