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