diff --git a/.github/workflows/paper.yml b/.github/workflows/paper.yml new file mode 100644 index 00000000..b6dfc748 --- /dev/null +++ b/.github/workflows/paper.yml @@ -0,0 +1,61 @@ +name: Paper + +# JOSS paper preview: compiles paper/paper.md to PDF whenever paper content changes, so a +# broken front matter, malformed bibtex, or unresolvable citation fails here instead of at +# submission. The paper's structural gates (citation resolution, word budgets, front +# matter) live in tests/test_paper.py and run in ci.yml's python-test job on every PR; +# this workflow deliberately adds only the compiled artifact, path-filtered so untouched +# PRs pay nothing. +# +# Compilation uses the official JOSS paperdraft resources from the archived (frozen +# 2024-04-16) openjournals/whedon repository. The prebuilt openjournals/whedon Docker Hub +# image is gone, so the image is built per run; the Dockerfile needs three patches, +# identical to the Makefile's paper-image target (keep the two in sync): +# 1. fully-qualify the pandoc/latex base image registry, +# 2. pin tlmgr to the frozen TeXLive 2021 historic repo (the live tlnet now serves a +# newer TeXLive, so the stock install commands silently install nothing), +# 3. strip the vestigial biblatex block from the JOSS latex template (frozen polyglossia +# forbids the template's load order; pandoc citeproc renders references anyway). +on: + pull_request: + paths: + - "paper/**" + - ".github/workflows/paper.yml" + push: + branches: + - main + paths: + - "paper/**" + - ".github/workflows/paper.yml" + +permissions: + contents: read + +concurrency: + group: paper-${{ github.ref }} + cancel-in-progress: true + +jobs: + paper: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v7 + - name: Clone and patch JOSS paperdraft sources + run: | + git clone --depth 1 https://github.com/openjournals/whedon.git /tmp/whedon + sed -i 's|^FROM pandoc/latex|FROM docker.io/pandoc/latex|' /tmp/whedon/paperdraft.Dockerfile + sed -i '/^RUN tlmgr update --self/i RUN tlmgr option repository https://ftp.math.utah.edu/pub/tex/historic/systems/texlive/2021/tlnet-final' /tmp/whedon/paperdraft.Dockerfile + perl -0pi -e 's/\\usepackage\[\n backend=biber,\n%[^\n]*\n%[^\n]*\n\]\{biblatex\}\n\\bibliography\{[^}]*\}\n//; s/\\renewcommand\{\\bibfont\}\{[^{}]*\}\n//' /tmp/whedon/resources/joss/latex.template + - name: Build paperdraft image + run: docker build -f /tmp/whedon/paperdraft.Dockerfile -t paperdraft /tmp/whedon + # paperdraft writes paper.pdf next to paper.md (fail-if-warnings: on), so an + # unresolvable citation or latex warning fails the job. + - name: Compile the JOSS paper PDF (test build, fails on warnings) + run: docker run --rm -v "$PWD/paper:/paper" -w /paper paperdraft paper.md + - name: Upload PDF preview + uses: actions/upload-artifact@v4 + with: + name: paper + path: paper/paper.pdf + retention-days: 14 diff --git a/.gitignore b/.gitignore index 3b673b74..34d34ec2 100644 --- a/.gitignore +++ b/.gitignore @@ -34,4 +34,12 @@ *.opencode/ # mkdocs build output site/ +# JOSS paper build output: paper.md/paper.bib/INSTRUCTIONS.md are tracked, the PDF is generated +paper/*.pdf +paper/*.tex +paper/*.aux +paper/*.fls +paper/*.fdb_latexmk + .ralph/ +.paperdraft/ diff --git a/.markdownlint-cli2.jsonc b/.markdownlint-cli2.jsonc index 861487c0..85d16c69 100644 --- a/.markdownlint-cli2.jsonc +++ b/.markdownlint-cli2.jsonc @@ -6,7 +6,8 @@ // Scope deliberately includes top-level *.md, docs/, and .github/ templates, and excludes // machine-generated/internal trees: .git, node_modules, .venv, site (mkdocs output), // target (Rust build), __pycache__, .ralph (agent state), plans/ + PLAN.md (agent-generated -// planning artifacts), tests/ (fixture data), .tablassert (runtime artifacts). +// planning artifacts), .paperdraft (local clone of the JOSS paperdraft compiler sources), +// tests/ (fixture data), .tablassert (runtime