mmu 0.2.0rc1

Fast binary classification metrics with uncertainty quantification

A library for the evaluation of model performance and estimation of the uncertainty on these metrics.

Documentation

This package was developed as part of the paper Pointwise sampling uncertainties on the Precision-Recall curve. If you use this package as part of your research please cite using the CITATION file.

Functionality

On a high level MMU provides two types of functionality:

• Metrics - functions to compute confusion matrix(ces) and binary classification metrics over classifier scores or predictions.

• Uncertainty estimators - functionality to compute the joint uncertainty over classification metrics.

We currently focus on binary classification models but aim to include support for other types of models and their metrics in the future.

Confusion Matrix & Metrics

Metrics consist mainly of high-performance functions to compute the confusion matrix and metrics over a single test set, multiple classification thresholds and or multiple runs.

The binary_metrics functions compute the 10 most commonly used metrics:

• Negative precision aka Negative Predictive Value (NPV)

• Positive recision aka Positive Predictive Value (PPV)

• Negative recall aka True Negative Rate (TNR) aka Specificity

• Positive recall aka True Positive Rate (TPR) aka Sensitivity

• Negative f1 score

• Positive f1 score

• False Positive Rate (FPR)

• False Negative Rate (FNR)

• Accuracy

• Mathew’s Correlation Coefficient (MCC)

Uncertainty estimators

MMU provides two methods for modelling the joint uncertainty on precision and recall: Multinomial uncertainty and Bivariate-Normal.

The Multinomial approach estimates the uncertainty by computing the profile log-likelihoods scores for a grid around the precision and recall. The scores are chi2 distributed with 2 degrees of freedom which can be used to determine the confidence interval.

The Bivariate-Normal approach models the statistical uncertainty over the linearly propagated errors of the confusion matrix and the analytical covariance matrix. The resulting joint uncertainty is elliptical in nature.

Installation

mmu can be installed from PyPi.

pip install mmu

We provide wheels for:

• MacOS [x86, ARM]

• Linux

• Windows

Installing the package from source requires a C++ compiler with support for C++14. If you have a compiler available it is advised to install without the wheel as this enables architecture specific optimisations.

pip install mmu --no-binary mmu

Other build options exist, see the Installation section of the docs.

Usage

import mmu

# Create some example data
scores, yhat, y = mmu.generate_data(n_samples=1000)

# Compute the joint uncertainty on precision-recall curve
pr_err = mmu.PrecisionRecallCurveUncertainty.from_scores(y, scores)

# Plot the uncertainty
pr_err.plot()

See Basics section of the docs or the tutorial notebooks for more examples.

Contributing

We very much welcome contributions, please see the contributing section for details.