'f': statistics_pb2.FeatureNameStatistics.FLOAT,

'i': statistics_pb2.FeatureNameStatistics.INT,

'u': statistics_pb2.FeatureNameStatistics.INT,

'S': statistics_pb2.FeatureNameStatistics.STRING,

'O': statistics_pb2.FeatureNameStatistics.STRING,

'U': statistics_pb2.FeatureNameStatistics.STRING,

"""Returns the value decoded as utf-8, or None if it cannot be decoded.

Args:

value: The bytes value to decode.

Returns:

The value decoded as utf-8, or None, if the value cannot be decoded.

"""

try:

decoded_value = value.decode('utf-8')

except UnicodeError:

return None

dtype: np.dtype) -> Optional[types.FeatureNameStatisticsType]:

"""Get feature type from numpy dtype.

Args:

dtype: Numpy dtype.

Returns:

A statistics_pb2.FeatureNameStatistics.Type value.

"""

feature_path: types.FeaturePath,

arrow_type: pa.DataType) -> Optional[types.FeatureNameStatisticsType]:

"""Get feature type from Arrow type.

Args:

feature_path: path of the feature.

arrow_type: Arrow DataType.

Returns:

A statistics_pb2.FeatureNameStatistics.Type value or None if arrow_type

is null (which means it cannot be determined for now).

Raises:

TypeError: if the type is not supported.

"""

if pa.types.is_null(arrow_type):

return None

if not array_util.is_list_like(arrow_type):

raise TypeError('Expected feature column to be a '

'(Large)List<primitive|struct> or null, but feature {} '

'was {}.'.format(feature_path, arrow_type))

value_type = array_util.get_innermost_nested_type(arrow_type)

if pa.types.is_integer(value_type):

return statistics_pb2.FeatureNameStatistics.INT

elif pa.types.is_floating(value_type):

return statistics_pb2.FeatureNameStatistics.FLOAT

elif arrow_util.is_binary_like(value_type):

return statistics_pb2.FeatureNameStatistics.STRING

elif pa.types.is_struct(value_type):

return statistics_pb2.FeatureNameStatistics.STRUCT

elif pa.types.is_null(value_type):

return None

raise TypeError('Feature {} has unsupported arrow type: {}'.format(

stats_values: Dict[types.FeaturePath, Dict[Text, float]]

) -> statistics_pb2.DatasetFeatureStatistics:

"""Builds DatasetFeatureStatistics proto with custom stats from input dict.

Args:

stats_values: A Dict[FeaturePath, Dict[str,float]] where the keys are

feature paths, and values are Dicts with keys denoting name of the custom

statistic and values denoting the value of the custom statistic

for the feature.

Ex. {

FeaturePath(('feature_1',)): {

'Mutual Information': 0.5,

'Correlation': 0.1 },

FeaturePath(('feature_2',)): {

'Mutual Information': 0.8,

'Correlation': 0.6 }

}

Returns:

DatasetFeatureStatistics proto containing the custom statistics for each

feature in the dataset.

"""

result = statistics_pb2.DatasetFeatureStatistics()

# Sort alphabetically by feature name to have deterministic ordering

feature_paths = sorted(stats_values.keys())

for feature_path in feature_paths:

feature_stats_proto = _make_feature_stats_proto(stats_values[feature_path],

feature_path)

new_feature_stats_proto = result.features.add()

new_feature_stats_proto.CopyFrom(feature_stats_proto)

stats_values: Dict[Text, float],

feature_path: types.FeaturePath) -> statistics_pb2.FeatureNameStatistics:

"""Creates the FeatureNameStatistics proto for one feature.

Args:

stats_values: A Dict[str,float] where the key of the dict is the name of the

custom statistic and the value is the numeric value of the custom

statistic of that feature. Ex. {

'Mutual Information': 0.5,

'Correlation': 0.1 }

feature_path: The path of the feature.

Returns:

A FeatureNameStatistic proto containing the custom statistics for a

feature.