artifacts). { "config": { "default": true, @@ -33,8 +34,20 @@ "style": "compact" } }, - "customRules": ["./.markdownlint/no-unicode-chars.cjs"], + "customRules": ["./.markdownlint/no-unicode-chars.cjs", "./.markdownlint/no-double-hyphen.cjs"], "globs": ["**/*.md"], + // The JOSS paper template puts every required section (Summary, Statement of need, + // ...) at heading level 1 below the YAML front-matter title; multiple H1s are the + // expected format there, not a structure error. + "overrides": [ + { + "filter": ["paper/paper.md"], + "config": { + "MD025": false + }, + "combine": "merge" + } + ], "ignores": [ "**/.git/**", "**/node_modules/**", @@ -45,6 +58,7 @@ ".ralph/**", "plans/**", "PLAN.md", + ".paperdraft/**", "tests/**", ".tablassert/**" ] diff --git a/.markdownlint/no-double-hyphen.cjs b/.markdownlint/no-double-hyphen.cjs new file mode 100644 index 00000000..ef31ebdc --- /dev/null +++ b/.markdownlint/no-double-hyphen.cjs @@ -0,0 +1,64 @@ +// Custom markdownlint rule: bans "--" in markdown prose. "--" is the ASCII stand-in for +// an em dash, the same AI-typical punctuation no-unicode-chars.cjs bans in unicode form. +// Rephrase with a comma, colon, semicolon, or parentheses instead. +// +// Only prose text is checked. Code spans, fenced or indented code, HTML (including +// ), link destinations, and autolinks are skipped, so CLI flags such as +// `--fix` stay legal when written as code. Runs of three or more hyphens (thematic +// breaks, table delimiter rows, front matter fences) are not matched. +// +// No fixInfo: the right replacement depends on the sentence, so a mechanical rewrite +// would make prose worse. Fix violations by hand. +// +// Loaded via "customRules" in .markdownlint-cli2.jsonc, which both the pre-commit hook and +// the CI markdown-lint job use. tests/test_docs_style.py's em-dash-substitute check covers +// only space-flanked "--" in user-facing docs; this rule covers every form, repo-wide. +"use strict"; + +// Micromark token types whose text is code, markup, or a URL rather than prose. +const SKIP = new Set([ + "autolink", + "codeFenced", + "codeIndented", + "codeText", + "definition", + "htmlFlow", + "htmlText", + "literalAutolink", + "resourceDestination" +]); + +const DOUBLE_HYPHEN = /(? { + walk(params.parsers.micromark.tokens, (token) => { + DOUBLE_HYPHEN.lastIndex = 0; + let match = null; + while ((match = DOUBLE_HYPHEN.exec(token.text)) !== null) { + // Data tokens never span lines, so the offset maps directly to a column. + onError({ + lineNumber: token.startLine, + detail: "Rephrase with a comma, colon, or parentheses, or wrap a flag in a code span", + range: [ token.startColumn + match.index, 2 ] + }); + } + }); + } +}; diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 350c4903..f6ae1b5c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -62,6 +62,17 @@ repos: - html-validate@11.16.4 - html-validate-markdown@6.0.0 pass_filenames: false + # JOSS paper integrity gate (front matter, citation resolution, word budgets). + # Scoped to one stdlib-only test file so it stays fast enough for the pre-commit + # stage, unlike the full suite (see the two-stage design comment above); the + # regular CI suite also runs it in python-test. markdownlint-cli2 above already + # lints paper/*.md via its **/*.md globs, so no paper scope is added there. + - id: paper-checks + name: paper-checks + entry: uv run pytest tests/test_paper.py -q -p no:cacheprovider + language: system + files: ^(paper/|tests/test_paper\.py$) + pass_filenames: false - id: pyright name: pyright entry: uv run pyright diff --git a/AGENTS.md b/AGENTS.md index dd7e0092..122c3044 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,13 +1,13 @@ -# AGENTS.md -- Tablassert +# AGENTS.md: Tablassert Tablassert turns biomedical spreadsheets into KGX knowledge graphs for NCATS Translator. Python package in `src/tablassert/` plus a PyO3 Rust extension