def test_list_cross_features(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
self.assertCountEqual(
view.list_cross_features(),
[(types.FeaturePath(['f1x']), types.FeaturePath(['f1y'])),
(types.FeaturePath(['f2x']), types.FeaturePath(['f2y']))])
def test_get_feature_by_path(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
feature1 = view.get_feature(types.FeaturePath(['f0_step1',
'f0_step2']))
self.assertEqual(self._stats_proto.datasets[2].features[0],
feature1.proto())
def test_get_cross_feature(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
cross_feature = view.get_cross_feature(types.FeaturePath(['f1x']),
types.FeaturePath(['f1y']))
self.assertEqual(self._stats_proto.datasets[2].cross_features[0],
cross_feature.proto())
def test_list_features(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
self.assertCountEqual(view.list_features(), [
types.FeaturePath(['f0_step1', 'f0_step2']),
types.FeaturePath(['f1']),
types.FeaturePath(['f3_derived'])
])
def test_get_derived_feature(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
feature1 = view.get_derived_feature('my_deriver_name', [
types.FeaturePath(['f0_step1', 'f0_step2']),
types.FeaturePath(['f1'])
])
self.assertEqual(self._stats_proto.datasets[2].features[2],
feature1.proto())
def test_get_derived_feature_ambiguous(self):
stats_proto = statistics_pb2.DatasetFeatureStatisticsList.FromString(
self._stats_proto.SerializeToString())
# Duplicate the derived feature.
stats_proto.datasets[2].features.append(
stats_proto.datasets[2].features[2])
view = stats_util.DatasetListView(stats_proto).get_default_slice()
with self.assertRaisesRegex(ValueError,
'Ambiguous result, 2 features matched'):
view.get_derived_feature('my_deriver_name', [
types.FeaturePath(['f0_step1', 'f0_step2']),
types.FeaturePath(['f1'])
])
def test_get_derived_feature_missing(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
self.assertIsNone(
view.get_derived_feature('mismatched_name', [
types.FeaturePath(['f0_step1', 'f0_step2']),
types.FeaturePath(['f1'])
]))
self.assertIsNone(
view.get_derived_feature('my_deriver_name', [
types.FeaturePath(['f0_step1', 'f0_step2', 'mismatched_step']),
types.FeaturePath(['f1'])
]))
self.assertIsNone(view.get_derived_feature('my_deriver_name', []))
def test_get_feature_defined_by_name(self):
stats = statistics_pb2.DatasetFeatureStatisticsList()
text_format.Parse(
"""
datasets: {
name: 'All Examples'
features: {
name: "f0"
}
features: {
name: "f1"
}
}
""", stats)
view = stats_util.DatasetListView(stats).get_default_slice()
self.assertEqual(stats.datasets[0].features[1],
view.get_feature(types.FeaturePath(['f1'])).proto())
def test_mixed_path_and_name_is_an_error(self):
stats = statistics_pb2.DatasetFeatureStatisticsList()
text_format.Parse(
"""
datasets: {
name: 'All Examples'
features: {
path: {
step: "f0_step1"
step: "f0_step2"
}
}
features: {
name: "f1"
}
}
""", stats)
view = stats_util.DatasetListView(stats).get_default_slice()
with self.assertRaisesRegex(ValueError,
('Features must be specified with '
'either path or name within a Dataset')):
view.get_feature(types.FeaturePath('f1'))
def _flatten_statistics_for_sliced_validation(
statistics: statistics_pb2.DatasetFeatureStatisticsList
) -> Tuple[statistics_pb2.DatasetFeatureStatisticsList, Set[str]]:
"""Flattens sliced stats into unsliced stats with prepended slice keys."""
result = statistics_pb2.DatasetFeatureStatisticsList()
dataset_flat = result.datasets.add()
# Copy top level metadata from the default (overall) slice.
default_slice = stats_util.DatasetListView(statistics).get_default_slice()
if default_slice is None:
raise ValueError('Missing default slice')
dataset_flat.CopyFrom(default_slice.proto())
dataset_flat.ClearField('features')
dataset_flat.ClearField('cross_features')
slice_names = set()
for dataset in statistics.datasets:
slice_names.add(dataset.name)
for feature in dataset.features:
copied_feature = dataset_flat.features.add()
copied_feature.CopyFrom(feature)
copied_feature.path.CopyFrom(
_prepend_slice_path(
dataset.name,
types.FeaturePath.from_proto(
copied_feature.path)).to_proto())
for cross_feature in dataset.cross_features:
copied_cross_feature = dataset_flat.cross_features.add()
copied_cross_feature.CopyFrom(cross_feature)
copied_cross_feature.path_x.CopyFrom(
_prepend_slice_path(
dataset.name,
types.FeaturePath.from_proto(
copied_cross_feature.path_x)).to_proto())
copied_cross_feature.path_y.CopyFrom(
_prepend_slice_path(
dataset.name,
types.FeaturePath.from_proto(
copied_cross_feature.path_y)).to_proto())
return result, slice_names
def test_get_missing_feature(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
self.assertIsNone(
view.get_feature(types.FeaturePath(['not', 'a', 'path'])))
def test_get_feature_by_name(self):
view = stats_util.DatasetListView(
self._stats_proto).get_default_slice()
feature1 = view.get_feature('f1')
self.assertEqual(self._stats_proto.datasets[2].features[1],
feature1.proto())
def test_get_missing_slice(self):
view = stats_util.DatasetListView(self._stats_proto)
slice99 = view.get_slice('slice99')
self.assertIsNone(slice99)
def test_get_default(self):
view = stats_util.DatasetListView(self._stats_proto)
default_slice = view.get_default_slice()
self.assertEqual(self._stats_proto.datasets[2], default_slice.proto())
def test_get_slice1(self):
view = stats_util.DatasetListView(self._stats_proto)
slice1 = view.get_slice('slice1')
self.assertEqual(self._stats_proto.datasets[1], slice1.proto())
def test_list_slices(self):
view = stats_util.DatasetListView(self._stats_proto)
self.assertCountEqual(['slice0', 'slice1', 'All Examples'],
view.list_slices())