def get_single_slice_specs(slicing_spec: SlicingSpec,
num_classes: int = None) -> List[SingleSliceSpec]:
"""Returns slices of data according to slicing_spec."""
result = []
if slicing_spec.entire_dataset:
result.append(SingleSliceSpec())
# Create slices by class.
by_class = slicing_spec.by_class
if isinstance(by_class, bool):
if by_class:
assert num_classes, "When by_class == True, num_classes should be given."
assert 0 <= num_classes <= 1000, (
f"Too much classes for slicing by classes. "
f"Found {num_classes}.")
for c in range(num_classes):
result.append(SingleSliceSpec(SlicingFeature.CLASS, c))
elif isinstance(by_class, int):
result.append(SingleSliceSpec(SlicingFeature.CLASS, by_class))
elif isinstance(by_class, collections.Iterable):
for c in by_class:
result.append(SingleSliceSpec(SlicingFeature.CLASS, c))
# Create slices by percentiles
if slicing_spec.by_percentiles:
for percent in range(0, 100, 10):
result.append(
SingleSliceSpec(SlicingFeature.PERCENTILE, (percent, percent + 10)))
# Create slices by correctness of the classifications.
if slicing_spec.by_classification_correctness:
result.append(SingleSliceSpec(SlicingFeature.CORRECTLY_CLASSIFIED, True))
result.append(SingleSliceSpec(SlicingFeature.CORRECTLY_CLASSIFIED, False))
return result
def testStr(self, feature, value, expected_str):
self.assertEqual(str(SingleSliceSpec(feature, value)), expected_str)
def testStrEntireDataset(self):
self.assertEqual(str(SingleSliceSpec()), 'Entire dataset')
def _get_slice_spec(data: AttackInputData) -> SingleSliceSpec:
if hasattr(data, 'slice_spec'):
return data.slice_spec
return SingleSliceSpec()
def test_slice_entire_dataset(self):
entire_dataset_slice = SingleSliceSpec()
output = get_slice(self.input_data, entire_dataset_slice)
expected = self.input_data
expected.slice_spec = entire_dataset_slice
self.assertTrue(_are_all_fields_equal(output, self.input_data))