in `rust/` built with maturin; uv-managed. Guidance below is evidence-keyed; fix anything stale when touched. ## Build / Test / Lint -- One-time setup: `make setup` (uv sync --group dev --extra cli --extra qc --extra log, then debug `maturin develop`). [verified] +- One-time setup: `make setup` (`uv sync --group dev --extra cli --extra qc --extra log`, then debug `maturin develop`). [verified] - After any Rust change, or when the extension may be stale: `make dev` (debug build; `make build` for release-mode performance checks only). [verified] - Full local gate before a PR: `make check` (ruff lint + format check + pyright + pytest + cargo test + clippy -D warnings). [verified] -- Python tests: `uv run pytest` [verified] -- parallel via pytest-xdist (`-n auto` in pyproject addopts, coverage inline); use `-n 0` for one serial test while debugging. +- Python tests: `uv run pytest` [verified]; parallel via pytest-xdist (`-n auto` in pyproject addopts, coverage inline); use `-n 0` for one serial test while debugging. - Rust tests: `cargo test --manifest-path rust/Cargo.toml` (evidence: ci) - CI jobs are exactly: `ruff check`, `ruff format --check`, `uv run pyright`, `uv run pytest` (after `uv run maturin develop`), `cargo fmt --check`, `cargo clippy --all-targets -- -D warnings`, `cargo test`. (evidence: ci) - After editing `pyproject.toml`, relock; `uv lock --check` runs in pre-commit and catches a stale lockfile. [verified] diff --git a/CITATION.cff b/CITATION.cff index a73ff715..3f6c2e9c 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -12,16 +12,16 @@ authors: email: sgoetz@isbscience.org affiliation: Institute for Systems Biology orcid: "https://orcid.org/0009-0007-8373-2352" - - given-names: "Gwênlyn" - family-names: Glusman - email: gglusman@isbscience.org - affiliation: Institute for Systems Biology - orcid: "https://orcid.org/0000-0001-8060-5955" - given-names: Jared C. family-names: Roach email: jroach@isbscience.org affiliation: Institute for Systems Biology orcid: "https://orcid.org/0000-0003-4524-0107" + - given-names: "Gwênlyn" + family-names: Glusman + email: gglusman@isbscience.org + affiliation: Institute for Systems Biology + orcid: "https://orcid.org/0000-0001-8060-5955" references: - type: article title: "MicrobiomeKG: bridging microbiome research and host health through knowledge graphs" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 353105fe..a852239e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -59,6 +59,10 @@ The `Makefile` is intentionally small and mirrors the underlying commands: | `make fmt-check` | Ruff format check and `cargo fmt --check` | | `make typecheck` | Pyright | | `make check` | Lint, format check, typecheck, Python tests, Rust tests, and clippy with `-D warnings` | +| `make paper-check` | JOSS paper gate: front matter, citation resolution, word budgets | +| `make paper-image` | Build the JOSS paperdraft compiler image (local Docker layer cache) | +| `make paper-pdf` | Compile `paper/paper.pdf` from `paper/paper.md` with Docker | +| `make paper` | JOSS paper gate plus PDF compile | | `make clean` | Local build/test/doc artifacts | ## Project layout diff --git a/Makefile b/Makefile index 937f26f0..1fbb9a9d 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: setup build dev test test-rust lint fmt fmt-check typecheck check clean +.PHONY: setup build dev test test-rust lint fmt fmt-check typecheck check clean paper paper-check paper-pdf paper-image preprint preprint-pdf # Install Python dev dependencies, CLI/QC/log extras, and the editable Rust extension. setup: @@ -45,4 +45,48 @@ check: lint fmt-check typecheck test test-rust # Remove local build, test, documentation, and compiled-extension artifacts. clean: - rm -rf build dist site htmlcov .coverage .pytest_cache .ruff_cache rust/target src/tablassert/*.so + rm -rf build dist site htmlcov .coverage .pytest_cache .ruff_cache rust/target