def _make_bytes_stats_proto(
bytes_stats: _PartialBytesStats,
total_num_values: int) -> statistics_pb2.BytesStatistics:
"""Convert the partial bytes statistics into BytesStatistics proto."""
result = statistics_pb2.BytesStatistics()
if total_num_values > 0:
result.avg_num_bytes = bytes_stats.total_num_bytes / total_num_values
result.min_num_bytes = bytes_stats.min_num_bytes
result.max_num_bytes = bytes_stats.max_num_bytes
return result
def get_dataset_feature_statistics(builder, split):
"""Calculate statistics for the specified split."""
statistics = statistics_pb2.DatasetFeatureStatistics()
# Make this to the best of our abilities.
schema = schema_pb2.Schema()
dataset = builder.as_dataset(split=split)
# Just computing the number of examples for now.
statistics.num_examples = 0
# Feature dictionaries.
feature_to_shape = {}
feature_to_dtype = {}
feature_to_num_examples = collections.defaultdict(int)
feature_to_min = {}
feature_to_max = {}
for example in dataset:
statistics.num_examples += 1
assert isinstance(example, dict)
feature_names = example.keys()
for feature_name in feature_names:
# Update the number of examples this feature appears in.
feature_to_num_examples[feature_name] += 1
feature_shape = example[feature_name].shape
feature_dtype = example[feature_name].dtype
feature_np = example[feature_name].numpy()
feature_min, feature_max = None, None
is_numeric = (
feature_dtype.is_floating or feature_dtype.is_integer or
feature_dtype.is_bool)
if is_numeric:
feature_min = np.min(feature_np)
feature_max = np.max(feature_np)
# TODO(afrozm): What if shapes don't match? Populate ValueCount? Add
# logic for that.
# Set the shape, or assert shapes match.
if feature_name not in feature_to_shape:
feature_to_shape[feature_name] = feature_shape
else:
assert feature_to_shape[feature_name] == feature_shape
# Set the shape, or assert shapes match.
if feature_name not in feature_to_dtype:
feature_to_dtype[feature_name] = feature_dtype
else:
assert feature_to_dtype[feature_name] == feature_dtype
# Set or update the min, max.
if is_numeric:
if ((feature_name not in feature_to_min) or
(feature_to_min[feature_name] > feature_min)):
feature_to_min[feature_name] = feature_min
if ((feature_name not in feature_to_max) or
(feature_to_max[feature_name] < feature_max)):
feature_to_max[feature_name] = feature_max
# Start here, we've processed all examples.
# Assert that the keys match up.
assert feature_to_shape.keys() == feature_to_dtype.keys()
assert feature_to_shape.keys() == feature_to_num_examples.keys()
for feature_name in feature_to_shape:
# Try to fill in the schema.
feature = schema.feature.add()
feature.name = feature_name
# TODO(afrozm): What do we do for non fixed size shapes?
# What to do for scalars?
for dim in feature_to_shape[feature_name].as_list():
feature.shape.dim.add().size = dim
feature_type = feature_to_dtype[feature_name]
feature.type = _FEATURE_TYPE_MAP.get(feature_type, schema_pb2.BYTES)
common_statistics = statistics_pb2.CommonStatistics()
common_statistics.num_non_missing = feature_to_num_examples[feature_name]
common_statistics.num_missing = (
statistics.num_examples - common_statistics.num_non_missing)
feature_name_statistics = statistics.features.add()
feature_name_statistics.name = feature_name
# TODO(afrozm): This can be skipped, since type information was added to
# the Schema.
feature_name_statistics.type = _SCHEMA_TYPE_MAP.get(
feature.type, statistics_pb2.FeatureNameStatistics.BYTES)
if feature.type == schema_pb2.INT or feature.type == schema_pb2.FLOAT:
numeric_statistics = statistics_pb2.NumericStatistics()
numeric_statistics.min = feature_to_min[feature_name]
numeric_statistics.max = feature_to_max[feature_name]
numeric_statistics.common_stats.CopyFrom(common_statistics)
feature_name_statistics.num_stats.CopyFrom(numeric_statistics)
else:
# Let's shove it into BytesStatistics for now.
bytes_statistics = statistics_pb2.BytesStatistics()
bytes_statistics.common_stats.CopyFrom(common_statistics)
feature_name_statistics.bytes_stats.CopyFrom(bytes_statistics)
return statistics, schema
def get_dataset_feature_statistics(builder, split):
"""Calculate statistics for the specified split."""
