Confidence Intervals for Error Rates in :jigsaw: Matching Tasks

This repository hosts the cimat (Confidence Intervals for MAtching Tasks) package, designed to create confidence intervals for performance metrics in 1:1 matching tasks like face and speaker verification.

With cimat, you can generate confidence intervals ($C_{\alpha}$) with a confidence level of $1-\alpha$ for metrics ($\theta^*$) such as:

• False Positive Rate (FPR, aka FMR or FAR) and False Negative Rate (FNR, aka FNMR or FRR) estimates
• ROC coordinate estimates such as FNR@FPR (aka FNMR@FMR or FRR@FAR)

such that $\mathbb{P}(\theta^*\in C_{\alpha})\geq 1-\alpha$. Check out our paper for a description of the methods.

:rocket: Getting started

In order to intall the cimat package, run

pip install cimat

or

pip install git+https://github.com/awslabs/cis-matching-tasks.git

for the latest version of the package.

Test your setup using this example that derives confidence intervals for FNMR and FMR obtained by binarizing the similarity scores at a given threshold:

import json
from cimat import MTData, UncertaintyEstimator

# Load embeddings from JSON file into a dictionary structure
df = json.load(open('data/embeddings.json', 'r'))  # Example structure: dictionary[id][image] = embedding
mt = MTData(df)
mt.generate_similarity_scores()  # Generate cosine similarity scores between images

# Set a threshold for determining matches versus non-matches
threshold = 0.9
# Instantiate the class to estimate error rates using similarity scores
# Example structure: dictionary[id1][id2] = [score between image from id1 and id2]
uq = UncertaintyEstimator(scores=mt.similarity_scores) 
# Compute False Non-Match Rate (FNMR, aka FNR) and False Match Rate (FMR, aka FPR) based on the threshold
fnr, fpr, _ = uq.compute_binerror_metrics(threshold)
fnr, fpr

## Calculate 95% Confidence Intervals (CI) for FNMR and FMR using Wilson's method
# with a plug-in estimator of the variance
var_fnr, var_fpr = uq.compute_variance(threshold=threshold, estimator="plugin")
ci_fnr, ci_fpr = uq.get_binerror_ci(threshold=threshold, var_fnr=var_fnr, var_fpr=var_fpr, alpha=0.05)
ci_fnr, ci_fpr

# with a double-or-nothing bootstrap estimator of the variance
uq.run_bootstrap(B=1000)  # runs the bootstrap
var_fnr_boot, var_fpr_boot = uq.compute_variance(threshold=threshold, estimator="boot")
ci_fnr_boot, ci_fpr_boot = uq.get_binerror_ci(threshold=threshold, var_fnr=var_fnr_boot, var_fpr=var_fpr_boot, alpha=0.05)
ci_fnr_boot, ci_fpr_boot

To generate the intervals without bothering about variance estimation, use

uq.get_binerror_ci(threshold = threshold, alpha = 0.05)

Under the hood, this function computes the variance with the plug-in estimator.

To obtain pointwise confidence intervals for the ROC with the double-or-nothing bootstrap, use

ci_tpr_at_fnr, ci_auc = uq.get_roc_ci(target_fpr=[0.01, 0.1], alpha = 0.05)
ci_tpr_at_fnr, ci_auc

See the code in examples/morph.ipynb for a more detailed example on how to use the package. In case of large datasets, the computations of the uncertainty may be burdensome. Luckily, the computational speed of the functions in this package can be substantially improved. Contact me if you are interested in this.

We have moved all the code related to the experiments in the paper to another branch named paper.

:books: Citation

To cite our paper/code/package, use

@article{fogliato2023confidence,
  title={Confidence Intervals for Error Rates in Matching Tasks: Critical Statistical Analysis and Recommendations},
  author={Fogliato, Riccardo and Patil, Pratik and Perona, Pietro},
  journal={arXiv preprint arXiv:2306.01198},
  year={2023}
}

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.