Consistency resampling of calibrated predictions.
Author devmotion
2 Stars
Updated Last
12 Months Ago
Started In
May 2019


Consistency resampling of calibrated predictions.

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This package implements consistency resampling in the Julia language, as described by Bröcker and Smith (2007). It is based on the Bootstrap.jl package for statistical bootstrapping in Julia.

Consistency resampling is a resampling technique that generates calibrated predictions from a data set of predictions and corresponding labels. First a set of predictions is sampled from the data set with replacement. In a second step artificial labels are sampled with the predicted probabilities. This resampling procedure ensures that the predictions are calibrated for the artificial labels.


The predictions have to be provided as a matrix of size (m, n), in which each of the n columns corresponds to predicted probabilities of the labels 1,…,m. The corresponding labels have to be provided as a vector of length n, in which every element is from the set 1,…,m.

using Distributions

predictions = rand(Dirichlet(10, 1), 500)
labels = rand(1:10, 500)

Consistency resampling is performed similar to the other bootstrapping approaches in Bootstrap.jl. A random number generator can be provided as optional argument.

using ConsistencyResampling
using Distances
using Flux: onehotbatch

b = bootstrap((predictions, labels), ConsistentSampling(100_000)) do (x, y)
  totalvariation(x, onehotbatch(y, 1:10)) / 500

The bootstrapped samples can be explored and used for estimation of confidence intervals, as explained in the documentation of Bootstrap.jl.


Bröcker, J. and Smith, L.A., 2007. Increasing the reliability of reliability diagrams. Weather and forecasting, 22(3), pp. 651-661.

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