src/tablassert/*.so .paperdraft paper/paper.pdf paper/preprint.pdf paper/preprint.tex paper/preprint.aux paper/preprint.log paper/preprint.out + +# Validate the JOSS paper draft: front matter, citation resolution, word budgets. +paper-check: + uv run pytest tests/test_paper.py -q + +# Clone + patch the JOSS paperdraft sources once; the stamp file keeps the patches +# single-application across repeated make runs. +.paperdraft/.patched: + git clone --depth 1 https://github.com/openjournals/whedon.git .paperdraft/whedon + # Qualify the base image registry so podman-based runtimes (no unqualified-search + # registries) resolve it exactly like Docker does, and pin tlmgr to the frozen + # TeXLive 2021 historic repo: the base image (pandoc/latex:2.11.2 ships TeXLive 2021) + # points tlmgr at the live tlnet, which now serves a newer TeXLive, so the stock + # tlmgr install calls silently install nothing. + sed -i 's|^FROM pandoc/latex|FROM docker.io/pandoc/latex|' .paperdraft/whedon/paperdraft.Dockerfile + sed -i '/^RUN tlmgr update --self/i RUN tlmgr option repository https://ftp.math.utah.edu/pub/tex/historic/systems/texlive/2021/tlnet-final' .paperdraft/whedon/paperdraft.Dockerfile + # The JOSS template loads biblatex before pandoc's polyglossia and the frozen + # TeXLive 2021 polyglossia forbids that order; pandoc citeproc renders the + # bibliography anyway, so the biblatex block is vestigial and gets stripped. + perl -0pi -e 's/\\usepackage\[\n backend=biber,\n%[^\n]*\n%[^\n]*\n\]\{biblatex\}\n\\bibliography\{[^}]*\}\n//; s/\\renewcommand\{\\bibfont\}\{[^{}]*\}\n//' .paperdraft/whedon/resources/joss/latex.template + touch .paperdraft/.patched + +# Build the JOSS paperdraft compiler image from the openjournals/whedon resources. +# whedon is archived (frozen 2024-04-16) but its paperdraft.Dockerfile and LaTeX templates +# are still the official JOSS compilation path; the old openjournals/whedon Docker Hub +# image is gone, so the image is built locally. Docker layer cache makes this a no-op +# after the first build. +paper-image: .paperdraft/.patched + docker build -f .paperdraft/whedon/paperdraft.Dockerfile -t paperdraft .paperdraft/whedon + +# Compile paper/paper.md to paper/paper.pdf with the JOSS paperdraft image. +paper-pdf: paper-image + docker run --rm -v $(PWD)/paper:/paper -w /paper paperdraft paper.md + +# Full JOSS paper gate: validate the draft, then compile the PDF. +paper: paper-check paper-pdf + +# Generate standalone preprint LaTeX with Pandoc. +preprint: paper-check + cd paper && pandoc paper.md --citeproc --standalone --output=preprint.tex + +# Compile the generated preprint LaTeX to PDF; two XeLaTeX passes resolve references. +preprint-pdf: preprint + cd paper && xelatex -interaction=nonstopmode -halt-on-error preprint.tex && xelatex -interaction=nonstopmode -halt-on-error preprint.tex diff --git a/llms.txt b/llms.txt index a397f5a1..8f4267a4 100644 --- a/llms.txt +++ b/llms.txt @@ -38,7 +38,7 @@ Current CLI behavior queries an embedded redb database whose primary file is `fu - [Project Metadata](pyproject.toml): dependencies, tooling config, and CLI entrypoint. Optional extras: `cli`, `rt`, `aria2`, `qc`, and `log`. - [Tests](tests/): pytest coverage and fixtures. - [MkDocs Configuration](mkdocs.yml): docs navigation and published pages. -- [GitHub Workflows](.github/workflows/): `ci.yml` runs lint, format, type, test, and rust checks; `docs.yml` builds and deploys MkDocs; `pipy.yml` checks the project version, builds sdist and wheel artifacts, and publishes to PyPI; `tag-version.yml` tags changed project versions. +- [GitHub Workflows](.github/workflows/): `ci.yml` runs lint, format, type, test, and rust checks; `docs.yml` builds and deploys MkDocs; `pipy.yml` checks the project version, builds sdist and wheel artifacts, and publishes to PyPI; `tag-version.yml` tags changed project versions; `paper.yml` tests the JOSS