synthesizer.utils.stats

Functions

synthesizer.utils.stats.binned_weighted_quantile(x, y, weights, bins, quantiles)[source]
synthesizer.utils.stats.n_weighted_moment(values, weights, n)[source]
synthesizer.utils.stats.weighted_mean(data, weights)[source]

Calculate the weighted mean

Parameters:

  • data (list or numpy.array) – data

  • weights (list or numpy.array) – weights

Author: Stephen Wilkins

synthesizer.utils.stats.weighted_median(data, weights)[source]
Parameters:

  • data (list or numpy.array) – data

  • weights (list or numpy.array) – weights

synthesizer.utils.stats.weighted_quantile(values, quantiles, sample_weight=None, values_sorted=False, old_style=False)[source]

Taken from From https://stackoverflow.com/a/29677616/1718096

Very close to numpy.percentile, but supports weights. NOTE: quantiles should be in [0, 1]!

Parameters:

  • values (numpy.array) – values to weight

  • quantiles (array-like) – array of quantiles needed

  • sample_weight (array-like) – same length as array

  • values_sorted (bool) – f True, then will avoid sorting of initial array

  • old_style (bool) – If True, will correct output to be consistent with numpy.percentile.

Returns:

numpy.array with computed quantiles.