def test_lift_no_categorical_features(self):
examples = [
pa.Table.from_arrays([
pa.array([[1.0], [2.0], [3.0], [4.0]]),
pa.array([[1], [0], [1], [0]]),
], ['continous_x', 'int_y']),
]
schema = text_format.Parse(
"""
feature {
name: 'continuous_x'
type: FLOAT
}
feature {
name: 'int_y'
type: INT
int_domain {
is_categorical: true
}
}
""", schema_pb2.Schema())
expected_result = []
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema, y_path=types.FeaturePath(['int_y']))
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_lift_min_x_count_filters_all(self):
examples = [
pa.Table.from_arrays([
pa.array([['a'], ['a'], ['b'], ['a']]),
pa.array([['cat'], ['dog'], ['cat'], ['dog']]),
], ['categorical_x', 'string_y']),
]
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'string_y'
type: BYTES
}
""", schema_pb2.Schema())
expected_result = []
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema,
y_path=types.FeaturePath(['string_y']),
min_x_count=4)
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_stats_options_with_generators_to_json(self):
generators = [
lift_stats_generator.LiftStatsGenerator(
schema=None,
y_path=types.FeaturePath(['label']),
x_paths=[types.FeaturePath(['feature'])])
]
options = stats_options.StatsOptions(generators=generators)
with self.assertRaisesRegex(ValueError,
'StatsOptions cannot be converted'):
options.to_json()
def test_lift_int_y_with_no_boundaries(self):
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'int_y'
type: INT
}
""", schema_pb2.Schema())
with self.assertRaisesRegex(ValueError,
r'Boundaries must be provided with a non-'
'categorical y_path.*'):
lift_stats_generator.LiftStatsGenerator(
schema=schema, y_path=types.FeaturePath(['int_y']))
def test_lift_string_y_with_boundaries(self):
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'string_y'
type: BYTES
}
""", schema_pb2.Schema())
with self.assertRaisesRegex(
ValueError, r'Boundaries cannot be applied to a '
'categorical y_path.*'):
lift_stats_generator.LiftStatsGenerator(schema,
y_path=types.FeaturePath(
['string_y']),
y_boundaries=[1, 2, 3])
def get_generators(options: stats_options.StatsOptions,
in_memory: bool = False
) -> List[stats_generator.StatsGenerator]:
"""Initializes the list of stats generators, including custom generators.
Args:
options: A StatsOptions object.
in_memory: Whether the generators will be used to generate statistics in
memory (True) or using Beam (False).
Returns:
A list of stats generator objects.
"""
generators = _get_default_generators(options, in_memory)
if options.generators:
# Add custom stats generators.
generators.extend(options.generators)
if options.enable_semantic_domain_stats:
semantic_domain_feature_stats_generators = [
image_stats_generator.ImageStatsGenerator(),
natural_language_stats_generator.NLStatsGenerator(),
time_stats_generator.TimeStatsGenerator(),
]
# Wrap semantic domain feature stats generators as a separate combiner
# stats generator, so that we can apply sampling only for those and other
# feature stats generators are not affected by it.
generators.append(
CombinerFeatureStatsWrapperGenerator(
semantic_domain_feature_stats_generators,
weight_feature=options.weight_feature,
sample_rate=options.semantic_domain_stats_sample_rate))
if options.schema is not None:
if _schema_has_sparse_features(options.schema):
generators.append(
sparse_feature_stats_generator.SparseFeatureStatsGenerator(
options.schema))
if options.schema.weighted_feature:
generators.append(
weighted_feature_stats_generator.WeightedFeatureStatsGenerator(
options.schema))
if options.label_feature and not in_memory:
# The LiftStatsGenerator is not a CombinerStatsGenerator and therefore
# cannot currenty be used for in_memory executions.
generators.append(
lift_stats_generator.LiftStatsGenerator(
y_path=types.FeaturePath([options.label_feature]),
schema=options.schema,
weight_column_name=options.weight_feature,
output_custom_stats=True))
