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pycalibration

Estimation and hypothesis tests of calibration in Python using CalibrationErrors.jl and CalibrationTests.jl.

Stable Dev Status codecov CalibrationErrors.jl Status CalibrationTests.jl Status

pycalibration is a package for estimating calibration of probabilistic models in Python. It is a Python interface for CalibrationErrors.jl and CalibrationTests.jl. As such, the package allows the estimation of calibration errors (ECE and SKCE) and statistical testing of the null hypothesis that a model is calibrated.

Installation

To install pycalibration, run

python -m pip install git+https://github.com/devmotion/pycalibration.git

The use of pycalibration requires that its dependency pyjulia (installed automatically) and itself are configured correctly.

For pyjulia, you have to install Julia (at least version 1.6 is required) and the Julia dependencies of pyjulia. The configuration process is described in detail in the pyjulia documentation.

When pyjulia is configured correctly, you can install the Julia packages required by pycalibration in the Python interpreter:

>>> import pycalibration
>>> pycalibration.install()

Custom Julia environment

With the default settings, pyjulia and pycalibration install all Julia dependencies in the default environment. In particular, if you use Julia for other projects as well, a separate project environment can simplify package management and ensure that the state of the Julia dependencies is reproducible. In pyjulia and pycalibration, a custom project environment is used if you set the environment variable JULIA_PROJECT:

export JULIA_PROJECT="path/to/the/environment/"

Usage

Import and setup calibration analysis tools from CalibrationErrors.jl and CalibrationTests.jl with

>>> from pycalibration import ca

You can then do the same as would be done in Julia, except you have to add ca. in front for functionality from the Julia packages. Most of the commands will work without any modification. Thus the documentation of the Julia packages is the main in-depth documentation for this package.

Valid identifiers

Not all valid Julia identifiers are valid Python identifiers. This is an inherent limitation of Python and pyjulia. In particular, it is a common idiom in Julia to append ! to functions that mutate their arguments but it is not possible to use ! in function names in Python. pyjulia renames these functions by substituting ! with _b, e.g., you can call the Julia function copy! with copy_b in Python.

Calibration errors

Let us estimate the squared kernel calibration error (SKCE) with the tensor product kernel

$$k((p, y), (p̃, ỹ)) = exp(-|p - p̃|) δ(y - ỹ)$$

from a set of predictions and corresponding observed outcomes.

>>> skce = ca.SKCE(ca.tensor(ca.ExponentialKernel(), ca.WhiteKernel()))

Other estimators of the SKCE and estimators of other calibration errors such as the expected calibration error (ECE) are available as well. The Julia package KernelFunctions.jl supports a variety of kernels, all compositions and transformations of kernels available there can be used.

Sequences of probabilities

Predictions can be provided as sequences of probabilities. In this case, the predictions correspond to Bernoulli distributions with these parameters and the targets are boolean values.

>>> import random
>>> random.seed(1234)
>>> predictions = [random.random() for _ in range(100)]
>>> outcomes = [bool(random.getrandbits(1)) for _ in range(100)]
>>> skce(predictions, outcomes)
0.028399084017414655

NumPy arrays are supported as well.

>>> import numpy as np
>>> rng = np.random.default_rng(1234)
>>> predictions = rng.random(100)
>>> outcomes = rng.choice([True, False], 100)
>>> skce(predictions, outcomes)
0.03320398246523166

Sequences of probability vectors

Predictions can be provided as sequences of probability vectors (i.e., vectors in the probability simplex) as well. In this case, the predictions correspond to categorical distributions with these class probabilities and the targets are integers in {1,...,n}.

>>> import numpy as np
>>> rng = np.random.default_rng(1234)
>>> predictions = [rng.dirichlet((3, 2, 5)) for _ in range(100)]
>>> outcomes = rng.integers(low=1, high=4, size=100)
>>> skce(predictions, outcomes)
0.02015240706950358

Sequences of probability vectors can also be provided as NumPy matrices. However, it is required to specify if the probability vectors correspond to rows or columns of the matrix by wrapping them in ca.RowVecs and ca.ColVecs, respectively. These wrappers are defined in KernelFunctions.jl.

