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Train and score the §4 ablation on InterPro 85.0 (t0) domain architectures - #75
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📝 WalkthroughWalkthroughThis change adds InterPro 85.0 data sources and archived-input support for training and evaluation. It records input and subset provenance, checks evaluation inputs against training manifests, and adds a t0 InterPro ablation workflow with a paired comparison and updated analysis documentation. ChangesArchived inputs and provenance
Evaluation validation and cohort selection
t0 InterPro ablation and comparison
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~45 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant Runbook as run_t0_interpro_ablation.py
participant Extractor as extract_human_interpro.py
participant Trainer as run_dcgo_human.py
participant Evaluator as validation/ablation.py
participant Comparator as validation/compare_t0_interpro.py
Runbook->>Extractor: Extract subset from InterPro 85.0 using t0 and t1 GAFs
Runbook->>Trainer: Train ablation rungs with t0 GAF and selected subset
Runbook->>Evaluator: Score rungs on committed cohort
Runbook->>Comparator: Compare three arms on shared cohort and IC values
Merge Risk: 🟡 Moderate · up to A changed T1 annotation file can produce incorrect ablation scores without an error. Check selection-input contents before reusing the subset and completed runs. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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The 2021 -> 2026 benchmark and the section 4 ablation train on GOA release 205 (2021-04-21) but read the 2026-07 protein2ipr, in training and in the evaluator's transfer step: look-ahead on the domain side (TODO P0). This is the code half of removing it; the InterPro 85.0 runs follow on this branch. - run_dcgo_human.py takes --gaf and --interpro (defaults unchanged). Both flow through RunRequest into the early missing-input checks and into the manifest with hashes and release headers like the defaults, but without the species' current-release URL label, which would name other bytes. This replaces the symlink staging (goa_human_t0_2021 / a linked default GAF path) in RESULTS.md, validation/mouse/PROVENANCE.md and the section 4 regeneration notes. - extract_human_interpro.py takes --source, repeatable --gaf (union) and --output, so a t0 subset can be cut from an archived protein2ipr and selected by the t0 and t1 GAFs. The current subset was selected by the t1 GAF's non-IEA proteins alone (19,089, exactly); t0 + t1 under any evidence is 19,871, a strict superset. - download_data.py --group interpro-85 fetches InterPro 85.0 (protein2ipr checked against the release's published md5, interpro2go, entry.list) into data/raw/interpro_archive/85.0/. - validation/ablation.py refuses to score a rung whose manifest records a different protein2ipr (by SHA-256) than its --interpro, and its provenance TSV records the protein2ipr path and hash. temporal_benchmark.py applies the same check against the manifest beside --predictions. - scripts/run_t0_interpro_ablation.py is the runbook for the follow-up: verify the archive, extract the subset, run the nine rungs exactly as their committed manifests record them plus --gaf/--interpro, evaluate. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
On a loaded machine one rung can outlive a job limit; rerunning the whole
cell then repeats ~15 minutes of finished work. A rung whose manifest is
finalised ("completed") is now skipped, as the extraction already was when
its marker names the same source. Skipping is safe because the evaluator
refuses any rung trained on a different protein2ipr than the subset.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Review of the t0 InterPro branch: - The evaluators now compare the run manifest's training GAF with --t0-gaf, as they already did protein2ipr with --interpro. The committed ablation manifests read GOA 205 through the default GAF path, which now holds the 2026 file: replaying one without --gaf trains on the held-out labels, and nothing refused it. The provenance TSV records the t0/t1 GAF and cohort file hashes too. apply_relative_inference.py, temporal_surprise.py and temporal_breadth.py apply the same check. - temporal_benchmark.py --allow-input-mismatch turns the check into a warning. The all-species held-out arm (scratch_allspecies/13, 14) trains on other protein2ipr and GAF files on purpose and was refused outright; both scripts now pass it. - ablation.py --cohort-interpro fixes the scored cohort to one file. The runbook passes the committed cell's, so the t0 arm is scored on the same no-knowledge proteins as the committed cell and the control: a protein with no InterPro 85.0 domain is a miss, not dropped from the denominator, and ablation_selection_counts.tsv counts them. Default unchanged. - The runbook reuses a subset only when its marker records this exact extraction (source, both GAFs, evidence filter "all"), reruns every rung when it cuts a new one, and copies the subset's sidecar into the eval manifests so the archive and selection are committed. Extract markers now record the evidence filter. - Docs: the runbook is .py, not .sh; the current protein2ipr was downloaded 2026-07-07, not "of 2026-07-22"; VALIDATION_PLAN states the rerun's remaining look-ahead (seven of nine rungs train on go-basic 2026-06-15). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
