osr-metrics 0.1.2

Open-Set Recognition (OSR) and OOD-detection metrics for ML research

osr-metrics

Open-Set Recognition (OSR) and OOD-detection metrics for machine-learning research.

A small, framework-agnostic Python library that bundles the metrics needed for credible OSR / OOD-detection publications, with consistent score-direction conventions and first-principles-verified formulas.

What's inside

Group	Metrics
OOD detection	auroc, fpr_at_tpr, fpr_at_95tpr, aupr_in, aupr_out
Open-Set Recognition	compute_aoscr (canonical Dhamija/Vaze), oscr_curve, compute_nf_rejection_at_tpr
Multi-label classification	macro_auprc, macro_auprc_id_labels, macro_f1_with_thresholds, per_label_auprc, f1_per_label
Four-class OSR partitioning	build_fourclass_masks, compute_fourclass_metrics, partition_ood_by_purity
Calibration	expected_calibration_error, brier_score
Statistical comparison	delong_test (O(n log n) rank-based), bootstrap_ci (with optional stratification)

All functions take plain numpy arrays and return scalars or simple dictionaries — no PyTorch, TensorFlow, or framework lock-in.

Score-direction convention

For every OOD/novelty metric in this library, higher score = more OOD. ID-positive metrics (aupr_in) handle the sign flip internally so you don't have to.

Install

pip install osr-metrics

Requires Python 3.10+, numpy, scikit-learn, scipy.

Development install

git clone https://github.com/hxtruong6/osr-metrics.git
cd osr-metrics
pip install -e .[dev]

Quick start

import numpy as np
from osr_metrics import auroc, fpr_at_95tpr, compute_aoscr, expected_calibration_error

# OOD detection
scores = np.random.randn(1000)          # higher = more OOD
labels = np.random.randint(0, 2, 1000)  # 1 = OOD, 0 = ID
print("AUROC:", auroc(scores, labels))
print("FPR@95TPR:", fpr_at_95tpr(scores, labels))

# Open-Set Classification Rate (joint classify+reject)
cls_pred = np.random.randint(0, 5, 1000)
cls_true = np.random.randint(0, 5, 1000)
print("AOSCR:", compute_aoscr(scores, labels, cls_pred, cls_true))

# Calibration
probs = np.random.uniform(0, 1, (1000, 14))
multi_labels = (np.random.uniform(0, 1, (1000, 14)) < probs).astype(int)
print("ECE:", expected_calibration_error(probs, multi_labels))

Statistical comparison

from osr_metrics import delong_test, bootstrap_ci, auroc

# Pairwise AUROC comparison (DeLong 1988)
z, p = delong_test(scores_method_a, scores_method_b, labels)
print(f"DeLong z={z:.3f}, p={p:.4f}")

# Bootstrap CI (use stratify=True for imbalanced data)
lo, mean, hi = bootstrap_ci(scores, labels, auroc, n_bootstrap=1000, stratify=True)
print(f"AUROC = {mean:.4f}  95% CI = [{lo:.4f}, {hi:.4f}]")

Four-class OSR partitioning

For multi-label problems with held-out labels (chest X-ray OSR style):

from osr_metrics import build_fourclass_masks, compute_fourclass_metrics

label_names = ["A", "B", "C", "D"]
held_out = ["C", "D"]
metrics = compute_fourclass_metrics(scores, label_vecs, label_names, held_out)
# Returns: auroc_full, fpr95_full, auroc_pure, auroc_mixed,
#          auroc_mixed_vs_id_disease, auroc_nf_vs_pure,
#          auroc_disease_only, counts...

Partitions images into four mutually exclusive classes:

• id_disease — only known labels
• no_finding — all-zero label vector
• pure_ood — only held-out labels
• mixed_ood — both known + held-out labels

Five AUROC pairings answer different questions:

Key	Negatives	Positives	What it asks
auroc_pure	ID-disease + NF	Pure OOD	Upper-bound separability
auroc_mixed	ID-disease + NF	Mixed OOD	Mixed-OOD detection difficulty
auroc_mixed_vs_id_disease	ID-disease only	Mixed OOD	Near-OOD sensitivity (NF removed)
auroc_nf_vs_pure	NF only	Pure OOD	Diagnostic floor: healthy-vs-anything
auroc_full	ID-disease + NF	Pure + Mixed OOD	Full population measurement

Why another metrics library?

Most OOD/OSR libraries (PyTorch-OOD, OpenOOD) couple metrics with detection methods, datasets, and a heavy framework. osr-metrics is just the metrics — useful when you want to compute AOSCR or DeLong on cached scores from any pipeline, regardless of how those scores were produced.

Documentation

• docs/USAGE.md — "which metric should I use?" decision tree.
• docs/EXAMPLES.md — end-to-end runnable examples including the full publication metric panel, DeLong comparison, and seed aggregation.
• CHANGELOG.md — version history.
• CITATION.cff — citation metadata.

Testing

pytest tests/ -v

Each metric is verified against a first-principles brute-force reference; the test suite covers numerical equivalence, edge cases (empty class, single-value scores), and known properties (DeLong z=0 on identical inputs, ECE=0.9 on overconfident-wrong, etc.).

License

MIT.