"""
Measures of centrality (e.g. mean) used to combine predictions across an
ensemble. The input to these functions are log affinities, and they are expected
to return a centrality measure also in log-space.
"""
import numpy
from functools import partial
[docs]def robust_mean(log_values):
"""
Mean of values falling within the 25-75 percentiles.
Parameters
----------
log_values : 2-d numpy.array
Center is computed along the second axis (i.e. per row).
Returns
-------
center : numpy.array of length log_values.shape[1]
"""
if log_values.shape[1] <= 3:
# Too few values to use robust mean.
return numpy.nanmean(log_values, axis=1)
without_nans = numpy.nan_to_num(log_values) # replace nan with 0
mask = (
(~numpy.isnan(log_values)) &
(without_nans <= numpy.nanpercentile(log_values, 75, axis=1).reshape((-1, 1))) &
(without_nans >= numpy.nanpercentile(log_values, 25, axis=1).reshape((-1, 1))))
return (without_nans * mask.astype(float)).sum(1) / mask.sum(1)
CENTRALITY_MEASURES = {
"mean": partial(numpy.nanmean, axis=1),
"median": partial(numpy.nanmedian, axis=1),
"robust_mean": robust_mean,
}