def test_all_string_features(self):
# fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e'
# fb: 1 'a', 2 'b', 3 'c'
batches = [{'fa': np.array([np.array(['a', 'b', 'c', 'e']), None,
np.array(['a', 'c', 'd'])], dtype=np.object),
'fb': np.array([np.array(['a', 'c', 'c']), np.array(['b']),
None], dtype=np.object)},
{'fa': np.array([np.array(['a', 'a', 'b', 'c', 'd']), None],
dtype=np.object),
'fb': np.array([None, np.array(['b', 'c'])],
dtype=np.object)}]
expected_result_fa = text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 5
}
}""", statistics_pb2.DatasetFeatureStatistics())
expected_result_fb = text_format.Parse(
"""
features {
name: 'fb'
type: STRING
string_stats {
unique: 3
}
}""", statistics_pb2.DatasetFeatureStatistics())
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertTransformOutputEqual(batches, generator,
[expected_result_fa, expected_result_fb])
def test_with_categorical_feature(self):
batches = [{'fa': np.array([np.array([12, 23, 34, 12]),
np.array([45, 23])])},
{'fa': np.array([np.array([12, 12, 34, 45])])}]
expected_result_fa = text_format.Parse(
"""
features {
name: 'fa'
type: INT
string_stats {
unique: 4
}
}""", statistics_pb2.DatasetFeatureStatistics())
schema = text_format.Parse(
"""
feature {
name: "fa"
type: INT
int_domain {
is_categorical: true
}
}
""", schema_pb2.Schema())
generator = uniques_stats_generator.UniquesStatsGenerator(schema=schema)
self.assertTransformOutputEqual(batches, generator, [expected_result_fa])
def test_one_numeric_feature(self):
# fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e'
examples = [{
'fa': np.array(['a', 'b', 'c', 'e']),
'fb': np.array([1.0, 2.0, 3.0])
}, {
'fa': None,
'fb': np.array([4.0, 5.0])
}, {
'fa': np.array(['a', 'c', 'd']),
'fb': None
}, {
'fa': np.array(['a', 'a', 'b', 'c', 'd']),
'fb': None
}]
expected_result_fa = text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 5
}
}""", statistics_pb2.DatasetFeatureStatistics())
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertTransformOutputEqual(examples, generator,
[expected_result_fa])
def test_generate_stats_impl(self):
# input with two batches: first batch has two examples and second batch
# has a single example.
batches = [{'a': np.array([np.array(['xyz']), np.array(['qwe'])])},
{'a': np.array([np.array(['ab'])])}]
generator1 = string_stats_generator.StringStatsGenerator()
generator2 = uniques_stats_generator.UniquesStatsGenerator()
expected_result = text_format.Parse(
"""
datasets {
features {
name: 'a'
type: STRING
string_stats {
avg_length: 2.66666666
unique: 3
}
}
}
""", statistics_pb2.DatasetFeatureStatisticsList())
with beam.Pipeline() as p:
result = (p | beam.Create(batches) |
stats_impl.GenerateStatisticsImpl(
generators=[generator1, generator2]))
util.assert_that(
result,
test_util.make_dataset_feature_stats_list_proto_equal_fn(
self, expected_result))
def test_single_unicode_feature(self):
# fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e'
examples = [{
'fa': np.array(['a', 'b', 'c', 'e'], dtype=np.unicode_)
}, {
'fa': np.array(['a', 'c', 'd', 'a'], dtype=np.unicode_)
}, {
'fa': np.array(['a', 'b', 'c', 'd'], dtype=np.unicode_)
}]
expected_result = [
text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 5
}
}""", statistics_pb2.DatasetFeatureStatistics())
]
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_unique_stats_with_slicing(self):
examples = [('slice1', {
'fa': np.array(['a', 'b', 'a', 'e']),
'fb': np.array(['1', '1', '0'])
}),
('slice2', {
'fa': np.array(['a', 'a', 'a']),
'fb': np.array(['0', '1', '2', '3', '0'])
}), ('slice1', {
'fa': None,
'fb': np.array(['2', '0'])
}), ('slice2', {
'fa': np.array(['b', 'a']),
'fb': None
})]
expected_result = [('slice1',
text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 3
}
}""", statistics_pb2.DatasetFeatureStatistics())),
('slice1',
text_format.Parse(
"""
features {
name: 'fb'
type: STRING
string_stats {
unique: 3
}
}""", statistics_pb2.DatasetFeatureStatistics())),
('slice2',
text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 2
}
}""", statistics_pb2.DatasetFeatureStatistics())),
('slice2',
text_format.Parse(
"""
features {
name: 'fb'
type: STRING
string_stats {
unique: 4
}
}""", statistics_pb2.DatasetFeatureStatistics()))]
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertSlicingAwareTransformOutputEqual(examples, generator,
expected_result)
def test_with_empty_dict(self):
examples = [{}]
expected_result = []
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def expand(self, dataset):