"""

result = statistics_pb2.FeatureNameStatistics()

result.path.CopyFrom(feature_path.to_proto())

# Sort alphabetically by statistic name to have deterministic ordering

stat_names = sorted(stats_values.keys())

for stat_name in stat_names:

result.custom_stats.add(name=stat_name, num=stats_values[stat_name])

output_path: Text) -> None:

"""Writes a DatasetFeatureStatisticsList proto to a file in text format.

Args:

stats: A DatasetFeatureStatisticsList proto.

output_path: File path to write the DatasetFeatureStatisticsList proto.

Raises:

TypeError: If the input proto is not of the expected type.

"""

if not isinstance(stats, statistics_pb2.DatasetFeatureStatisticsList):

raise TypeError(

'stats is of type %s, should be a '

'DatasetFeatureStatisticsList proto.' % type(stats).__name__)

stats_proto_text = text_format.MessageToString(stats)

input_path: Text) -> statistics_pb2.DatasetFeatureStatisticsList:

"""Loads the specified DatasetFeatureStatisticsList proto stored in text format.

Args:

input_path: File path from which to load the DatasetFeatureStatisticsList

proto.

Returns:

A DatasetFeatureStatisticsList proto.

"""

stats_proto = statistics_pb2.DatasetFeatureStatisticsList()

stats_text = io_util.read_file_to_string(input_path)

text_format.Parse(stats_text, stats_proto)

input_path: Text) -> statistics_pb2.DatasetFeatureStatisticsList:

"""Loads a serialized DatasetFeatureStatisticsList proto from a file.

Args:

input_path: File path from which to load the DatasetFeatureStatisticsList

proto.

Returns:

A DatasetFeatureStatisticsList proto.

"""

stats_proto = statistics_pb2.DatasetFeatureStatisticsList()

stats_proto.ParseFromString(io_util.read_file_to_string(

input_path, binary_mode=True))

input_path: Text) -> statistics_pb2.DatasetFeatureStatisticsList:

"""Loads data statistics proto from TFRecord file.

Args:

input_path: Data statistics file path.

Returns:

A DatasetFeatureStatisticsList proto.

"""

it = artifacts_io_impl.get_io_provider('tfrecords').record_iterator_impl(

[input_path])

result = next(it)

try:

next(it)

raise ValueError('load_stats_tfrecord expects a single record.')

except StopIteration:

return result

except Exception as e:

feature_path: types.FeaturePath

) -> statistics_pb2.FeatureNameStatistics:

"""Get feature statistics from the dataset statistics.

Args:

stats: A DatasetFeatureStatistics protocol buffer.

feature_path: The path of the feature whose statistics to obtain from the

dataset statistics.

Returns:

A FeatureNameStatistics protocol buffer.

Raises:

TypeError: If the input statistics is not of the expected type.

ValueError: If the input feature is not found in the dataset statistics.

"""

if not isinstance(stats, statistics_pb2.DatasetFeatureStatistics):

raise TypeError('statistics is of type %s, should be a '

'DatasetFeatureStatistics proto.' %

type(stats).__name__)

for feature_stats in stats.features:

if feature_path == types.FeaturePath.from_proto(feature_stats.path):

return feature_stats

raise ValueError('Feature %s not found in the dataset statistics.' %

feature_stats: statistics_pb2.FeatureNameStatistics,

custom_stats_name: Text

) -> Union[float, Text, statistics_pb2.Histogram, statistics_pb2.RankHistogram]:

"""Get custom statistics from the feature statistics.

Args:

feature_stats: A FeatureNameStatistics protocol buffer.

custom_stats_name: The name of the custom statistics to obtain from the

feature statistics proto.

Returns:

The custom statistic.

Raises:

TypeError: If the input feature statistics is not of the expected type.

ValueError: If the custom statistic is not found in the feature statistics.