>>> import numpy as np
>>> rng = np.random.default_rng(1234)
>>> predictions = rng.dirichlet((3, 2, 5), 100)
>>> outcomes = rng.integers(low=1, high=4, size=100)
>>> skce(ca.RowVecs(predictions), outcomes)
0.02015240706950358

The wrappers have to be used also for, e.g., lists of lists since pyjulia converts them to matrices automatically.

>>> predictions = [[0.1, 0.8, 0.1], [0.2, 0.5, 0.3]]
>>> outcomes = [2, 3]
>>> skce(ca.RowVecs(predictions), outcomes)
-0.10317943453412069

Sequences of probability distributions

Predictions can also be provided as sequences of probability distributions defined in the Julia package Distributions.jl. Currently, analytical formulas for the estimators of the SKCE and unnormalized calibration mean embedding (UCME) are implemented for uni- and multivariate normal distributions ca.Normal and ca.MvNormal with squared exponential kernels on the target space and Laplace distributions ca.Laplace with exponential kernels on the target spaca.

In this example we use the tensor product kernel

$$k((p, y), (p̃, ỹ)) = exp(-W₂(p, p̃)) exp(-(y - ỹ)²/2),$$

where W₂(p, p̃) is the 2-Wasserstein distance of the two normal distributions p and p̃. It is given by

$$W₂(p, p̃) = √((μ - μ̃)² + (σ - σ̃)²),$$

where p = N(μ, σ) and p̃ = N(μ̃, σ̃).

>>> import random
>>> random.seed(1234)
>>> predictions = [ca.Normal(random.gauss(0, 1), random.random()) for _ in range(100)]
>>> outcomes = [random.gauss(0, 1) for _ in range(100)]
>>> skce = ca.SKCE(ca.tensor(ca.ExponentialKernel(metric=ca.Wasserstein()), ca.SqExponentialKernel()))
>>> skce(predictions, outcomes)
0.02203618235964146

Calibration tests

pycalibration provides different calibration tests that estimate the p-value of the null hypothesis that a model is calibrated, based on a set of predictions and outcomes:

  • ca.ConsistencyTest estimates the p-value with consistency resampling for a given calibration error estimator
  • ca.DistributionFreeSKCETest computes distribution-free (and therefore usually quite weak) upper bounds of the p-value for different estimators of the SKCE
  • ca.AsymptoticBlockSKCETest estimates the p-value based on the asymptotic distribution of the unbiased block estimator of the SKCE
  • ca.AsymptoticSKCETest estimates the p-value based on the asymptotic distribution of the unbiased estimator of the SKCE
  • ca.AsymptoticCMETest estimates the p-value based on the asymptotic distribution of the UCME
>>> import numpy as np
>>> rng = np.random.default_rng(1234)
>>> predictions = rng.dirichlet((3, 2, 5), 100)
>>> outcomes = rng.integers(low=1, high=4, size=100)
>>> kernel = ca.tensor(ca.ExponentialKernel(metric=ca.TotalVariation()), ca.WhiteKernel())
>>> test = ca.AsymptoticSKCETest(kernel, predictions, outcomes)
>>> print(test)
<PyCall.jlwrap Asymptotic SKCE test
--------------------
Population details:
    parameter of interest:   SKCE
    value under h_0:         0.0
    point estimate:          6.07887e-5

Test summary:
    outcome with 95% confidence: fail to reject h_0
    one-sided p-value:           0.4330

Details:
    test statistic: -4.955380469272125
>>> ca.pvalue(test)
0.435

Citing

If you use pycalibration as part of your research, teaching, or other activities, please consider citing the following publications:

Widmann, D., Lindsten, F., & Zachariah, D. (2019). Calibration tests in multi-class classification: A unifying framework. In Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (pp. 12257–12267).

Widmann, D., Lindsten, F., & Zachariah, D. (2021). Calibration tests beyond classification. International Conference on Learning Representations (ICLR 2021).

Acknowledgements

This work was financially supported by the Swedish Research Council via the projects Learning of Large-Scale Probabilistic Dynamical Models (contract number: 2016-04278), Counterfactual Prediction Methods for Heterogeneous Populations (contract number: 2018-05040), and Handling Uncertainty in Machine Learning Systems (contract number: 2020-04122), by the Swedish Foundation for Strategic Research via the project Probabilistic Modeling and Inference for Machine Learning (contract number: ICA16-0015), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by ELLIIT.