def generate_statistics_from_tfrecord(
data_location: Text,
output_path: Optional[bytes] = None,
stats_options: options.StatsOptions = options.StatsOptions(),
pipeline_options: Optional[PipelineOptions] = None,
) -> statistics_pb2.DatasetFeatureStatisticsList:
"""Compute data statistics from TFRecord files containing TFExamples.
Runs a Beam pipeline to compute the data statistics and return the result
data statistics proto.
This is a convenience method for users with data in TFRecord format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'GenerateStatistics'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
output_path: The file path to output data statistics result to. If None, we
use a temporary directory. It will be a TFRecord file containing a single
data statistics proto, and can be read with the 'load_statistics' API.
If you run this function on Google Cloud, you must specify an
output_path. Specifying None may cause an error.
stats_options: `tfdv.StatsOptions` for generating data statistics.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
Returns:
A DatasetFeatureStatisticsList proto.
"""
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(), 'data_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.io.gfile.exists(output_dir_path):
tf.io.gfile.makedirs(output_dir_path)
batch_size = stats_options.desired_batch_size
# PyLint doesn't understand Beam PTransforms.
# pylint: disable=no-value-for-parameter
with beam.Pipeline(options=pipeline_options) as p:
# Auto detect tfrecord file compression format based on input data
# path suffix.
_ = (
p
| 'ReadData' >> (tf_example_record.TFExampleRecord(
file_pattern=data_location,
schema=None,
telemetry_descriptors=['tfdv', 'generate_statistics_from_tfrecord'])
.BeamSource(batch_size))
| 'GenerateStatistics' >> stats_api.GenerateStatistics(stats_options)
| 'WriteStatsOutput' >>
(stats_api.WriteStatisticsToTFRecord(output_path)))
return stats_util.load_statistics(output_path)
def test_write_load_stats_text(self):
stats = text_format.Parse(
"""
datasets { name: 'abc' }
""", statistics_pb2.DatasetFeatureStatisticsList())
stats_path = os.path.join(FLAGS.test_tmpdir, 'stats.pbtxt')
stats_util.write_stats_text(stats=stats, output_path=stats_path)
self.assertEqual(stats,
stats_util.load_stats_text(input_path=stats_path))
self.assertEqual(stats,
stats_util.load_statistics(input_path=stats_path))
def test_load_stats_tfrecord(self):
stats = text_format.Parse(
"""
datasets { name: 'abc' }
""", statistics_pb2.DatasetFeatureStatisticsList())
stats_path = os.path.join(FLAGS.test_tmpdir, 'stats.tfrecord')
with tf.io.TFRecordWriter(stats_path) as writer:
writer.write(stats.SerializeToString())
self.assertEqual(stats,
stats_util.load_stats_tfrecord(input_path=stats_path))
self.assertEqual(stats,
stats_util.load_statistics(input_path=stats_path))
def test_write_stats_to_tfrecrod(self):
stats = text_format.Parse(
"""
datasets {
name: 'x'
num_examples: 100
}
""", statistics_pb2.DatasetFeatureStatisticsList())
output_path = os.path.join(self._get_temp_dir(), 'stats')
with beam.Pipeline() as p:
_ = (p | beam.Create([stats])
| stats_api.WriteStatisticsToTFRecord(output_path))
stats_from_file = stats_util.load_statistics(output_path)
self.assertLen(stats_from_file.datasets, 1)
test_util.assert_dataset_feature_stats_proto_equal(
self, stats_from_file.datasets[0], stats.datasets[0])
def generate_statistics_from_csv(
data_location: Text,
column_names: Optional[List[types.FeatureName]] = None,
delimiter: Text = ',',
output_path: Optional[bytes] = None,
stats_options: options.StatsOptions = options.StatsOptions(),
pipeline_options: Optional[PipelineOptions] = None,
compression_type: Text = CompressionTypes.AUTO,
) -> statistics_pb2.DatasetFeatureStatisticsList:
"""Compute data statistics from CSV files.
Runs a Beam pipeline to compute the data statistics and return the result
data statistics proto.