# Initialize a list of stats generators to run.
stats_generators = [
# Create common stats generator.
common_stats_generator.CommonStatsGenerator(
schema=self._options.schema,
num_values_histogram_buckets=\
self._options.num_values_histogram_buckets,
epsilon=self._options.epsilon),
# Create numeric stats generator.
numeric_stats_generator.NumericStatsGenerator(
schema=self._options.schema,
num_histogram_buckets=self._options.num_histogram_buckets,
num_quantiles_histogram_buckets=\
self._options.num_quantiles_histogram_buckets,
epsilon=self._options.epsilon),
# Create string stats generator.
string_stats_generator.StringStatsGenerator(
schema=self._options.schema),
# Create topk stats generator.
top_k_stats_generator.TopKStatsGenerator(
schema=self._options.schema,
num_top_values=self._options.num_top_values,
num_rank_histogram_buckets=\
self._options.num_rank_histogram_buckets),
# Create uniques stats generator.
uniques_stats_generator.UniquesStatsGenerator(
schema=self._options.schema)
]
if self._options.generators is not None:
# Add custom stats generators.
stats_generators.extend(self._options.generators)
# Profile and then batch input examples.
batched_dataset = (dataset
| 'Profile' >> profile_util.Profile()
| 'BatchInputs' >> batch_util.BatchExamples())
# If a set of whitelist features are provided, keep only those features.
filtered_dataset = batched_dataset
if self._options.feature_whitelist:
filtered_dataset = (
batched_dataset | 'RemoveNonWhitelistedFeatures' >> beam.Map(
_filter_features,
feature_whitelist=self._options.feature_whitelist))
return (filtered_dataset | 'RunStatsGenerators' >>
stats_impl.GenerateStatisticsImpl(stats_generators))
def _get_default_generators(
options, in_memory = False
):
"""Initialize default list of stats 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.
"""
stats_generators = [
common_stats_generator.CommonStatsGenerator(
schema=options.schema,
weight_feature=options.weight_feature,
num_values_histogram_buckets=options.num_values_histogram_buckets,
epsilon=options.epsilon),
numeric_stats_generator.NumericStatsGenerator(
schema=options.schema,
weight_feature=options.weight_feature,
num_histogram_buckets=options.num_histogram_buckets,
num_quantiles_histogram_buckets=\
options.num_quantiles_histogram_buckets,
epsilon=options.epsilon),
string_stats_generator.StringStatsGenerator(
schema=options.schema)
]
if in_memory:
stats_generators.append(
top_k_uniques_combiner_stats_generator.