"""

if not isinstance(feature_stats, statistics_pb2.FeatureNameStatistics):

raise TypeError('feature_stats is of type %s, should be a '

'FeatureNameStatistics proto.' %

type(feature_stats).__name__)

for custom_stats in feature_stats.custom_stats:

if custom_stats.name == custom_stats_name:

return getattr(custom_stats, custom_stats.WhichOneof('val'))

raise ValueError('Custom statistics %s not found in the feature statistics.' %

stats: statistics_pb2.DatasetFeatureStatisticsList,

slice_key: Text) -> statistics_pb2.DatasetFeatureStatisticsList:

"""Get statistics associated with a specific slice.

Args:

stats: A DatasetFeatureStatisticsList protocol buffer.

slice_key: Slice key of the slice.

Returns:

Statistics of the specific slice.

Raises:

ValueError: If the input statistics proto does not have the specified slice

statistics.

"""

for slice_stats in stats.datasets:

if slice_stats.name == slice_key:

result = statistics_pb2.DatasetFeatureStatisticsList()

result.datasets.add().CopyFrom(slice_stats)

return result

input_path: Text) -> statistics_pb2.DatasetFeatureStatisticsList:

"""Loads data statistics proto from file.

Args:

input_path: Data statistics file path. The file should be a one-record

TFRecord file or a plain file containing the statistics proto in Proto

Text Format.

Returns:

A DatasetFeatureStatisticsList proto.

Raises:

IOError: If the input path does not exist.

"""

if not tf.io.gfile.exists(input_path):

raise IOError('Invalid input path {}.'.format(input_path))

try:

return load_stats_tfrecord(input_path)

except Exception: # pylint: disable=broad-except

logging.info('File %s did not look like a TFRecord. Try reading as a plain '

'file.', input_path)

name_or_path_or_steps: Union[str, types.FeaturePath, Iterable[str]]

) -> types.FeaturePath:

if isinstance(name_or_path_or_steps, str):

return types.FeaturePath([name_or_path_or_steps])

if isinstance(name_or_path_or_steps, types.FeaturePath):

return name_or_path_or_steps

"""View of statistics for multiple datasets (slices)."""

def __init__(self, stats_proto: statistics_pb2.DatasetFeatureStatisticsList):

self._statistics = stats_proto

self._slice_map = {} # type: Dict[str, DatasetView]

self._initialized = False

def _init_index(self):

"""Initializes internal mappings."""

# Lazily initialize in case we don't need an index.

if self._initialized:

return

for dataset in self._statistics.datasets:

if dataset.name in self._slice_map:

raise ValueError('Duplicate slice name %s' % dataset.name)

self._slice_map[dataset.name] = DatasetView(dataset)

self._initialized = True

def proto(self) -> statistics_pb2.DatasetFeatureStatisticsList:

"""Retrieve the underlying proto."""

return self._statistics

def get_slice(self, slice_key: str) -> Optional['DatasetView']:

self._init_index()

return self._slice_map.get(slice_key, None)

def get_default_slice(self) -> Optional['DatasetView']:

self._init_index()

if len(self._slice_map) == 1:

for _, v in self._slice_map.items():

return v

return self._slice_map.get(constants.DEFAULT_SLICE_KEY, None)

def get_default_slice_or_die(self) -> 'DatasetView':

# TODO(b/221453427): Update uses, or consider changing get_default_slice.

default_slice = self.get_default_slice()

if default_slice is None:

raise ValueError('Missing default slice')

return default_slice

def list_slices(self) -> Iterable[str]:

self._init_index()

"""View of statistics for a dataset (slice)."""

def __init__(self, stats_proto: statistics_pb2.DatasetFeatureStatistics):

self._feature_map = {} # type: Dict[types.FeaturePath, int]

self._cross_feature_map = {

} # type: Dict[Tuple[types.FeaturePath, types.FeaturePath], int]

self._statistics = stats_proto

self._initialized = False

def _init_index(self):

"""Initializes internal indices. Noop if already initialized."""