statistics = statistics_pb2.DatasetFeatureStatistics()
# Make this to the best of our abilities.
schema = schema_pb2.Schema()
dataset = builder.as_dataset(split=split)
# Just computing the number of examples for now.
statistics.num_examples = 0
# Feature dictionaries.
feature_to_num_examples = collections.defaultdict(int)
feature_to_min = {}
feature_to_max = {}
np_dataset = dataset_utils.dataset_as_numpy(dataset)
for example in tqdm.tqdm(np_dataset, unit=" examples"):
statistics.num_examples += 1
assert isinstance(example, dict)
feature_names = sorted(example.keys())
for feature_name in feature_names:
# Update the number of examples this feature appears in.
feature_to_num_examples[feature_name] += 1
feature_np = example[feature_name]
# For compatibility in graph and eager mode, we can get PODs here and
# everything may not be neatly wrapped up in numpy's ndarray.
feature_dtype = type(feature_np)
if isinstance(feature_np, np.ndarray):
feature_dtype = feature_np.dtype.type
feature_min, feature_max = None, None
is_numeric = (np.issubdtype(feature_dtype, np.number)
or feature_dtype == np.bool_)
if is_numeric:
feature_min = np.min(feature_np)
feature_max = np.max(feature_np)
# TODO(afrozm): What if shapes don't match? Populate ValueCount? Add
# logic for that.
# Set or update the min, max.
if is_numeric:
if ((feature_name not in feature_to_min)
or (feature_to_min[feature_name] > feature_min)):
feature_to_min[feature_name] = feature_min
if ((feature_name not in feature_to_max)
or (feature_to_max[feature_name] < feature_max)):
feature_to_max[feature_name] = feature_max
# Start here, we've processed all examples.
output_shapes_dict = dataset.output_shapes
output_types_dict = dataset.output_types
for feature_name in sorted(feature_to_num_examples.keys()):
# Try to fill in the schema.
feature = schema.feature.add()
feature.name = feature_name
# TODO(afrozm): Make this work with nested structures, currently the Schema
# proto has no support for it.
maybe_feature_shape = output_shapes_dict[feature_name]
if not isinstance(maybe_feature_shape, tf.TensorShape):
logging.error(
"Statistics generation doesn't work for nested structures yet")
continue
for dim in maybe_feature_shape.as_list():
# We denote `None`s as -1 in the shape proto.
feature.shape.dim.add().size = dim if dim else -1
feature_type = output_types_dict[feature_name]
feature.type = _FEATURE_TYPE_MAP.get(feature_type, schema_pb2.BYTES)
common_statistics = statistics_pb2.CommonStatistics()
common_statistics.num_non_missing = feature_to_num_examples[
feature_name]
common_statistics.num_missing = (statistics.num_examples -
common_statistics.num_non_missing)
feature_name_statistics = statistics.features.add()
feature_name_statistics.name = feature_name
# TODO(afrozm): This can be skipped, since type information was added to
# the Schema.
feature_name_statistics.type = _SCHEMA_TYPE_MAP.get(
feature.type, statistics_pb2.FeatureNameStatistics.BYTES)
if feature.type == schema_pb2.INT or feature.type == schema_pb2.FLOAT:
numeric_statistics = statistics_pb2.NumericStatistics()
numeric_statistics.min = feature_to_min[feature_name]
numeric_statistics.max = feature_to_max[feature_name]
numeric_statistics.common_stats.CopyFrom(common_statistics)
feature_name_statistics.num_stats.CopyFrom(numeric_statistics)
else:
# Let's shove it into BytesStatistics for now.
bytes_statistics = statistics_pb2.BytesStatistics()
bytes_statistics.common_stats.CopyFrom(common_statistics)
feature_name_statistics.bytes_stats.CopyFrom(bytes_statistics)
return statistics, schema
