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_numeric_stats_generator_categorical_feature(self):
# input with two batches: first batch has two examples and second batch
# has a single example.
batches = [{
'a': np.array([np.array([1, 0]),
np.array([0, 1, 0])])
}, {
'a': np.array([np.array([1])])
}]
expected_result = {}
schema = text_format.Parse(
"""
feature {
name: "a"
type: INT
int_domain {
is_categorical: true
}
}
""", schema_pb2.Schema())
generator = numeric_stats_generator.NumericStatsGenerator(
schema=schema)
self.assertCombinerOutputEqual(batches, 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,
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_numeric_stats_generator_invalid_value_type(self):
batches = [{'a': np.array([np.array([1.34]), np.array([12])])}]
generator = numeric_stats_generator.NumericStatsGenerator()
with self.assertRaises(TypeError):
self.assertCombinerOutputEqual(batches, generator, None)
def test_numeric_stats_generator_empty_list(self):
batches = []
expected_result = {}
generator = numeric_stats_generator.NumericStatsGenerator()
self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_numeric_stats_generator_empty_batch(self):
batches = [{'a': np.array([])}]
expected_result = {}
generator = numeric_stats_generator.NumericStatsGenerator()
self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_numeric_stats_generator_with_missing_feature(self):
# Input with two batches: first batch has two examples and second batch
# has a single example. The first batch is missing feature 'b'.
batches = [{
'a':
np.array([np.array([1.0, 2.0]),
np.array([3.0, 4.0, 5.0])])
}, {
'a': np.array([np.array([1.0])]),
'b': np.array([np.linspace(1, 3000, 3000, dtype=np.int32)])
}]
expected_result = {
'a':
text_format.Parse(
"""
name: 'a'
type: FLOAT
num_stats {
mean: 2.66666666
std_dev: 1.49071198
num_zeros: 0
min: 1.0
max: 5.0
median: 3.0
histograms {
buckets {
low_value: 1.0
high_value: 2.3333333
sample_count: 2.9866667
}
buckets {
low_value: 2.3333333
high_value: 3.6666667
sample_count: 1.0066667
}
buckets {
low_value: 3.6666667
high_value: 5.0
sample_count: 2.0066667
}
type: STANDARD
}
histograms {
buckets {
low_value: 1.0
high_value: 1.0
sample_count: 1.5
}
buckets {
low_value: 1.0
high_value: 3.0
sample_count: 1.5
}
buckets {
low_value: 3.0
high_value: 4.0
sample_count: 1.5
}
buckets {
low_value: 4.0
high_value: 5.0
sample_count: 1.5
}
type: QUANTILES
}
}
""", statistics_pb2.FeatureNameStatistics()),
'b':
text_format.Parse(
"""
name: 'b'
type: INT
num_stats {
mean: 1500.5
std_dev: 866.025355672
min: 1.0
max: 3000.0
median: 1501.0
histograms {
buckets {
low_value: 1.0
high_value: 1000.66666667
sample_count: 999.666666667
}
buckets {
low_value: 1000.66666667
high_value: 2000.33333333
sample_count: 999.666666667
}
buckets {
low_value: 2000.33333333
high_value: 3000.0
sample_count: 1000.66666667
}
type: STANDARD
}
histograms {
buckets {
low_value: 1.0
high_value: 751.0
sample_count: 750.0
}
buckets {
low_value: 751.0
high_value: 1501.0
sample_count: 750.0
}
buckets {
low_value: 1501.0
high_value: 2250.0
sample_count: 750.0
}
buckets {
low_value: 2250.0
high_value: 3000.0
sample_count: 750.0
}
type: QUANTILES
}
}
""", statistics_pb2.FeatureNameStatistics())
}
generator = numeric_stats_generator.NumericStatsGenerator(
num_histogram_buckets=3,
num_quantiles_histogram_buckets=4,
epsilon=0.001)
self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_numeric_stats_generator_single_feature(self):
# input with two batches: first batch has two examples and second batch
# has a single example.
batches = [{
'a':
np.array([np.array([1.0, 2.0]),
np.array([3.0, 4.0, 5.0])])
}, {
'a': np.array([np.array([1.0])])
}]
expected_result = {
'a':
text_format.Parse(
"""
name: 'a'
type: FLOAT
num_stats {
mean: 2.66666666
std_dev: 1.49071198
num_zeros: 0
min: 1.0
max: 5.0
median: 3.0
histograms {
buckets {
low_value: 1.0
high_value: 2.3333333
sample_count: 2.9866667
}
buckets {
low_value: 2.3333333
high_value: 3.6666667
sample_count: 1.0066667
}
buckets {
low_value: 3.6666667
high_value: 5.0
sample_count: 2.0066667
}
type: STANDARD
}
histograms {
buckets {
low_value: 1.0
high_value: 1.0
sample_count: 1.5
}
buckets {
low_value: 1.0
high_value: 3.0
sample_count: 1.5
}
buckets {
low_value: 3.0
high_value: 4.0
sample_count: 1.5
}
buckets {
low_value: 4.0
high_value: 5.0
sample_count: 1.5
}
type: QUANTILES
}
}
""", statistics_pb2.FeatureNameStatistics())
}
generator = numeric_stats_generator.NumericStatsGenerator(
num_histogram_buckets=3, num_quantiles_histogram_buckets=4)
self.assertCombinerOutputEqual(batches, 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))
def generate_statistics_in_memory(examples,
options=stats_options.StatsOptions()):
"""Generates statistics for an in-memory list of examples.
Args:
examples: A list of input examples.
options: Options for generating data statistics.
Returns:
A DatasetFeatureStatisticsList proto.
"""
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),
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),
]
if options.generators is not None:
for generator in options.generators:
if isinstance(generator, stats_generator.CombinerStatsGenerator):
stats_generators.append(generator)
else:
raise TypeError(
'Statistics generator used in '
'generate_statistics_in_memory must '
'extend CombinerStatsGenerator, found object of type '
'%s.' % generator.__class__.__name__)
batch = batch_util.merge_single_batch(examples)
# If whitelist features are provided, keep only those features.
if options.feature_whitelist:
batch = {
feature_name: batch[feature_name]
for feature_name in options.feature_whitelist
}
outputs = [
generator.extract_output(
generator.add_input(generator.create_accumulator(), batch))
for generator in stats_generators
]
return _make_dataset_feature_statistics_list_proto(
_merge_dataset_feature_stats_protos(outputs))