validation/compare_t0_interpro.py scores three arms of the ipr_manual cell on the committed cohort: the committed cell (current protein2ipr, t1 non-IEA selection), a control (current protein2ipr, t0+t1 selection) and InterPro 85.0 with the same selection. Per aspect x IC floor it runs one resampling.paired_bootstrap over every arm x method panel, on the resamples the per-arm cells used (ablation.bootstrap_seed), and writes control - baseline (selection), t0 - control (InterPro release) and t0 - baseline for the report's four configurations and naive. It writes nothing unless every arm reproduces its committed ablation_metrics.tsv, the arms share one cohort and IC table, and the naive baseline is identical across them. Runs trained on another protein2ipr or GAF are refused as in ablation.py. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The ipr_manual cell retrained on InterPro 85.0 (2021-04-08) and a selection-control arm (current protein2ipr, the same t0+t1 protein selection) were scored with the post-#74 evaluator. Both use the committed cell's cohort (--cohort-interpro): 298 BP / 406 MF / 436 CC, of which 19 / 16 / 14 proteins have no InterPro 85.0 domain and count as misses. The runs predate this rebase. run_dcgo_human.py and the analysis code are unchanged since, so they were scored directly with validation/ablation.py rather than through the runbook, whose evidence-filter check would re-extract and retrain them. - Selection (control - baseline): within -0.003..+0.008 F_max and -0.001..+0.005 AUPRC across the four configurations. - InterPro release (t0 - control): lower in 32/36 F_max (11 significant) and 36/36 AUPRC (18 significant) comparisons, at most 0.023 / 0.018. Base loses most in MF: 0.357 -> 0.335 F_max at IC 2. - No conclusion changes. Base -> Base + relative keeps the same significant cells in the same direction (F_max 7/9 lower, 5 sig; AUPRC 8/9 lower, 5 sig, MF IC 0 a significant gain). The permutation null is 18/18 at p = 1/201 in every arm. Tables: validation/ablation_cells/{ipr_manual_t0interpro, ipr_manual_selctrl}/ with rung manifests and the subset provenance sidecars, the own-cohort sensitivity in ipr_manual_t0interpro_owncohort/, and the paired comparison in t0_interpro_comparison.tsv. VALIDATION_PLAN section 4 has the write-up, including what is not removed: seven of nine rungs still train with go-basic.obo 2026-06-15. RELATIVE_INFERENCE_REPORT section 4.2 gets the t0 row, and TODO P0 lists the analyses that still read current protein2ipr. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Review fixes for the t0 InterPro PR. - extract_human_interpro.py: --source or --gaf without --output is now a usage error. Before, it replaced data/interim/protein2ipr_<species>.dat.gz, the default --interpro of every run and evaluator; the overwrite guard could not catch it (same "goa" rule, and the human extract has no sidecar). - validation/ablation.py: with --cohort-interpro, the proteins_with_domains row counts the cohort file, so it is now labelled as such, and a new transfer_proteins_with_domains row counts the --interpro file. Rescored both arms with the runbook's eval command: t0 19,393 and control 19,522 transfer proteins against the cohort file's 18,908. The other four tables reproduce byte-for-byte, except one s_min in the t0 metrics that differs in the last digit (float order); the committed metrics are kept. - VALIDATION_PLAN section 4: the control arm flips 9 of 414 paired significance calls against the committed cell (t0: 22), none changing sign, including two supra - single F_max losses that no other arm has. The cohort is still chosen by the 2026 protein2ipr, which biases against t0; the own-cohort run makes the same Base -> Base + relative calls. The manifests' commit ac7485a is mapped to its rebased twin 613ead2, and the claim that run_dcgo_human.py was "unchanged since" is replaced by what did change. - REPRODUCIBILITY.md: the two committed subset sidecars predate the evidence_filter field. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Review comments at @scripts/run_t0_interpro_ablation.py:
- Around line 100-166: Update build_steps and the subset marker validation to
record and compare content hashes for both T0_GAF and T1_GAF; treat either hash
changing as a new subset so extraction runs and completed rungs are rerun.