if self._initialized:

return

field_identifier = None

for j, feature in enumerate(self._statistics.features):

if field_identifier is None:

field_identifier = feature.WhichOneof('field_id')

elif feature.WhichOneof('field_id') != field_identifier:

raise ValueError(

'Features must be specified with either path or name within a'

' Dataset.')

if field_identifier == 'name':

feature_id = types.FeaturePath([feature.name])

else:

feature_id = types.FeaturePath.from_proto(feature.path)

if feature_id in self._feature_map:

raise ValueError('Duplicate feature %s' % feature_id)

self._feature_map[feature_id] = j

for j, cross_feature in enumerate(self._statistics.cross_features):

feature_id = (types.FeaturePath.from_proto(cross_feature.path_x),

types.FeaturePath.from_proto(cross_feature.path_y))

if feature_id in self._cross_feature_map:

raise ValueError('Duplicate feature %s' % feature_id)

self._cross_feature_map[feature_id] = j

self._initialized = True

def proto(self) -> statistics_pb2.DatasetFeatureStatistics:

"""Retrieve the underlying proto."""

return self._statistics

def get_feature(

self, feature_id: Union[str, types.FeaturePath, Iterable[str]]

) -> Optional['FeatureView']:

"""Retrieve a feature if it exists.

Features specified within the underlying proto by name (instead of path) are

normalized to a length 1 path, and can be referred to as such.

Args:

feature_id: A types.FeaturePath, Iterable[str] consisting of path steps,

or a str, which is converted to a length one path.

Returns:

A FeatureView, or None if feature_id is not present.

"""

feature_id = _normalize_feature_id(feature_id)

self._init_index()

index = self._feature_map.get(feature_id, None)

if index is None:

return None

return FeatureView(self._statistics.features[index])

def get_cross_feature(

self, x_path: Union[str, types.FeaturePath,

Iterable[str]], y_path: Union[str, types.FeaturePath,

Iterable[str]]

) -> Optional['CrossFeatureView']:

"""Retrieve a cross-feature if it exists, or None."""

x_path = _normalize_feature_id(x_path)

y_path = _normalize_feature_id(y_path)

self._init_index()

feature_id = (x_path, y_path)

index = self._cross_feature_map.get(feature_id, None)

if index is None:

return None

return CrossFeatureView(self._statistics.cross_features[index])

def list_features(self) -> Iterable[types.FeaturePath]:

"""Lists feature identifiers."""

self._init_index()

return self._feature_map.keys()

def list_cross_features(

self) -> Iterable[Tuple[types.FeaturePath, types.FeaturePath]]:

"""Lists cross-feature identifiers."""

self._init_index()

return self._cross_feature_map.keys()

def get_derived_feature(

self, deriver_name: str,

source_paths: Sequence[types.FeaturePath]) -> Optional['FeatureView']:

"""Retrieve a derived feature based on a deriver name and its inputs.

Args:

deriver_name: The name of a deriver. Matches validation_derived_source

deriver_name.

source_paths: Source paths for derived features. Matches

validation_derived_source.source_path.

Returns:

FeatureView of derived feature.

Raises:

ValueError if multiple derived features match.

"""

# TODO(b/221453427): Consider indexing if performance becomes an issue.

results = []

for feature in self.proto().features:

if feature.validation_derived_source is None:

continue

if feature.validation_derived_source.deriver_name != deriver_name:

continue

if (len(source_paths) != len(

feature.validation_derived_source.source_path)):

continue

all_match = True

for i in range(len(source_paths)):

if (source_paths[i] != types.FeaturePath.from_proto(

feature.validation_derived_source.source_path[i])):

all_match = False

break

if all_match:

results.append(FeatureView(feature))

if len(results) > 1:

raise ValueError('Ambiguous result, %d features matched' % len(results))

if len(results) == 1:

return results.pop()

"""View of a single feature.

