class_docstr: Base class for generator of stats proto from TF data.
class_docstr_tok: ["Base","class","for","generator","of","stats","proto","from","TF","data","."]
docstr: Creates a feature statistics proto from a set of TFRecord files.
  
  Args:
    files: A list of dicts describing files for each dataset for the proto.
      Each
        entry contains a 'path' field with the path to the TFRecord file on
          disk
        and a 'name' field to identify the dataset in the proto.
    max_entries: The maximum number of examples to load from each dataset
        in order to create the proto. Defaults to 10000.
    features: A list of strings that is a whitelist of feature names to create
        feature statistics for. If set to None then all features in the
          dataset
        are analyzed. Defaults to None.
    is_sequence: True if the input data from 'tables' are tf.SequenceExamples,
        False if tf.Examples. Defaults to false.
    iterator_options: Options to pass to the iterator that reads the examples.
        Defaults to None.
    histogram_categorical_levels_count: int, controls the maximum number of
        levels to display in histograms for categorical features.
        Useful to prevent codes/IDs features from bloating the stats object.
        Defaults to None.
  
  Returns:
    The feature statistics proto for the provided files.
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code: def ProtoFromTfRecordFiles(
      self,
      files,
      max_entries=10000,
      features=None,
      is_sequence=False,
      iterator_options=None,
      histogram_categorical_levels_count=None,
  ):
      """Creates a feature statistics proto from a set of TFRecord files.
  
      Args:
        files: A list of dicts describing files for each dataset for the proto.
          Each
            entry contains a 'path' field with the path to the TFRecord file on
              disk
            and a 'name' field to identify the dataset in the proto.
        max_entries: The maximum number of examples to load from each dataset
            in order to create the proto. Defaults to 10000.
        features: A list of strings that is a whitelist of feature names to create
            feature statistics for. If set to None then all features in the
              dataset
            are analyzed. Defaults to None.
        is_sequence: True if the input data from 'tables' are tf.SequenceExamples,
            False if tf.Examples. Defaults to false.
        iterator_options: Options to pass to the iterator that reads the examples.
            Defaults to None.
        histogram_categorical_levels_count: int, controls the maximum number of
            levels to display in histograms for categorical features.
            Useful to prevent codes/IDs features from bloating the stats object.
            Defaults to None.
  
      Returns:
        The feature statistics proto for the provided files.
      """
      warnings.warn(
          "Code in this directory is deprecated. Use the facets-overview pip package instead.",
          DeprecationWarning,
      )
      datasets = []
      for entry in files:
          entries, size = self._GetTfRecordEntries(
              entry["path"], max_entries, is_sequence, iterator_options
          )
          datasets.append(
              {"entries": entries, "size": size, "name": entry["name"]}
          )
      return self.GetDatasetsProto(
          datasets, features, histogram_categorical_levels_count
      )
  
file_path: facets/facets_overview/python/base_feature_statistics_generator.py
imports: from functools import partial
  import warnings
  from base_generic_feature_statistics_generator import BaseGenericFeatureStatisticsGenerator
  import tensorflow
label_desc: Use image node_id 2 for calling the BaseFeatureStatisticsGenerator obj's underlying member method code with example usage: obj.ProtoFromTfRecordFiles(files, max_entries, features, is_sequence, iterator_options, histogram_categorical_levels_count) and returns: self