This is a convenience method for users with data in CSV format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'GenerateStatistics'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
column_names: A list of column names to be treated as the CSV header. Order
must match the order in the input CSV files. If this argument is not
specified, we assume the first line in the input CSV files as the
header. Note that this option is valid only for 'csv' input file format.
delimiter: A one-character string used to separate fields in a CSV file.
output_path: The file path to output data statistics result to. If None, we
use a temporary directory. It will be a TFRecord file containing a single
data statistics proto, and can be read with the 'load_statistics' API.
If you run this function on Google Cloud, you must specify an
output_path. Specifying None may cause an error.
stats_options: `tfdv.StatsOptions` for generating data statistics.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
compression_type: Used to handle compressed input files. Default value is
CompressionTypes.AUTO, in which case the file_path's extension will be
used to detect the compression.
Returns:
A DatasetFeatureStatisticsList proto.
"""
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(), 'data_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.io.gfile.exists(output_dir_path):
tf.io.gfile.makedirs(output_dir_path)
batch_size = (stats_options.desired_batch_size
if stats_options.desired_batch_size
and stats_options.desired_batch_size > 0 else
constants.DEFAULT_DESIRED_INPUT_BATCH_SIZE)
# PyLint doesn't understand Beam PTransforms.
# pylint: disable=no-value-for-parameter
with beam.Pipeline(options=pipeline_options) as p:
# If a header is not provided, assume the first line in a file
# to be the header.
skip_header_lines = 1 if column_names is None else 0
if column_names is None:
column_names = get_csv_header(data_location, delimiter)
_ = (
p
| 'ReadData' >> beam.io.textio.ReadFromText(
file_pattern=data_location,
skip_header_lines=skip_header_lines,
compression_type=compression_type)
| 'DecodeData' >> csv_decoder.DecodeCSV(
column_names=column_names,
delimiter=delimiter,
schema=stats_options.schema
if stats_options.infer_type_from_schema else None,
desired_batch_size=batch_size)
|
'GenerateStatistics' >> stats_api.GenerateStatistics(stats_options)
| 'WriteStatsOutput' >>
stats_api.WriteStatisticsToTFRecord(output_path))
return stats_util.load_statistics(output_path)
def validate_examples_in_tfrecord(
data_location: Text,
stats_options: options.StatsOptions,
output_path: Optional[Text] = None,
# TODO(b/131719250): Add option to output a sample of anomalous examples for
# each anomaly reason.
pipeline_options: Optional[PipelineOptions] = None,
) -> statistics_pb2.DatasetFeatureStatisticsList:
"""Validates TFExamples in TFRecord files.
Runs a Beam pipeline to detect anomalies on a per-example basis. If this
function detects anomalous examples, it generates summary statistics regarding
the set of examples that exhibit each anomaly.
This is a convenience function for users with data in TFRecord format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'IdentifyAnomalousExamples'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
stats_options: `tfdv.StatsOptions` for generating data statistics. This must
contain a schema.
output_path: The file path to output data statistics result to. If None, the
function uses a temporary directory. The output will be a TFRecord file
containing a single data statistics list proto, and can be read with the
'load_statistics' function.
If you run this function on Google Cloud, you must specify an
output_path. Specifying None may cause an error.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
Returns:
A DatasetFeatureStatisticsList proto in which each dataset consists of the
set of examples that exhibit a particular anomaly.
Raises:
ValueError: If the specified stats_options does not include a schema.
"""
if stats_options.schema is None:
raise ValueError('The specified stats_options must include a schema.')
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(),
'anomaly_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.io.gfile.exists(output_dir_path):
tf.io.gfile.makedirs(output_dir_path)
with beam.Pipeline(options=pipeline_options) as p:
_ = (p
| 'ReadData' >> (tf_example_record.TFExampleRecord(
file_pattern=data_location,
schema=None,
telemetry_descriptors=[
'tfdv', 'validate_examples_in_tfrecord'
]).BeamSource(batch_size=1))
| 'DetectAnomalies' >>
validation_api.IdentifyAnomalousExamples(stats_options)
| 'GenerateSummaryStatistics' >>
stats_impl.GenerateSlicedStatisticsImpl(stats_options,
is_slicing_enabled=True)
| 'WriteStatsOutput' >>
stats_api.WriteStatisticsToTFRecord(output_path))
return stats_util.load_statistics(output_path)
def validate_examples_in_csv(
data_location: Text,
stats_options: options.StatsOptions,
column_names: Optional[List[types.FeatureName]] = None,
delimiter: Text = ',',
output_path: Optional[Text] = None,
# TODO(b/131719250): Add option to output a sample of anomalous examples for
# each anomaly reason.
pipeline_options: Optional[PipelineOptions] = None,
) -> statistics_pb2.DatasetFeatureStatisticsList:
"""Validates examples in csv files.