# Replace all CombinerFeatureStatsGenerator with a single
# CombinerFeatureStatsWrapperGenerator.
feature_generators = [
x for x in generators
if isinstance(x, stats_generator.CombinerFeatureStatsGenerator)
]
if feature_generators:
generators = [
x for x in generators
if not isinstance(x, stats_generator.CombinerFeatureStatsGenerator)
] + [
CombinerFeatureStatsWrapperGenerator(
feature_generators, weight_feature=options.weight_feature)
]
if in_memory:
for generator in generators:
if not isinstance(generator, stats_generator.CombinerStatsGenerator):
raise TypeError('Statistics generator used in '
'generate_statistics_in_memory must '
'extend CombinerStatsGenerator, found object of '
'type %s.' % generator.__class__.__name__)
return generators
def test_stats_options_json_round_trip(self):
generators = [
lift_stats_generator.LiftStatsGenerator(
schema=None,
y_path=types.FeaturePath(['label']),
x_paths=[types.FeaturePath(['feature'])])
]
feature_whitelist = ['a']
schema = schema_pb2.Schema(feature=[schema_pb2.Feature(name='f')])
label_feature = 'label'
weight_feature = 'weight'
slice_functions = [slicing_util.get_feature_value_slicer({'b': None})]
sample_rate = 0.01
num_top_values = 21
frequency_threshold = 2
weighted_frequency_threshold = 2.0
num_rank_histogram_buckets = 1001
num_values_histogram_buckets = 11
num_histogram_buckets = 11
num_quantiles_histogram_buckets = 11
epsilon = 0.02
infer_type_from_schema = True
desired_batch_size = 100
enable_semantic_domain_stats = True
semantic_domain_stats_sample_rate = 0.1
options = stats_options.StatsOptions(
generators=generators,
feature_whitelist=feature_whitelist,
schema=schema,
label_feature=label_feature,
weight_feature=weight_feature,
slice_functions=slice_functions,
sample_rate=sample_rate,
num_top_values=num_top_values,
frequency_threshold=frequency_threshold,
weighted_frequency_threshold=weighted_frequency_threshold,
num_rank_histogram_buckets=num_rank_histogram_buckets,
num_values_histogram_buckets=num_values_histogram_buckets,
num_histogram_buckets=num_histogram_buckets,
num_quantiles_histogram_buckets=num_quantiles_histogram_buckets,
epsilon=epsilon,
infer_type_from_schema=infer_type_from_schema,
desired_batch_size=desired_batch_size,
enable_semantic_domain_stats=enable_semantic_domain_stats,
semantic_domain_stats_sample_rate=semantic_domain_stats_sample_rate
)
options_json = options.to_json()
options = stats_options.StatsOptions.from_json(options_json)
self.assertIsNone(options.generators)
self.assertEqual(feature_whitelist, options.feature_whitelist)
compare.assertProtoEqual(self, schema, options.schema)
self.assertEqual(label_feature, options.label_feature)
self.assertEqual(weight_feature, options.weight_feature)
self.assertIsNone(options.slice_functions)
self.assertEqual(sample_rate, options.sample_rate)
self.assertEqual(num_top_values, options.num_top_values)
self.assertEqual(frequency_threshold, options.frequency_threshold)
self.assertEqual(weighted_frequency_threshold,
options.weighted_frequency_threshold)
self.assertEqual(num_rank_histogram_buckets,
options.num_rank_histogram_buckets)
self.assertEqual(num_values_histogram_buckets,
options.num_values_histogram_buckets)
self.assertEqual(num_histogram_buckets, options.num_histogram_buckets)
self.assertEqual(num_quantiles_histogram_buckets,
options.num_quantiles_histogram_buckets)
self.assertEqual(epsilon, options.epsilon)
self.assertEqual(infer_type_from_schema,
options.infer_type_from_schema)
self.assertEqual(desired_batch_size, options.desired_batch_size)
self.assertEqual(enable_semantic_domain_stats,
options.enable_semantic_domain_stats)
self.assertEqual(semantic_domain_stats_sample_rate,
options.semantic_domain_stats_sample_rate)
def test_lift_flattened_x_leaf(self):
examples = [