This class provides accessor methods, as well as access to the underlying

proto. Where possible, accessors should be used in place of proto access (for

example, x.numeric_statistics() instead of x.proto().num_stats) in order to

support future extension of the proto.

"""

def __init__(self, stats_proto: statistics_pb2.FeatureNameStatistics):

self._statistics = stats_proto

def proto(self) -> statistics_pb2.FeatureNameStatistics:

"""Retrieve the underlying proto."""

return self._statistics

def custom_statistic(self,

name: str) -> Optional[statistics_pb2.CustomStatistic]:

"""Retrieve a custom_statistic by name."""

result = None

for stat in self._statistics.custom_stats:

if stat.name == name:

if result is None:

result = stat

else:

raise ValueError('Duplicate custom_stats for name %s' % name)

return result

# TODO(b/202910677): Add convenience methods for retrieving first-party custom

# statistics (e.g., MI, NLP).

def numeric_statistics(self) -> Optional[statistics_pb2.NumericStatistics]:

"""Retrieve numeric statistics if available."""

if self._statistics.WhichOneof('stats') == 'num_stats':

return self._statistics.num_stats

return None

def string_statistics(self) -> Optional[statistics_pb2.StringStatistics]:

"""Retrieve string statistics if available."""

if self._statistics.WhichOneof('stats') == 'string_stats':

return self._statistics.string_stats

return None

def bytes_statistics(self) -> Optional[statistics_pb2.BytesStatistics]:

"""Retrieve byte statistics if available."""

if self._statistics.WhichOneof('stats') == 'bytes_stats':

return self._statistics.bytes_stats

return None

def struct_statistics(self) -> Optional[statistics_pb2.StructStatistics]:

"""Retrieve struct statistics if available."""

if self._statistics.WhichOneof('stats') == 'struct_stats':

return self._statistics.struct_stats

return None

def common_statistics(self) -> Optional[statistics_pb2.CommonStatistics]:

"""Retrieve common statistics if available."""

which = self._statistics.WhichOneof('stats')

if which == 'num_stats':

return self._statistics.num_stats.common_stats

if which == 'string_stats':

return self._statistics.string_stats.common_stats

if which == 'bytes_stats':

return self._statistics.bytes_stats.common_stats

if which == 'struct_stats':

return self._statistics.struct_stats.common_stats

"""View of a single cross feature."""

def __init__(self, stats_proto: statistics_pb2.CrossFeatureStatistics):

self._statistics = stats_proto

def proto(self) -> statistics_pb2.CrossFeatureStatistics:

"""Retrieve the underlying proto."""

input_path_prefix: Optional[str] = None,

input_paths: Optional[Iterable[str]] = None,

io_provider: Optional[artifacts_io_impl.StatisticsIOProvider] = None

) -> DatasetListView:

"""Read a sharded DatasetFeatureStatisticsList from disk as a DatasetListView.

Args:

input_path_prefix: If passed, loads files starting with this prefix and

ending with a pattern corresponding to the output of the provided

io_provider.

input_paths: A list of file paths of files containing sharded

DatasetFeatureStatisticsList protos.

io_provider: Optional StatisticsIOProvider. If unset, a default will be

constructed.

Returns:

A DatasetListView containing the merged proto.

"""

if input_path_prefix is None == input_paths is None:

raise ValueError('Must provide one of input_paths_prefix, input_paths.')

if io_provider is None:

io_provider = artifacts_io_impl.get_io_provider()

if input_path_prefix is not None:

input_paths = io_provider.glob(input_path_prefix)

if not input_paths:

raise ValueError('No input paths found paths=%s, pattern=%s' %

(input_paths, input_path_prefix))

acc = statistics.DatasetListAccumulator()

stats_iter = io_provider.record_iterator_impl(input_paths)

for stats_list in stats_iter:

for dataset in stats_list.datasets:

acc.MergeDatasetFeatureStatistics(dataset.SerializeToString())

stats = statistics_pb2.DatasetFeatureStatisticsList()

stats.ParseFromString(acc.Get())