TopKUniquesCombinerStatsGenerator(
schema=options.schema,
weight_feature=options.weight_feature,
num_top_values=options.num_top_values,
num_rank_histogram_buckets=options.num_rank_histogram_buckets))
else:
stats_generators.extend([
top_k_stats_generator.TopKStatsGenerator(
schema=options.schema,
weight_feature=options.weight_feature,
num_top_values=options.num_top_values,
num_rank_histogram_buckets=options.num_rank_histogram_buckets),
uniques_stats_generator.UniquesStatsGenerator(schema=options.schema)
])
return stats_generators
def test_single_unicode_feature(self):
# fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e'
batches = [{'fa': np.array([np.array(['a', 'b', 'c', 'e']),
np.array(['a', 'c', 'd', 'a'])],
dtype=np.unicode_)},
{'fa': np.array([np.array(['a', 'b', 'c', 'd'])],
dtype=np.unicode_)}]
expected_result_fa = text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 5
}
}""", statistics_pb2.DatasetFeatureStatistics())
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertTransformOutputEqual(batches, generator, [expected_result_fa])
def test_with_categorical_feature(self):
examples = [{
'fa': np.array([12, 23, 34, 12])
}, {
'fa': np.array([45, 23])
}, {
'fa': np.array([12, 12, 34, 45])
}]
expected_result = [
text_format.Parse(
"""
features {
name: 'fa'
type: INT
string_stats {
unique: 4
}
}""", statistics_pb2.DatasetFeatureStatistics())
]
schema = text_format.Parse(
"""
feature {
name: "fa"
type: INT
int_domain {
is_categorical: true
}
}
""", schema_pb2.Schema())
generator = uniques_stats_generator.UniquesStatsGenerator(
schema=schema)
self.assertSlicingAwareTransformOutputEqual(
examples,
generator,
expected_result,
add_default_slice_key_to_input=True,
add_default_slice_key_to_output=True)
def test_with_missing_feature(self):
# fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e'
# fb: 1 'a', 1 'b', 2 'c'
examples = [{
'fa': np.array(['a', 'b', 'c', 'e']),
'fb': np.array(['a', 'c', 'c'])
}, {
'fa': None,
'fb': np.array(['b'])
}, {
'fa': np.array(['a', 'c', 'd']),
'fb': None
}, {
'fa': np.array(['a', 'a', 'b', 'c', 'd'])
}, {
'fa': None
}]
expected_result_fa = text_format.Parse(
"""
features {
name: 'fa'
type: STRING
string_stats {
unique: 5
}
}""", statistics_pb2.DatasetFeatureStatistics())
expected_result_fb = text_format.Parse(
"""
features {
name: 'fb'
type: STRING
string_stats {
unique: 3
}
}""", statistics_pb2.DatasetFeatureStatistics())
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertTransformOutputEqual(
examples, generator, [expected_result_fa, expected_result_fb])
def expand(self, dataset):
# Initialize a list of stats generators to run.
stats_generators = [
# Create common stats generator.
common_stats_generator.CommonStatsGenerator(
schema=self._options.schema,
num_values_histogram_buckets=\
self._options.num_values_histogram_buckets,
epsilon=self._options.epsilon),
# Create numeric stats generator.
numeric_stats_generator.NumericStatsGenerator(
schema=self._options.schema,
num_histogram_buckets=self._options.num_histogram_buckets,
num_quantiles_histogram_buckets=\
self._options.num_quantiles_histogram_buckets,
epsilon=self._options.epsilon),
# Create string stats generator.
string_stats_generator.StringStatsGenerator(
schema=self._options.schema),
# Create topk stats generator.
top_k_stats_generator.TopKStatsGenerator(
schema=self._options.schema,
num_top_values=self._options.num_top_values,
num_rank_histogram_buckets=\
self._options.num_rank_histogram_buckets),
# Create uniques stats generator.
uniques_stats_generator.UniquesStatsGenerator(
schema=self._options.schema)
]
if self._options.generators is not None:
# Add custom stats generators.
stats_generators.extend(self._options.generators)
# Profile the input examples.
dataset |= 'ProfileExamples' >> profile_util.Profile()
# Sample input data if sample_count option is provided.
if self._options.sample_count is not None:
# beam.combiners.Sample.FixedSizeGlobally returns a
# PCollection[List[types.Example]], which we then flatten to get a
# PCollection[types.Example].
dataset |= ('SampleExamples(%s)' % self._options.sample_count >>
beam.combiners.Sample.FixedSizeGlobally(
self._options.sample_count)
| 'FlattenExamples' >> beam.FlatMap(lambda lst: lst))
elif self._options.sample_rate is not None:
dataset |= ('SampleExamplesAtRate(%s)' % self._options.sample_rate
>> beam.FlatMap(_sample_at_rate,
sample_rate=self._options.sample_rate))