pa.Table.from_arrays([
pa.array([['a', 'a'], ['a'], ['b', 'b'], ['a', 'a']]),
pa.array([['cat'], ['dog'], ['cat'], ['dog']]),
], ['categorical_x', 'string_y']),
]
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'string_y'
type: BYTES
}
""", schema_pb2.Schema())
expected_result = [
text_format.Parse(
"""
cross_features {
path_x {
step: "categorical_x"
}
path_y {
step: "string_y"
}
categorical_cross_stats {
lift_series {
y_string: "cat"
y_count: 2
lift_values {
x_string: "b"
lift: 2.0
x_count: 1
x_and_y_count: 1
}
lift_values {
x_string: "a"
lift: 0.66666698
x_count: 3
x_and_y_count: 1
}
}
lift_series {
y_string: "dog"
y_count: 2
lift_values {
x_string: "a"
lift: 1.33333301544
x_count: 3
x_and_y_count: 2
}
lift_values {
x_string: "b"
lift: 0.0
x_count: 1
x_and_y_count: 0
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics()),
]
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema, y_path=types.FeaturePath(['string_y']))
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_lift_flattened_x(self):
examples = [
pa.Table.from_arrays([
pa.array([[{
'docs': ['a', 'b']
}, {
'docs': ['a']
}, {
'docs': ['c']
}], [{
'docs': ['a', 'b']
}]]),
pa.array([['pos'], ['neg']]),
], ['doc_set', 'string_y']),
]
schema = text_format.Parse(
"""
feature {
name: 'doc_set'
struct_domain {
feature {
name: 'docs'
type: BYTES
}
}
type: STRUCT
}
feature {
name: 'string_y'
type: BYTES
}
""", schema_pb2.Schema())
expected_result = [
text_format.Parse(
"""
cross_features {
path_x {
step: 'doc_set'
step: 'docs'
}
path_y {
step: "string_y"
}
categorical_cross_stats {
lift_series {
y_string: "neg"
y_count: 1
lift_values {
x_string: "a"
lift: 1.0
x_count: 2
x_and_y_count: 1
}
lift_values {
x_string: "b"
lift: 1.0
x_count: 2
x_and_y_count: 1
}
lift_values {
x_string: "c"
lift: 0.0
x_count: 1
x_and_y_count: 0
}
}
lift_series {
y_string: "pos"
y_count: 1
lift_values {
x_string: "c"
lift: 2.0
x_count: 1
x_and_y_count: 1
}
lift_values {
x_string: "a"
lift: 1.0
x_count: 2
x_and_y_count: 1
}
lift_values {
x_string: "b"
lift: 1.0
x_count: 2
x_and_y_count: 1
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics()),
]
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema, y_path=types.FeaturePath(['string_y']))
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_lift_y_is_none(self):
examples = [
pa.Table.from_arrays([
pa.array([['a'], ['a'], ['b'], ['a']]),
pa.array([None, [.7], [.4], [.6]]),
], ['categorical_x', 'float_y']),
]
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'float_y'
type: FLOAT
}
""", schema_pb2.Schema())
expected_result = [
text_format.Parse(
"""
cross_features {
path_x {
step: "categorical_x"
}
path_y {
step: "float_y"
}
categorical_cross_stats {
lift_series {
y_bucket {
low_value: -inf
high_value: 0.5
}
y_count: 1
lift_values {
x_string: "b"
lift: 4.0
x_count: 1
x_and_y_count: 1
}
lift_values {
x_string: "a"
lift: 0.0
x_count: 3
x_and_y_count: 0
}
}
lift_series {
y_bucket {
low_value: 0.5
high_value: inf
}
y_count: 2
lift_values {
x_string: "a"
lift: 1.33333301544
x_count: 3
x_and_y_count: 2
}
lift_values {
x_string: "b"
lift: 0.0
x_count: 1
x_and_y_count: 0
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics()),
]
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema,
y_path=types.FeaturePath(['float_y']),
y_boundaries=[0.5])
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_lift_int_y(self):
examples = [
pa.Table.from_arrays([
pa.array([[11], [11], [22], [11]]),
pa.array([[1], [0], [1], [0]]),
], ['categorical_x', 'int_y']),
]
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'int_y'
type: INT
int_domain {
is_categorical: true
}
}
""", schema_pb2.Schema())
expected_result = [
text_format.Parse(
"""
cross_features {
path_x {
step: "categorical_x"
}
path_y {
step: "int_y"
}