paper compile and uploads the PDF artifact. - [Changelog](CHANGELOG.md): release history and version notes. ## Implementation Map diff --git a/paper/AGENTS.md b/paper/AGENTS.md new file mode 100644 index 00000000..ee5c1d6e --- /dev/null +++ b/paper/AGENTS.md @@ -0,0 +1,88 @@ +# JOSS Paper Instructions + +This directory holds the draft JOSS (Journal of Open Source Software) paper for Tablassert. +`paper.md` is the manuscript, `paper.bib` is its bibliography, and the compiled `paper.pdf` +is generated, never committed. See the [JOSS author guidelines](https://joss.readthedocs.io/en/latest/submitting.html) +before making major changes. + +## What JOSS reviewers check + +JOSS reviews the software as much as the text. Tablassert already satisfies the entry +requirements, so the review focuses on the paper itself: + +- A clear **statement of need**: what problem Tablassert solves and for whom. +- **State of the field**: how the prevailing practice (hand-written per-source Translator + ingests) falls short, what alternatives exist (Koza, KGX, hosted name resolution), and the + build-versus-contribute justification for a new package. +- **Software design** and a **research impact statement**: the design trade-offs and the + realized uses (MicrobiomeKG, DAKP, KRAKEN, pending Translator integration). +- An **AI usage disclosure** naming any generative tools used for the software, + documentation, or manuscript, with versions and how outputs were verified. +- An **archive**: the tagged release gets a Zenodo DOI at acceptance; keep + `CITATION.cff` and this paper's author list in sync. +- The paper's body stays between 750 and 1750 words excluding references; the abstract + under 250. The `paper-check` gate enforces both locally. + +JOSS rules change; re-check the + [paper format page](https://joss.readthedocs.io/en/latest/paper.html) before submission. + +## File map + +- `paper.md`: manuscript in JOSS (pandoc) format. YAML front matter carries title, + authors, ORCIDs, and the `bibliography: paper.bib` key that citeproc loads; + the body uses the sections JOSS expects (Summary, Statement of need, State of the + field, Software design, Research impact statement, AI usage disclosure, + Acknowledgements, References). Author `affiliation:` values are INDICES into the + separate `affiliations:` list (the JOSS convention); putting an institution string + there silently collapses the rendered author block to the last author. +- `paper.bib`: BibTeX entries. Every `[@key]` citation in `paper.md` must resolve to an + entry here, and every entry must be cited; `paper-check` enforces both directions. + Note: pandoc's bibliography parser treats `%` comment lines loosely, so keep `]`, `@`, + and other entry-like punctuation out of comments. +- `figures/`: figure images referenced from `paper.md` (create when adding the figure). + +## Workflow + +1. Draft or edit `paper.md`. Cite with `[@key]`, for example KGX [@kgx] renders as + "(Mungall et al. 2022)". Multi-cite as `[@a; @b]`. +2. Verify the matching `paper.bib` entry: title, authors, year, DOI. Entries seeded with + a `% TODO: verify` comment are unverified; check them against the publisher page or + before submission. +3. Add a figure by dropping the image into `figures/` and referencing it from its own + paragraph with `![Caption text](figures/fig1.png)`; JOSS turns a captioned standalone + image into a numbered figure. There is no figure in the current draft. +4. Run the local gates before committing: + +```bash +make paper-check # front matter, citations, word budgets (also a pre-commit hook) +make paper-pdf # compile paper/paper.pdf with Docker (requires Docker) +make paper # both +``` + +`make paper-pdf` builds the official JOSS paperdraft compiler image from the archived +openjournals/whedon resources (the prebuilt Docker Hub image is gone); the first build +takes a few minutes and later builds hit