def assertSliceResult(self, name, features_dict, columns, features, expected):
spec = slicer.SingleSliceSpec(columns=columns, features=features)
msg = 'Test case %s: slice on columns %s, features %s' % (name, columns,
features)
six.assertCountEqual(
self, expected,
slicer.get_slices_for_features_dict(features_dict, [spec]), msg)
def process(self, element: types.Extracts) -> List[types.Extracts]:
fpl = element.get(constants.FEATURES_PREDICTIONS_LABELS_KEY)
if not fpl:
raise RuntimeError(
'FPL missing, Please ensure Predict() was called.')
if not isinstance(fpl, types.FeaturesPredictionsLabels):
raise TypeError(
'Expected FPL to be instance of FeaturesPredictionsLabel. FPL was: '
'%s of type %s' % (str(fpl), type(fpl)))
features = fpl.features
slices = list(
slicer.get_slices_for_features_dict(features, self._slice_spec))
# Make a a shallow copy, so we don't mutate the original.
element_copy = copy.copy(element)
element_copy[constants.SLICE_KEY_TYPES_KEY] = slices
# Add a list of stringified slice keys to be materialized to output table.
if self._materialize:
element_copy[constants.SLICE_KEYS_KEY] = types.MaterializedColumn(
name=constants.SLICE_KEYS_KEY,
value=(list(
slicer.stringify_slice_key(x).encode('utf-8')
for x in slices)))
return [element_copy]
def process(self, element):
features = element.features
slice_count = 0
for slice_key in slicer.get_slices_for_features_dict(
features, self._slice_spec):
slice_count += 1
yield (slice_key, element)
self._num_slices_generated_per_instance.update(slice_count)
self._post_slice_num_instances.inc(slice_count)
def process(self, element):
fpl = element.extracts[constants.FEATURES_PREDICTIONS_LABELS_KEY]
features = fpl.features
slice_count = 0
for slice_key in slicer.get_slices_for_features_dict(
features, self._slice_spec):
slice_count += 1
yield (slice_key, fpl)
self._num_slices_generated_per_instance.update(slice_count)
self._post_slice_num_instances.inc(slice_count)
def testGetSlicesForFeaturesDictMultipleSingleSliceSpecs(self):
features_dict = self._makeFeaturesDict({
'gender': ['f'],
'age': [5],
'interest': ['cars']
})
spec_overall = slicer.SingleSliceSpec()
spec_age = slicer.SingleSliceSpec(columns=['age'])
spec_age4 = slicer.SingleSliceSpec(features=[('age', 4)])
spec_age5_gender = slicer.SingleSliceSpec(
columns=['gender'], features=[('age', 5)])
slice_spec = [spec_overall, spec_age, spec_age4, spec_age5_gender]
expected = [(), (('age', 5),), (('age', 5), ('gender', 'f'))]
self.assertItemsEqual(
expected, slicer.get_slices_for_features_dict(features_dict,
slice_spec))
def process(self, element):
fpl = element.extracts.get(constants.FEATURES_PREDICTIONS_LABELS_KEY)
if not fpl:
raise RuntimeError(
'FPL missing, Please ensure Predict() was called.')
if not isinstance(fpl, load.FeaturesPredictionsLabels):
raise TypeError(
'Expected FPL to be instance of FeaturesPredictionsLabel. FPL was: '
'%s of type %s' % (str(fpl), type(fpl)))
features = fpl.features
slices = list(
slicer.get_slices_for_features_dict(features, self._slice_spec))
# Make a a shallow copy, so we don't mutate the original.
element_copy = (element.create_copy_with_shallow_copy_of_extracts())
element_copy.extracts[constants.SLICE_KEYS] = slices
# Add a list of stringified slice keys to be materialized to output table.
element_copy.extracts[
constants.SLICE_KEYS_MATERIALIZED] = types.MaterializedColumn(
name=constants.SLICE_KEYS_MATERIALIZED,
value=(list(slicer.stringify_slice_key(x) for x in slices)))
return [element_copy]