categorical_cross_stats {
lift_series {
y_int: 0
y_count: 2
lift_values {
x_int: 11
lift: 1.333333
x_count: 3
x_and_y_count: 2
}
lift_values {
x_int: 22
lift: 0.0
x_count: 1
x_and_y_count: 0
}
}
lift_series {
y_int: 1
y_count: 2
lift_values {
x_int: 22
lift: 2.0
x_count: 1
x_and_y_count: 1
}
lift_values {
x_int: 11
lift: 0.66666698
x_count: 3
x_and_y_count: 1
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics()),
]
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema, y_path=types.FeaturePath(['int_y']))
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_lift_slice_aware(self):
examples = [
('slice1',
pa.Table.from_arrays([
pa.array([['a'], ['a'], ['b'], ['a']]),
pa.array([['cat'], ['dog'], ['cat'], ['dog']]),
], ['categorical_x', 'string_y'])),
('slice2',
pa.Table.from_arrays([
pa.array([['a'], ['a'], ['a']]),
pa.array([['cat'], ['dog'], ['dog']]),
], ['categorical_x', 'string_y'])),
('slice1',
pa.Table.from_arrays([
pa.array([['a'], ['a'], ['b'], ['a']]),
pa.array([['cat'], ['dog'], ['cat'], ['dog']]),
], ['categorical_x', 'string_y'])),
('slice2',
pa.Table.from_arrays([
pa.array([None, None, None, None], type=pa.null()),
pa.array([['cat'], ['dog'], ['cat'], ['dog']]),
], ['categorical_x', 'string_y'])),
]
schema = text_format.Parse(
"""
feature {
name: 'categorical_x'
type: BYTES
}
feature {
name: 'string_y'
type: BYTES
}
""", schema_pb2.Schema())
expected_result = [
('slice1',
text_format.Parse(
"""
cross_features {
path_x {
step: "categorical_x"
}
path_y {
step: "string_y"
}
categorical_cross_stats {
lift_series {
y_string: "cat"
y_count: 4
lift_values {
x_string: "b"
lift: 2.0
x_count: 2
x_and_y_count: 2
}
lift_values {
x_string: "a"
lift: 0.666666984558
x_count: 6
x_and_y_count: 2
}
}
lift_series {
y_string: "dog"
y_count: 4
lift_values {
x_string: "a"
lift: 1.33333301544
x_count: 6
x_and_y_count: 4
}
lift_values {
x_string: "b"
lift: 0.0
x_count: 2
x_and_y_count: 0
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics())),
('slice2',
text_format.Parse(
"""
cross_features {
path_x {
step: "categorical_x"
}
path_y {
step: "string_y"
}
categorical_cross_stats {
lift_series {
y_string: "cat"
y_count: 3
lift_values {
x_string: "a"
lift: 0.777778029441
x_count: 3
x_and_y_count: 1
}
}
lift_series {
y_string: "dog"
y_count: 4
lift_values {
x_string: "a"
lift: 1.16666698455
x_count: 3
x_and_y_count: 2
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics())),
]
generator = lift_stats_generator.LiftStatsGenerator(
schema=schema, y_path=types.FeaturePath(['string_y']))
self.assertSlicingAwareTransformOutputEqual(examples, generator,
expected_result)
def test_lift_with_no_schema_or_x_path(self):
with self.assertRaisesRegex(ValueError,
r'Either a schema or x_paths must be provided'):
lift_stats_generator.LiftStatsGenerator(
schema=None, y_path=types.FeaturePath(['int_y']))
def test_lift_provided_x_no_schema(self):
examples = [
pa.Table.from_arrays([
pa.array([['a'], ['a'], ['b'], ['a']]),
pa.array([['x'], ['x'], ['y'], ['x']]),
pa.array([['cat'], ['dog'], ['cat'], ['dog']]),
], ['categorical_x1', 'categorical_x2', 'string_y']),
]
expected_result = [
text_format.Parse("""
cross_features {
path_x {
step: "categorical_x1"
}
path_y {
step: "string_y"
}
categorical_cross_stats {
lift {
lift_series {
y_string: "cat"
y_count: 2
lift_values {
x_string: "b"
lift: 2.0
x_count: 1
x_and_y_count: 1
}
lift_values {
x_string: "a"
lift: 0.6666667
x_count: 3
x_and_y_count: 1
}
}
lift_series {
y_string: "dog"
y_count: 2
lift_values {
x_string: "a"
lift: 1.3333333
x_count: 3
x_and_y_count: 2
}
lift_values {
x_string: "b"
lift: 0.0
x_count: 1
x_and_y_count: 0
}
}
}
}
}""", statistics_pb2.DatasetFeatureStatistics()),
]
generator = lift_stats_generator.LiftStatsGenerator(
schema=None,
y_path=types.FeaturePath(['string_y']),
