cafaeval-protea

This is a modified fork. See CHANGES.md for the full list of modifications and their dates (required by GPLv3 §5.a).

cafaeval-protea is a fork of CAFA-evaluator-PK by Clara De Paolis, which is itself a fork of CAFA-evaluator by the BioComputing UP group at the University of Padua (Piovesan et al., 2024).

The fork exists to provide a faster evaluator for iterative work, without changing any scoring semantics. Fmax, Smin, weighted variants, Partial-Knowledge (PK) evaluation and information accretion weighting are all preserved and validated against the upstream output before any optimization lands.

The Python import path remains cafaeval (identical API) so that existing downstream code using from cafaeval.evaluation import cafa_eval keeps working unchanged. Only the PyPI distribution name differs:

pip install cafaeval-protea          # installs the fork
python -c "from cafaeval.evaluation import cafa_eval"  # same import

Attribution

Upstream authors (primary — always cite)

The original evaluator and all of its scoring logic are the work of:

CAFA-evaluator: A Python Tool for Benchmarking Ontological Classification Methods D. Piovesan, D. Zago, P. Joshi, M. C. De Paolis Kaluza, M. Mehdiabadi, R. Ramola, A. M. Monzon, W. Reade, I. Friedberg, P. Radivojac, S. C. E. Tosatto. Bioinformatics Advances, 2024. DOI: 10.1093/bioadv/vbae043

Upstream repository: BioComputingUP/CAFA-evaluator. Copyright © 2022 Damiano Piovesan. Licensed under GPLv3.

Direct parent (PK variant)

The Partial-Knowledge evaluation extensions were contributed by Clara De Paolis in claradepaolis/CAFA-evaluator-PK. This fork is branched directly from that repository and inherits its semantics for -known annotations and terms-of-interest filtering.

Speedup ideas and cherry-picked commits

The Phase A algorithmic speedups in this fork (weighted-only fast path, cached per-term children lists, fill-mode restricted to zero rows, incremental non-zero counter in the prediction parser, shared-memory parallel DAG propagation, fork-pool initializer pattern for the threshold sweep) come from Antonina Dolgorukova (T0chka) and her public fork T0chka/CAFA-evaluator-PK-speedup, which she shared in the CAFA 6 Kaggle discussion "Speeding up cafaeval" (post #664359).

The five substantive commits from her speedup-local branch were cherry-picked into this fork with authorship preserved (see git log). On top of that, we added: dead-code removal, structured cafaeval.* logging, extension of the fork-pool initializer pattern from the NK/LK branch to the PK (gt_exclude) branch of compute_metrics, and the parity harness under bench/ and tests/diff/.

If you use the speedups in published work please acknowledge the upstream paper above, Clara De Paolis' PK fork, and Antonina Dolgorukova's speedup work.

Scope of modifications

This fork modifies the following parts of the upstream:

Detailed per-commit diff against the upstream is maintained in CHANGES.md.

Validation policy

No optimization lands in this fork without a passing parity test against a frozen upstream oracle. The oracle is built in bench/ by running the unmodified upstream against a set of deterministic synthetic corpora (tiny / medium / large) and serializing the full output — Fmax, Smin, weighted Fmax, weighted Smin, precision-recall curves, optimal thresholds — into bench/oracle/*.pkl. The diff tests under tests/diff/ reload that oracle and compare the fork's output:

• Phase A (parser cherry-picks, cached children, weighted-only, zero-row propagation, NK sparse kernel, sparse propagate): atol=0, rtol=0. A single-bit divergence is a bug.
• Phase B (sparse PK confusion matrix): rtol=1e-6, atol=1e-9. The PK sparse kernel reorders per-protein inner sums, so a ULP-level (~4e-16) divergence in pr is expected and tolerated.

The active phase is controlled by the CAFAEVAL_PARITY_PHASE env var (A or B). The default flipped from A to B when the Phase B2 PK kernel landed. On a 4.45M-row real corpus the fork agrees with unmodified upstream to 2.1e-14 in PK and 1.9e-14 in NK, well inside the Phase B tolerance.

No result from this fork is trusted until the relevant corpus passes its diff test.

Performance

End-to-end wall time of a full cafa_eval(...) call (OBO load → ground truth parse → prediction parse + propagate → confusion matrix sweep → metric assembly → best-row aggregation), measured on a single workstation at n_cpu=1 and the CAFA-default th_step=0.01 (99 thresholds). "Upstream" is unmodified claradepaolis/CAFA-evaluator-PK at commit 16a6a6d. Corpus: real CAFA 6 PROTEA artifacts (8 712 BP / 4 992 MF / 5 125 CC ground-truth proteins, ~700 k-row prediction file, known_terms.tsv exclude set for PK).

The sparse confusion-matrix kernels (Phase B1/B2) are approximately flat in n_tau — moving from th_step=0.05 (20 thresholds) to the CAFA default th_step=0.01 (99 thresholds) costs the fork virtually nothing, while upstream's per-threshold scan scales linearly. Hence the speedup ratio grows with n_tau.

Where the fork's remaining time goes on PK end-to-end (10 s):

Runtime knobs

All optimizations are gated by environment variables so the legacy path is always available for A/B comparison or debugging.

Install

pip install cafaeval-protea               # core install (numpy + pandas + matplotlib)
pip install "cafaeval-protea[fast]"       # enables the PyArrow parser fast path

The hard dependency set is kept at numpy + pandas + matplotlib; pyarrow>=12 is an optional [fast] extra. Without it, pred_parser automatically falls back to the legacy loop.

Logging

The upstream evaluator is silent at the library boundary, which makes long-running calls inside other pipelines opaque. This fork adds structured logging using the stdlib logging module (no new dependencies), organized as a proper logger hierarchy:

cafaeval                    # root logger
├── cafaeval.parser         # parsing predictions / ground truth
├── cafaeval.propagate      # DAG propagation
├── cafaeval.metrics        # compute_metrics
└── cafaeval.eval           # orchestrator

Conventions:

• INFO: high-level events with timing, e.g. "parser: parsed 12345 proteins in 3.21s".
• DEBUG: per-namespace, per-threshold detail, matrix shapes.
• WARNING: non-fatal anomalies (terms missing from the ontology, proteins without ground truth).
• No print() calls, no basicConfig() inside the library. Handler configuration is always the consumer's responsibility.
• Structured fields are passed via logger.info(..., extra={...}) so that downstream consumers can extract machine-readable payloads without parsing log strings.

A downstream project that wants to capture these logs only needs:

import logging
logging.getLogger("cafaeval").setLevel(logging.INFO)
# attach your own handler here

Usage

The CLI and library interfaces are unchanged from the upstream. See README_upstream.md for the full input-file formats, command-line flags, and output layout. The cafaeval console script and the cafa_eval(...) Python entry point continue to work exactly as documented there.

License

GNU General Public License v3 (GPLv3), inherited unchanged from the upstream. See LICENCE.md.

Per GPLv3 §5.a, modifications introduced by this fork are documented in CHANGES.md with their dates. The original upstream copyright notice (© 2022 Damiano Piovesan) is preserved verbatim in LICENCE.md and is not superseded by the existence of this fork.