# Batch the input examples.
desired_batch_size = (None if self._options.sample_count is None else
self._options.sample_count)
dataset = (dataset | 'BatchExamples' >> batch_util.BatchExamples(
desired_batch_size=desired_batch_size))
# If a set of whitelist features are provided, keep only those features.
if self._options.feature_whitelist:
dataset |= ('RemoveNonWhitelistedFeatures' >> beam.Map(
_filter_features,
feature_whitelist=self._options.feature_whitelist))
return (dataset | 'RunStatsGenerators' >>
stats_impl.GenerateStatisticsImpl(stats_generators))
def test_with_empty_list(self): batches = [] expected_result = [] generator = uniques_stats_generator.UniquesStatsGenerator() self.assertTransformOutputEqual(batches, generator, expected_result)
def test_with_empty_dict(self):
examples = [{}]
expected_result = []
generator = uniques_stats_generator.UniquesStatsGenerator()
self.assertTransformOutputEqual(examples, generator, expected_result)
def expand(self, dataset):
# Initialize a list of stats generators to run.
stats_generators = [
# Create common stats generator.
common_stats_generator.CommonStatsGenerator(
schema=self._options.schema,
weight_feature=self._options.weight_feature,
num_values_histogram_buckets=\
self._options.num_values_histogram_buckets,
epsilon=self._options.epsilon),
# Create numeric stats generator.
numeric_stats_generator.NumericStatsGenerator(
schema=self._options.schema,
weight_feature=self._options.weight_feature,
num_histogram_buckets=self._options.num_histogram_buckets,
num_quantiles_histogram_buckets=\
self._options.num_quantiles_histogram_buckets,
epsilon=self._options.epsilon),
# Create string stats generator.
string_stats_generator.StringStatsGenerator(
schema=self._options.schema),
# Create topk stats generator.
top_k_stats_generator.TopKStatsGenerator(
schema=self._options.schema,
weight_feature=self._options.weight_feature,
num_top_values=self._options.num_top_values,
num_rank_histogram_buckets=\
self._options.num_rank_histogram_buckets),
# Create uniques stats generator.
uniques_stats_generator.UniquesStatsGenerator(
schema=self._options.schema)
]
if self._options.generators is not None:
# Add custom stats generators.
stats_generators.extend(self._options.generators)
# Batch the input examples.
desired_batch_size = (None if self._options.sample_count is None else
self._options.sample_count)
dataset = (dataset | 'BatchExamples' >> batch_util.BatchExamples(
desired_batch_size=desired_batch_size))
# If a set of whitelist features are provided, keep only those features.
if self._options.feature_whitelist:
dataset |= ('RemoveNonWhitelistedFeatures' >> beam.Map(
_filter_features,
feature_whitelist=self._options.feature_whitelist))
result_protos = []
# Iterate over the stats generators. For each generator,
# a) if it is a CombinerStatsGenerator, wrap it as a beam.CombineFn
# and run it.
# b) if it is a TransformStatsGenerator, wrap it as a beam.PTransform
# and run it.
for generator in stats_generators:
if isinstance(generator, stats_generator.CombinerStatsGenerator):
result_protos.append(dataset
| generator.name >> beam.CombineGlobally(
_CombineFnWrapper(generator)))
elif isinstance(generator,
stats_generator.TransformStatsGenerator):
result_protos.append(dataset
| generator.name >> generator.ptransform)
else:
raise TypeError(
'Statistics generator must extend one of '
'CombinerStatsGenerator or TransformStatsGenerator, '
'found object of type %s' % generator.__class__.__name__)
# Each stats generator will output a PCollection of DatasetFeatureStatistics
# protos. We now flatten the list of PCollections into a single PCollection,
# then merge the DatasetFeatureStatistics protos in the PCollection into a
# single DatasetFeatureStatisticsList proto.
return (result_protos | 'FlattenFeatureStatistics' >> beam.Flatten()
| 'MergeDatasetFeatureStatisticsProtos' >>
beam.CombineGlobally(_merge_dataset_feature_stats_protos)
| 'MakeDatasetFeatureStatisticsListProto' >>
beam.Map(_make_dataset_feature_statistics_list_proto))
