class_docstr: Options for generating statistics.
class_docstr_tok: ["Options","for","generating","statistics","."]
docstr: Initializes statistics options.
  
  Args:
    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,
        example.country WHERE country = 'USA'" Only one of
        experimental_slice_functions or experimental_slice_sqls must be
        specified. Note that this option is not supported on Windows.
    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.
docstr_tok: 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code: def __init__(
      self,
      generators: Optional[List[stats_generator.StatsGenerator]] = None,
      schema: Optional[schema_pb2.Schema] = None,
      label_feature: Optional[types.FeatureName] = None,
      weight_feature: Optional[types.FeatureName] = None,
      slice_functions: Optional[List[types.SliceFunction]] = None,
      sample_rate: Optional[float] = None,
      num_top_values: int = 20,
      frequency_threshold: int = 1,
      weighted_frequency_threshold: float = 1.0,
      num_rank_histogram_buckets: int = 1000,
      num_values_histogram_buckets: int = 10,
      num_histogram_buckets: int = 10,
      num_quantiles_histogram_buckets: int = 10,
      epsilon: float = 0.01,
      infer_type_from_schema: bool = False,
      desired_batch_size: Optional[int] = None,
      enable_semantic_domain_stats: bool = False,
      semantic_domain_stats_sample_rate: Optional[float] = None,
      per_feature_weight_override: Optional[
          Dict[types.FeaturePath, types.FeatureName]
      ] = None,
      vocab_paths: Optional[
          Dict[types.VocabName, types.VocabPath]
      ] = None,
      add_default_generators: bool = True,
      # TODO(b/255895499): Support "from schema" for feature_allowlist.
      feature_allowlist: Optional[
          Union[List[types.FeatureName], List[types.FeaturePath]]
      ] = None,
      experimental_use_sketch_based_topk_uniques: Optional[bool] = None,
      use_sketch_based_topk_uniques: Optional[bool] = None,
      experimental_slice_functions: Optional[
          List[types.SliceFunction]
      ] = None,
      experimental_slice_sqls: Optional[List[Text]] = None,
      experimental_result_partitions: int = 1,
      experimental_num_feature_partitions: int = 1,
      slicing_config: Optional[slicing_spec_pb2.SlicingConfig] = None,
      experimental_filter_read_paths: bool = False,
  ):
      """Initializes statistics options.
  
      Args:
        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,
            example.country WHERE country = 'USA'" Only one of
            experimental_slice_functions or experimental_slice_sqls must be
            specified. Note that this option is not supported on Windows.
        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.
      """
      self.generators = generators
      self.feature_allowlist = feature_allowlist
      self.schema = schema
      self.label_feature = label_feature
      self.weight_feature = weight_feature
      if (
          slice_functions is not None
          and experimental_slice_functions is not None
      ):
          raise ValueError(
              "Specify only one of slice_functions or experimental_slice_functions"
          )
      self.experimental_slice_functions = None
      if slice_functions is not None:
          self.experimental_slice_functions = slice_functions
      elif experimental_slice_functions is not None:
          self.experimental_slice_functions = (
              experimental_slice_functions
          )
      self.sample_rate = sample_rate
      self.num_top_values = num_top_values
      self.frequency_threshold = frequency_threshold
      self.weighted_frequency_threshold = weighted_frequency_threshold
      self.num_rank_histogram_buckets = num_rank_histogram_buckets
      self.num_values_histogram_buckets = num_values_histogram_buckets
      self.num_histogram_buckets = num_histogram_buckets
      self.num_quantiles_histogram_buckets = (
          num_quantiles_histogram_buckets
      )
      self.epsilon = epsilon
      self.infer_type_from_schema = infer_type_from_schema
      self.desired_batch_size = desired_batch_size
      self.enable_semantic_domain_stats = enable_semantic_domain_stats
      self.semantic_domain_stats_sample_rate = (
          semantic_domain_stats_sample_rate
      )
      self._per_feature_weight_override = per_feature_weight_override
      self.vocab_paths = vocab_paths
      self.add_default_generators = add_default_generators
      if (
          use_sketch_based_topk_uniques is not None
          and experimental_use_sketch_based_topk_uniques is not None
      ):
          raise ValueError(
              "Must set at most one of use_sketch_based_topk_uniques and"
              " experimental_use_sketch_based_topk_uniques"
          )
      # TODO(b/239609486): Change the None default to True.
      if (
          experimental_use_sketch_based_topk_uniques
          or use_sketch_based_topk_uniques
      ):
          self.use_sketch_based_topk_uniques = True
      else:
          self.use_sketch_based_topk_uniques = False
      self.experimental_slice_sqls = experimental_slice_sqls
      self.experimental_num_feature_partitions = (
          experimental_num_feature_partitions
      )
      self.experimental_result_partitions = (
          experimental_result_partitions
      )
      self.slicing_config = slicing_config
      self.experimental_filter_read_paths = (
          experimental_filter_read_paths
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import json
  import logging
  import types
  from typing import Dict, List, Optional, Text, Union
  from tensorflow_data_validation import types
  from tensorflow_data_validation.statistics.generators import stats_generator
  from tensorflow_data_validation.utils import example_weight_map
  from tensorflow_data_validation.utils import schema_util
  from tensorflow_data_validation.utils import slicing_util
  from tfx_bsl.arrow import sql_util
  from tfx_bsl.coders import example_coder
  from tfx_bsl.public.proto import slicing_spec_pb2
  from google.protobuf import json_format
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 1 to create a new StatsOptions object from inherited base classes: object with example: obj = StatsOptions(generators, schema, label_feature, weight_feature, slice_functions, sample_rate, num_top_values, frequency_threshold, weighted_frequency_threshold, num_rank_histogram_buckets, num_values_histogram_buckets, num_histogram_buckets, num_quantiles_histogram_buckets, epsilon, infer_type_from_schema, desired_batch_size, enable_semantic_domain_stats, semantic_domain_stats_sample_rate, per_feature_weight_override, vocab_paths, add_default_generators, feature_allowlist, experimental_use_sketch_based_topk_uniques, use_sketch_based_topk_uniques, experimental_slice_functions, experimental_slice_sqls, experimental_result_partitions, experimental_num_feature_partitions, slicing_config, experimental_filter_read_paths)