x_paths=[types.FeaturePath(['categorical_x1'])])
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_stats_options_json_round_trip(self):
generators = [
lift_stats_generator.LiftStatsGenerator(
schema=None,
y_path=types.FeaturePath(['label']),
x_paths=[types.FeaturePath(['feature'])])
]
feature_allowlist = ['a']
schema = schema_pb2.Schema(feature=[schema_pb2.Feature(name='f')])
vocab_paths = {'a': '/path/to/a'}
label_feature = 'label'
weight_feature = 'weight'
slice_functions = [slicing_util.get_feature_value_slicer({'b': None})]
sample_rate = 0.01
num_top_values = 21
frequency_threshold = 2
weighted_frequency_threshold = 2.0
num_rank_histogram_buckets = 1001
num_values_histogram_buckets = 11
num_histogram_buckets = 11
num_quantiles_histogram_buckets = 11
epsilon = 0.02
infer_type_from_schema = True
desired_batch_size = 100
enable_semantic_domain_stats = True
semantic_domain_stats_sample_rate = 0.1
per_feature_weight_override = {types.FeaturePath(['a']): 'w'}
add_default_generators = True
use_sketch_based_topk_uniques = True
options = stats_options.StatsOptions(
generators=generators,
feature_allowlist=feature_allowlist,
schema=schema,
vocab_paths=vocab_paths,
label_feature=label_feature,
weight_feature=weight_feature,
experimental_slice_functions=slice_functions,
sample_rate=sample_rate,
num_top_values=num_top_values,
frequency_threshold=frequency_threshold,
weighted_frequency_threshold=weighted_frequency_threshold,
num_rank_histogram_buckets=num_rank_histogram_buckets,
num_values_histogram_buckets=num_values_histogram_buckets,
num_histogram_buckets=num_histogram_buckets,
num_quantiles_histogram_buckets=num_quantiles_histogram_buckets,
epsilon=epsilon,
infer_type_from_schema=infer_type_from_schema,
desired_batch_size=desired_batch_size,
enable_semantic_domain_stats=enable_semantic_domain_stats,
semantic_domain_stats_sample_rate=semantic_domain_stats_sample_rate,
per_feature_weight_override=per_feature_weight_override,
add_default_generators=add_default_generators,
experimental_use_sketch_based_topk_uniques=use_sketch_based_topk_uniques
)
options_json = options.to_json()
options = stats_options.StatsOptions.from_json(options_json)
self.assertIsNone(options.generators)
self.assertEqual(feature_allowlist, options.feature_allowlist)
compare.assertProtoEqual(self, schema, options.schema)
self.assertEqual(vocab_paths, options.vocab_paths)
self.assertEqual(label_feature, options.label_feature)
self.assertEqual(weight_feature, options.weight_feature)
self.assertIsNone(options.experimental_slice_functions)
self.assertEqual(sample_rate, options.sample_rate)
self.assertEqual(num_top_values, options.num_top_values)
self.assertEqual(frequency_threshold, options.frequency_threshold)
self.assertEqual(weighted_frequency_threshold,
options.weighted_frequency_threshold)
self.assertEqual(num_rank_histogram_buckets,
options.num_rank_histogram_buckets)
self.assertEqual(num_values_histogram_buckets,
options.num_values_histogram_buckets)
self.assertEqual(num_histogram_buckets, options.num_histogram_buckets)
self.assertEqual(num_quantiles_histogram_buckets,
options.num_quantiles_histogram_buckets)
self.assertEqual(epsilon, options.epsilon)
self.assertEqual(infer_type_from_schema, options.infer_type_from_schema)
self.assertEqual(desired_batch_size, options.desired_batch_size)
self.assertEqual(enable_semantic_domain_stats,
options.enable_semantic_domain_stats)
self.assertEqual(semantic_domain_stats_sample_rate,
options.semantic_domain_stats_sample_rate)
self.assertEqual(per_feature_weight_override,
options._per_feature_weight_override)
self.assertEqual(add_default_generators, options.add_default_generators)
self.assertEqual(use_sketch_based_topk_uniques,
options.experimental_use_sketch_based_topk_uniques)