Runs a Beam pipeline to detect anomalies on a per-example basis. If this
function detects anomalous examples, it generates summary statistics regarding
the set of examples that exhibit each anomaly.
This is a convenience function for users with data in CSV format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'IdentifyAnomalousExamples'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
stats_options: `tfdv.StatsOptions` for generating data statistics. This must
contain a schema.
column_names: A list of column names to be treated as the CSV header. Order
must match the order in the input CSV files. If this argument is not
specified, we assume the first line in the input CSV files as the header.
Note that this option is valid only for 'csv' input file format.
delimiter: A one-character string used to separate fields in a CSV file.
output_path: The file path to output data statistics result to. If None, the
function uses a temporary directory. The output will be a TFRecord file
containing a single data statistics list proto, and can be read with the
'load_statistics' function.
If you run this function on Google Cloud, you must specify an
output_path. Specifying None may cause an error.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
Returns:
A DatasetFeatureStatisticsList proto in which each dataset consists of the
set of examples that exhibit a particular anomaly.
Raises:
ValueError: If the specified stats_options does not include a schema.
"""
if stats_options.schema is None:
raise ValueError('The specified stats_options must include a schema.')
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(),
'anomaly_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.io.gfile.exists(output_dir_path):
tf.io.gfile.makedirs(output_dir_path)
# If a header is not provided, assume the first line in a file
# to be the header.
skip_header_lines = 1 if column_names is None else 0
if column_names is None:
column_names = stats_gen_lib.get_csv_header(data_location, delimiter)
with beam.Pipeline(options=pipeline_options) as p:
_ = (p
| 'ReadData' >> beam.io.textio.ReadFromText(
file_pattern=data_location,
skip_header_lines=skip_header_lines)
| 'DecodeData' >> csv_decoder.DecodeCSV(
column_names=column_names,
delimiter=delimiter,
schema=stats_options.schema
if stats_options.infer_type_from_schema else None,
desired_batch_size=1)
| 'DetectAnomalies' >>
validation_api.IdentifyAnomalousExamples(stats_options)
| 'GenerateSummaryStatistics' >>
stats_impl.GenerateSlicedStatisticsImpl(stats_options,
is_slicing_enabled=True)
| 'WriteStatsOutput' >>
stats_api.WriteStatisticsToTFRecord(output_path))
return stats_util.load_statistics(output_path)
def generate_statistics_from_tfrecord(
data_location: Text,
output_path: Optional[bytes] = None,
stats_options: options.StatsOptions = options.StatsOptions(),
pipeline_options: Optional[PipelineOptions] = None,
compression_type: Text = CompressionTypes.AUTO,
) -> statistics_pb2.DatasetFeatureStatisticsList:
"""Compute data statistics from TFRecord files containing TFExamples.
Runs a Beam pipeline to compute the data statistics and return the result
data statistics proto.
This is a convenience method for users with data in TFRecord format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'GenerateStatistics'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
output_path: The file path to output data statistics result to. If None, we
use a temporary directory. It will be a TFRecord file containing a single
data statistics proto, and can be read with the 'load_statistics' API.
If you run this function on Google Cloud, you must specify an
output_path. Specifying None may cause an error.
stats_options: `tfdv.StatsOptions` for generating data statistics.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
compression_type: Used to handle compressed input files. Default value is
CompressionTypes.AUTO, in which case the file_path's extension will be
used to detect the compression.
Returns:
A DatasetFeatureStatisticsList proto.
