def __init__()

in tensorflow_data_validation/statistics/generators/basic_stats_generator.py [0:0]

• 18 lines of code
• 15 McCabe index (conditional complexity)

  def __init__(
      self, has_weights: bool,
      make_quantiles_sketch_fn: Callable[[], sketches.QuantilesSketch]):
    # The number of values for this feature that equal 0.
    self.num_zeros = 0
    # The number of NaN values for this feature. This is computed only for
    # FLOAT features.
    self.num_nan = 0
    # The minimum value among all the values for this feature.
    self.min = float('inf')
    # The maximum value among all the values for this feature.
    self.max = float('-inf')
    # The minimum value among all the finite values for this feature.
    self.finite_min = float('inf')
    # The maximum value among all the finite values for this feature.
    self.finite_max = float('-inf')
    # Summary of the quantiles for the values in this feature.
    self.quantiles_summary = make_quantiles_sketch_fn()

    self.has_weights = has_weights

    # Accumulator for mean and variance.
    self.mean_var_accumulator = variance_util.MeanVarAccumulator()
    # Keep track of partial weighted numeric stats.
    if has_weights:
      # Summary of the weighted quantiles for the values in this feature.
      self.weighted_quantiles_summary = make_quantiles_sketch_fn()
      # Accumulator for weighted mean and weighted variance.
      self.weighted_mean_var_accumulator = (
          variance_util.WeightedMeanVarAccumulator())
    else:
      self.weighted_mean_var_accumulator = None