Options for generating statistics.

Used in the notebooks

Used in the tutorials

generators An optional list of statistics generators. A statistics generator must extend either CombinerStatsGenerator or TransformStatsGenerator.
schema An optional tensorflow_metadata Schema proto. Currently we use the schema to infer categorical and bytes features.
label_feature An optional feature name which represents the label.
weight_feature An optional feature name whose numeric value represents the weight of an example.
slice_functions DEPRECATED. Use experimental_slice_functions.
sample_rate An optional sampling rate. If specified, statistics is computed over the sample.
num_top_values An optional number of most frequent feature values to keep for string features.
frequency_threshold An optional minimum number of examples the most frequent values must be present in.
weighted_frequency_threshold An optional minimum weighted number of examples the most frequent weighted values must be present in. This option is only relevant when a weight_feature is specified.
num_rank_histogram_buckets An optional number of buckets in the rank histogram for string features.
num_values_histogram_buckets An optional number of buckets in a quantiles histogram for the number of values per Feature, which is stored in CommonStatistics.num_values_histogram.
num_histogram_buckets An optional number of buckets in a standard NumericStatistics.histogram with equal-width buckets.
num_quantiles_histogram_buckets An optional number of buckets in a quantiles NumericStatistics.histogram.
epsilon An optional error tolerance for the computation of quantiles, typically a small fraction close to zero (e.g. 0.01). Higher values of epsilon increase the quantile approximation, and hence result in more unequal buckets, but could improve performance, and resource consumption.
infer_type_from_schema A boolean to indicate whether the feature types should be inferred from the schema. If set to True, an input schema must be provided. This flag is used only when invoking TFDV through tfdv.generate_statistics_from_csv.
desired_batch_size An optional maximum number of examples to include in each batch that is passed to the statistics generators. When invoking TFDV using its end-to-end APIs (e.g. generate_statistics_from_tfrecord), this option also controls the decoder batch size -- if provided, the decoded RecordBatches that are to be fed to TFDV will have the fixed batch size. When invoking TFDV using tfdv.GenerateStatistics, this option only controls the maximum size of RecordBatches constructed within StatsGenerators (a generator may combine RecordBatches).
enable_semantic_domain_stats If True statistics for semantic domains are generated (e.g: image, text domains).
semantic_domain_stats_sample_rate An optional sampling rate for semantic domain statistics. If specified, semantic domain statistics is computed over a sample.
per_feature_weight_override If specified, the "example weight" paired with a feature will be first looked up in this map and if not found, fall back to weight_feature.
vocab_paths An optional dictionary mapping vocab names to paths. Used in the schema when specifying a NaturalLanguageDomain. The paths can either be to GZIP-compressed TF record files that have a tfrecord.gz suffix or to text files.
add_default_generators Whether to invoke the default set of stats generators in the run. Generators invoked consists of 1) the default generators (controlled by this option); 2) user-provided generators ( controlled by the generators option); 3) semantic generators (controlled by enable_semantic_domain_stats) and 4) schema-based generators that are enabled based on information provided in the schema.
feature_allowlist An optional list of names of the features to calculate statistics for, or a list of paths.
experimental_use_sketch_based_topk_uniques Deprecated, prefer use_sketch_based_topk_uniques.
use_sketch_based_topk_uniques if True, use the sketch based top-k and uniques stats generator.
experimental_slice_functions An optional list of functions that generate slice keys for each example. Each slice function should take pyarrow.RecordBatch as input and return an Iterable[Tuple[Text, pyarrow.RecordBatch]]. Each tuple contains the slice key and the corresponding sliced RecordBatch. Only one of experimental_slice_functions or experimental_slice_sqls must be specified.
experimental_slice_sqls List of slicing SQL queries. The query must have the following pattern: "SELECT STRUCT({feature_name} [AS {slice_key}]) [FROM example.feature_name [, example.feature_name, ... ] [WHERE ... ]]" The “example.feature_name” inside the FROM statement is used to flatten the repeated fields. For non-repeated fields, you can directly write the query as follows: “SELECT STRUCT(non_repeated_feature_a, non_repeated_feature_b)” In the query, the “example” is a key word that binds to each input "row". The semantics of this variable will depend on the decoding of the input data to the Arrow representation (e.g., for tf.Example, each key is decoded to a separate column). Thus, structured data can be readily accessed by iterating/unnesting the fields of the "example" variable. Example 1: Slice on each value of a feature "SELECT STRUCT(gender) FROM example.gender" Example 2: Slice on each value of one feature and a specified value of another. "SELECT STRUCT(gender, country) FROM example.gender, WHERE country = 'USA'" Only one of experimental_slice_functions or experimental_slice_sqls must be specified.
experimental_result_partitions The number of feature partitions to combine output DatasetFeatureStatisticsLists into. If set to 1 (default) output is globally combined. If set to value greater than one, up to that many shards are returned, each containing a subset of features.
experimental_num_feature_partitions If > 1, partitions computations by supported generators to act on this many bundles of features. For best results this should be set to at least several times less than the number of features in a dataset, and never more than the available beam parallelism.
slicing_config an optional SlicingConfig. SlicingConfig includes slicing_specs specified with feature keys, feature values or slicing SQL queries.
experimental_filter_read_paths If provided, tries to push down either paths passed via feature_allowlist or via the schema (in that priority) to the underlying read operation. Support depends on the file reader.
per_feature_stats_config Supports granular control of what statistics are enabled per feature. Experimental.
























View source

Construct an instance of stats options from a JSON representation.

options_json A JSON representation of the dict attribute of a StatsOptions instance.

A StatsOptions instance constructed by setting the dict attribute to the deserialized value of options_json.


View source

Convert from an object to JSON representation of the dict attribute.

Custom generators and slice_functions cannot being converted. As a result, a ValueError will be raised when these options are specified and TFDV is running in a setting where the stats options have been json-serialized, first. This will happen in the case where TFDV is run as a TFX component. The schema proto and slicing_config will be json_encoded.

A JSON representation of a filtered version of dict.