"""
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(), 'data_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.io.gfile.exists(output_dir_path):
tf.io.gfile.makedirs(output_dir_path)
batch_size = (stats_options.desired_batch_size
if stats_options.desired_batch_size
and stats_options.desired_batch_size > 0 else
constants.DEFAULT_DESIRED_INPUT_BATCH_SIZE)
# PyLint doesn't understand Beam PTransforms.
# pylint: disable=no-value-for-parameter
with beam.Pipeline(options=pipeline_options) as p:
# Auto detect tfrecord file compression format based on input data
# path suffix.
_ = (
p
| 'ReadData' >> beam.io.ReadFromTFRecord(
file_pattern=data_location, compression_type=compression_type)
| 'DecodeData' >>
tf_example_decoder.DecodeTFExample(desired_batch_size=batch_size)
|
'GenerateStatistics' >> stats_api.GenerateStatistics(stats_options)
# TODO(b/112014711) Implement a custom sink to write the stats proto.
| 'WriteStatsOutput' >>
stats_api.WriteStatisticsToTFRecord(output_path))
return stats_util.load_statistics(output_path)
def validate_examples_in_tfrecord(
data_location: Text,
stats_options: options.StatsOptions,
output_path: Optional[Text] = None,
pipeline_options: Optional[PipelineOptions] = None,
num_sampled_examples=0,
) -> Union[statistics_pb2.DatasetFeatureStatisticsList, Tuple[
statistics_pb2.DatasetFeatureStatisticsList, Mapping[
str, List[tf.train.Example]]]]:
"""Validates TFExamples in TFRecord files.
Runs a Beam pipeline to detect anomalies on a per-example basis. If this
function detects anomalous examples, it generates summary statistics regarding
the set of examples that exhibit each anomaly.
This is a convenience function for users with data in TFRecord format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'IdentifyAnomalousExamples'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
stats_options: `tfdv.StatsOptions` for generating data statistics. This must
contain a schema.
output_path: The file path to output data statistics result to. If None, the
function uses a temporary directory. The output will be a TFRecord file
containing a single data statistics list proto, and can be read with the
'load_statistics' function.
If you run this function on Google Cloud, you must specify an
output_path. Specifying None may cause an error.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
num_sampled_examples: If set, returns up to this many examples
of each anomaly type as a map from anomaly reason string to a list of
tf.Examples.
Returns:
If num_sampled_examples is zero, returns a single
DatasetFeatureStatisticsList proto in which each dataset consists of the
set of examples that exhibit a particular anomaly. If
num_sampled_examples is nonzero, returns the same statistics
proto as well as a mapping from anomaly to a list of tf.Examples that
exhibited that anomaly.
Raises:
ValueError: If the specified stats_options does not include a schema.
"""
if stats_options.schema is None:
raise ValueError('The specified stats_options must include a schema.')
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(),
'anomaly_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.io.gfile.exists(output_dir_path):
tf.io.gfile.makedirs(output_dir_path)
with io_util.Materializer(output_dir_path) as sample_materializer:
with beam.Pipeline(options=pipeline_options) as p:
anomalous_examples = (
p
| 'ReadData' >> (tf_example_record.TFExampleRecord(
file_pattern=data_location,
schema=None,
telemetry_descriptors=[
'tfdv', 'validate_examples_in_tfrecord'
]).BeamSource(batch_size=1))
| 'DetectAnomalies' >>
validation_api.IdentifyAnomalousExamples(stats_options))
_ = (anomalous_examples | 'GenerateSummaryStatistics' >>
stats_impl.GenerateSlicedStatisticsImpl(
stats_options, is_slicing_enabled=True)
| 'WriteStatsOutput' >>
stats_api.WriteStatisticsToTFRecord(output_path))
if num_sampled_examples:
# TODO(b/68154497): Relint
# pylint: disable=no-value-for-parameter
_ = (
anomalous_examples
| 'Sample' >>
beam.combiners.Sample.FixedSizePerKey(num_sampled_examples)
| 'ToExample' >> _record_batch_to_example_fn(
example_coder.RecordBatchToExamplesEncoder(
stats_options.schema))
| 'WriteSamples' >> sample_materializer.writer())
# pylint: enable=no-value-for-parameter
if num_sampled_examples:
samples_per_reason = collections.defaultdict(list)
for reason, serialized_example in sample_materializer.reader():
samples_per_reason[reason].append(
tf.train.Example.FromString(serialized_example))
return stats_util.load_statistics(output_path), samples_per_reason
return stats_util.load_statistics(output_path)