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📒 Files selected for processing (52)
RELATIVE_INFERENCE_REPORT.mdREPRODUCIBILITY.mdRESULTS.mdTODO.mdVALIDATION_PLAN.mdconfig/settings.pyextract_human_interpro.pyrun_dcgo_human.pyscratch_allspecies/13_heldout_allspecies.shscratch_allspecies/14_heldout_experimental.shscripts/download_data.pyscripts/run_t0_interpro_ablation.pysrc/runner.pysrc/universe_provenance.pytests/integration/test_extract_human_interpro.pytests/integration/test_input_overrides.pytests/integration/test_validation_clis.pytests/unit/test_ablation.pytests/unit/test_compare_t0_interpro.pytests/unit/test_download_checksums.pytests/unit/test_generic_runner.pytests/unit/test_run_manifest_wiring.pytests/unit/test_run_t0_interpro_ablation.pytests/unit/test_temporal_benchmark.pytests/unit/test_universe_provenance.pyvalidation/ablation.pyvalidation/ablation_cells/ipr_manual_selctrl/manifests/full.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/protein2ipr_human_t0t1sel_current.dat.gz.provenance.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/single.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra_input.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra_input_output.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra_input_relative.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra_output.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra_relative.jsonvalidation/ablation_cells/ipr_manual_selctrl/manifests/supra_relative_output.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/full.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/protein2ipr_human_t0_ipr85.dat.gz.provenance.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/single.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra_input.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra_input_output.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra_input_relative.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra_output.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra_relative.jsonvalidation/ablation_cells/ipr_manual_t0interpro/manifests/supra_relative_output.jsonvalidation/apply_relative_inference.pyvalidation/compare_t0_interpro.pyvalidation/mouse/PROVENANCE.mdvalidation/temporal_benchmark.pyvalidation/temporal_breadth.pyvalidation/temporal_surprise.py
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The runbook reused an existing subset when its sidecar named the same source, GAF paths and evidence filter. The t1 GAF lives at a current-release path that a new download overwrites, so a stale subset would be reused without a word, and held-out proteins new to the refreshed GAF would score as misses for want of architectures. Extract sidecars now record selection_sha256, the SHA-256 of each selecting GAF in source order (read back as None for older sidecars), and the runbook reuses a subset only when both hashes still match; otherwise it re-extracts and reruns every rung. Raised by CodeRabbit on #75. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Stacked on #74 (
cafaeval-crosscheck, the evaluator fixes). The base is that branch, so the diff shows only this work. Retarget tomainonce #74 merges.Closes the domain-side half of the TODO P0 look-ahead item for the §4 ablation. The 2021 → 2026 benchmark trained on GOA 205 (2021-04-21) but read the 2026-07
protein2iprin training and in the evaluator's transfer step.Code
run_dcgo_human.py --gaf/--interpro. Both inputs are recorded in the manifest with hashes. Defaults are unchanged.extract_human_interpro.py --source/--gaf (repeatable)/--output. The extract marker records the evidence filter.download_data.py --group interpro-85fetches InterPro 85.0 (2021-04-08) and checks its md5.ablation.py,temporal_benchmark.py, breadth, surprise,apply_relative_inference.py) refuse a run whose manifest records a different protein2ipr or training GAF.temporal_benchmark.py --allow-input-mismatchis for the all-species arm.ablation.py --cohort-interprofixes the scored cohort. A cohort protein with no domain in--interprocounts as a miss.scripts/run_t0_interpro_ablation.pyis the runbook: extract, run the nine recorded rung commands, evaluate.validation/compare_t0_interpro.py, a cross-arm paired bootstrap on one cohort. It reusesresampling.paired_bootstrapon the per-cell resamples. It refuses to write unless every arm reproduces its committed metrics and the arms share one cohort, IC table and naive baseline (both checks passed).Three arms of
ipr_manual, all scored on the committed cohort (298 BP / 406 MF / 436 CC)19 / 16 / 14 cohort proteins have no InterPro 85.0 domain and count as misses. The naive baseline is identical across arms.
Base (
supra_input) F_max / AUPRC, baseline · control · t0, and t0 − control (95% paired CI):Not removed
go-basic.obo2026-06-15 (propagation, parental background, retired-id remap).protein2ipr.Both points are in
VALIDATION_PLAN.md§4 "t0 InterPro architectures",RELATIVE_INFERENCE_REPORT.md§4.2 (new t0 row) and TODO P0.How it was scored
The two arms were trained earlier at
ac7485a.run_dcgo_human.pyandsrc/analysis code are unchanged since. I scored them by callingvalidation/ablation.pydirectly (the runbook's eval step,--cohort-interpro data/interim/protein2ipr_human.dat.gz). The subset sidecars predate the marker'sevidence_filterfield (the extract logs showEvidence codes: ALL), so running the runbook would have re-extracted and retrained. I did not edit the sidecars.The rebase onto #74 resolved conflicts in
validation/ablation.py(--cohort-interpronow threads throughload_benchmark_inputs/BenchmarkInputs.cohort_proteins, andbuild_methodstolerates cohort proteins without architectures),tests/unit/test_temporal_benchmark.pyandVALIDATION_PLAN.md.CI gates (local)
ruff check: all passedruff format --check: 127 files formattedmypy: no issues in 7 filespytest: 1083 passed🤖 Generated with Claude Code
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