relplot 1.0.3

Compute and plot reliability diagrams based on calibration distance.

relplot: Principled Reliability Diagrams

relplot is a Python package for plotting reliability diagrams and measuring calibration error, in a theoretically-principled way. The package generates reliability diagrams as shown on the right:

How to Read the Diagram

• The input data is a set of observations: pairs of predicted probability and true outcomes $(f_i, y_i) \in [0, 1] \times {0, 1}$. For example, $f_i$ may be the forecasted "chance of rain" on day $i$, and $y_i$ the indicator of whether it rained or not on day $i$.

• The x-axis shows the predicted probabilities, and the y-axis shows an estimate of the true probability, conditioned on the predicted probability. Formally, this is a regression of outcomes $y$ on predictions $f$.

• The tick marks show the raw data: namely, the predicted probabilities for up to 100 datapoints, plotted above or below the x-axis according to whether the true outcome was 1 or 0. The thickness of the red regression curve represents the smoothed density of these tick marks, while the height of the curve represents the smoothed fraction whose true outcome is 1.

• The SmoothECE (smECE) is a measure of mis-calibration: it is essentially the average absolute difference between the red regression curve and the diagonal, averaged over x-coordinates that are distributed as the tick marks are (i.e. integrated over the density of predictions). See the paper for full details of the estimator and its properties.

• The smECE is reported with $\pm$ denoting 95% confidence intervals, estimated via bootstrapping. The gray band similarly shows 95% bootstrapped confidence bands around the regression line.

Formally, the reliability diagram is obtained by kernel smoothing with a careful choice of parameters. The choice of smoothing bandwidth (akin to "bin width") is cruicial, but is done automatically by the code in a theorhetically-justified way.

This package is based on the theoretical results in the paper Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing (ICLR 2024).

Installation

Install with Pip:

> pip install relplot

Or, clone the repo and install with:

> cd relplot
> pip install .

Getting Started

Basic usage:

import relplot as rp

# ...
# f: array of probabilities [f_i]
# y: array of binary labels [y_i]

calib_error = rp.smECE(f, y)   # compute calibration error (scalar)
fig, ax = rp.rel_diagram(f, y) # plot

See a quick demo in notebooks/demo.ipynb.

For more control, one can compute the calibration data with relplot.prepare_rel_diagram, and then plot it later with relplot.plot_rel_diagram. For example:

...
diagram = rp.prepare_rel_diagram(f, y) # compute calibration data (dictionary)
print('calibration error:', diagram['ce']) 
plt.plot(diagram['mesh'], diagram['mu']) # plot the calibration curve manually
fig, ax = rp.plot_rel_diagram(diagram) # plot the diagram in a new figure

The smoothed regression function itself is returned as diagram['mu'], which specifies values on the grid of x-coordinates in diagram['mesh']. This can be used for manual re-calibration.

Manual Bandwidth

To measure SmoothECE with a manual choice of bandwidth (rather than automatic choice), use:

calib_error = relplot.smECE_sigma(f, y, sigma=0.05)

Using a manual bandwidth can sometimes be desirable for interpretability: smECE_sigma behaves similarly to binnedECE with bin_width=sigma, at the cost of slightly weaker theoretical guarantees.

Data Format

Methods expect inputs in the form of a 1D array of predicted probabilities (f) and a 1D array of binary labels (y), where $f_i \in [0, 1]$ and $y_i \in {0, 1}$. We then consider the calibration of the distribution $(f_i, y_i)$ of prediction-outcome pairs. This package primarily considers the binary outcome setting, but can be used to measure multi-class confidence calibration as shown below.

Multi-class Confidence Calibration

In the multi-class setting, confidence calibration can be measured by expressing it as the binary calibration of the distribution on (confidence, accuracy) pairs. A convenience function for this common use case is provided:

# f: [N, C] array of logits over C classes
# y: [N, 1] array of predicted classes 
conf, acc = relplot.multiclass_logits_to_confidences(f, y) # reduce to binary setting
relplot.rel_diagram(f=conf, y=acc) # plot confidence calibration diagram
relplot.smECE(f=conf, y=acc) # compute smECE of confidence calibration

Customization and Usage Tips

The plot made by relplot.rel_diagram can be customized in various ways, as shown below. See this notebook for examples of more options: notebooks/figure1.ipynb

• For small datasets, you may want to disable bootstrapping (which subsamples the data). Pass the parameter plot_confidence_band=False.
• To override the automatic choice of kernel bandwidth for the diagram, set the parameter kde_bandwidth.

Additional Notebooks and Features

• The header image (Figure 1 of the paper) is generated in notebooks/figure1.ipynb
• The experiments in the paper are reproduced in notebooks/paper_experiments.ipynb
• relplot.metrics contains implementations of various alternate calibration measures, including binnedECE and laplace kernel calibration. This is in addition to the recommended calibration measure of smoothECE (relplot.smECE).
• relplot.rel_diagram_binned plots the "binned" reliability diagram. Not recommended for usage; included for comparison.
• relplot.config.use_tex_fonts can be set to True if you have $\LaTeX$ installed.

Citation

If you use relplot in your work, please consider citing:

@inproceedings{blasiok2024smooth,
      title={Smooth {ECE}: Principled Reliability Diagrams via Kernel Smoothing},
      author={B{\l}asiok, Jaros{\l}aw and Nakkiran, Preetum},
      booktitle={The Twelfth International Conference on Learning Representations},
      year={2024},
      url={https://openreview.net/forum?id=XwiA1nDahv}
}

Acknowledgements

We thank Jason Eisner and Adam Goliński for helpful suggestions on the package and documentation.