the Docker layer cache. The existing +markdownlint hooks cover this directory automatically; keep prose ASCII (no unicode +dashes or arrows) as everywhere else in the repository. + +## CI + +`.github/workflows/paper.yml` runs only when files under `paper/` change on a pull +request: it compiles the PDF from the JOSS paperdraft resources and uploads `paper.pdf` +as a workflow artifact so reviewers can preview the compiled paper without Docker. The +pytest gate runs in the regular CI suite on every pull request. + +## Submitting + +When the draft is ready: + +1. Resolve every `TODO(collaborators)` marker in `paper.md` (AI usage disclosure, + Acknowledgements) and verify every remaining `paper.bib` entry against its publisher + page or before submission. +2. Confirm `make paper-check` and `make paper-pdf` both pass. +3. The submitting author creates an account at , then submits + through the JOSS site, pointing at this repository and branch. The editor assigns + two reviewers; JOSS compiles `paper.md` automatically during review (the bot stack + formerly called Whedon, now Buffy, does the compilation). +4. Address review comments here, on this branch, until acceptance; JOSS archives the + accepted version and mints the DOI. diff --git a/paper/benchmarks/README.md b/paper/benchmarks/README.md new file mode 100644 index 00000000..0db302a6 --- /dev/null +++ b/paper/benchmarks/README.md @@ -0,0 +1,18 @@ +# Benchmarks + +Benchmarks backing the claims in the manuscript. Each subdirectory is self-contained: +scripts, inputs, and recorded results with the environment they were measured in. + +- `name_resolution/`: Tablassert's embedded fullmap resolution versus the hosted + Translator flow (`name-resolution-sri` `/lookup` plus batched NodeNormalization + `get_normalized_nodes`) on real ingest vocabulary, with per-term canonical + records and three-level coverage on both sides. +- `koza_comparison/`: line and non-whitespace-character counts for the + Tablassert tutorial recipe and a Koza transform calling the same Tablassert + resolution API, with recorded resolution-output equivalence. No runtime is + benchmarked. + +Name-resolution runs were executed on the `wenceslaus` build host (80 hardware +threads, 187 GiB RAM, Ubuntu Linux); environment details are recorded in +`name_resolution/results.json`. Numbers in the manuscript come from these +files and nowhere else. Re-run instructions are in each subdirectory's README. diff --git a/paper/benchmarks/evidence.md b/paper/benchmarks/evidence.md new file mode 100644 index 00000000..079bac46 --- /dev/null +++ b/paper/benchmarks/evidence.md @@ -0,0 +1,88 @@ +# Evidence for manuscript additions + +Checked 2026-10-29. These observations support the normalization, hardware, QC, +and research-impact descriptions; they are not additional benchmark trials. + +## Normalization and parallelism + +Source inspected at main commit `9a72ad4`: + +- `src/tablassert/nlp.py`, `level_one` and `level_two`: whitespace/quote cleaning, + Unicode lowercase, Porter2 stemming of ASCII alphabetic tokens, token deduplication, + byte-wise sorting and joining; level two removes non-word characters (`\W+`). +- `rust/src/nlp.rs`, `normalize_terms`: `py.detach` releases the Python GIL and + `into_par_iter` processes terms using Rayon. +- `rust/src/fullmap.rs`: GIL-released term lookups and parallel shard reads. +- `pyproject.toml`: Python >=3.11 supported. These workers do not require a + free-threaded Python interpreter. + +No controlled Python-only-versus-current benchmark was run here. The manuscript +identifies removed bottlenecks, rather than asserting an unmeasured speedup factor. + +## Benchmark host + +`lscpu`, `free`, `/etc/os-release`, `/proc/mdstat`, and `/sys/block` on wenceslaus: + +- Two Intel Xeon Gold 6230 processors, 20 cores/socket, two threads/core: + 40 physical cores, 80 hardware threads. +- 187 GiB RAM reported by `free -g`. +- Ubuntu 24.04.4 LTS. +- `/local_raid1` on `/dev/md127`, RAID1 over two non-rotating devices (SSDs). + +This is the host used for the recorded 2026-10-28 benchmark runs. Hardware was +inspected the following day, not captured contemporaneously by the harness. + +## DAKP mapping-QC rejection + +Source log on wenceslaus: + +```text +/local_raid1/sgoetz/CODE/DAKP/tmp/airflow/logs/dag_id=dakp_pipeline/run_id=manual__2026-10-22T21:34:56.310983+00:00/task_id=run_tablassert/attempt=1.log +``` + +Joined stderr fragments from the QC rejection: + +```text +QC rejected 'HMDB:HMDB0000607': original='factor ii' preferred='Cyanocobalamin' col=subject fuzz=37.5 config=faers_applied_to_treat.yaml hash=01e7437cc5b2706c.release.qc sapbert=0.4442202150821686 +``` + +The BABEL `2026jul22` file +`/local_raid1/sgoetz/DBSTORE/FULLMAP/downloads/synonyms/drugchemicalconflated.txt.gz` +contains `HMDB:HMDB0000607`, preferred name `Cyanocobalamin`, and the synonym +`Factor II`. The hosted `/synonyms` endpoint independently returns that alias: + +```bash +curl -s https://name-resolution-sri.renci.org/synonyms \ + -H 'Content-Type: application/json' \ + -d '{"preferred_curies":["HMDB:HMDB0000607"]}' +``` + +UniProt's reviewed human entry P00734 identifies prothrombin (gene F2), with +alternative name "Coagulation factor II": + + +This is an ambiguous synonym resolving to a vitamin rather than the coagulation +protein in a drug-treatment context. It demonstrates one useful rejection, not +an estimate of QC precision or recall. Other rejected aliases may be valid; +automatic rejection alone does not establish a mapping error. + +The earlier 1,661,094-row figure came from a separate DAKP off-label classification +audit, not Tablassert mapping QC, and has been removed from the manuscript. + +## Published graph sizes + +Dataset cards accessed 2026-10-29: + +- Multiomics 3.1.3: 222,994 nodes, 18,206,965 edges. + +- DAKP 1.26.0: 16,257 nodes, 135,263 edges. + + +Multiomics publication count: streamed the published edges file and counted distinct +`PMCID:PMC...` values in each record's `publications` list: 2,136 publications over +18,206,965 edges. The file's SHA-256 matches the dataset card: +`148a73057d8f9ffe72d2bc68d441eb514394e6a5dceb923cb8f36215f829f45c`. +Local path: +`/local_raid1/sgoetz/MOKG-REBUILD-19.7.0/out/published-3.1.3/MULTIOMICS_KP_3.1.3.edges.ndjson`. + +Counts describe separate versioned graphs, not their deduplicated union. diff --git a/paper/benchmarks/koza_comparison/README.md b/paper/benchmarks/koza_comparison/README.md new file mode 100644 index 00000000..368c1e11 --- /dev/null +++ b/paper/benchmarks/koza_comparison/README.md @@ -0,0 +1,69 @@ +# Koza comparison: configuration size and verbosity + +This directory compares two mappings for the same four-row tutorial CSV +(`tutorial-data.csv`). It does not benchmark runtime or additional tables. + +- Tablassert: `tutorial-table.yaml` maps columns to statements, provenance, and + annotations. `tutorial-graph.yaml` supplies the fullmap and Resource Ingest Guide + (RIG) configuration. +- Koza: `tutorial-koza.yaml` configures input/output. `tutorial-transform.py` + calls `tablassert.lib.resolve_many` once per whole column, with the recipe's + human taxon default (`9606`), Gene/Disease priorities, and column context enabled. + It uses the same fullmap path and retains all tied CURIEs, deduplicating repeated + aliases per input label before constructing the Cartesian product of matches. + +The Koza example intentionally depends on Tablassert for identical resolution; +this is not an independent resolver comparison or a benchmark of Koza alone. + +## Counting + +```bash +python count_loc.py --out loc.json +``` + +`count_loc.py` excludes blank lines, comment-only lines, and Python docstrings. +Verbosity is non-whitespace characters on the counted lines; indentation style +does not affect it. Tablassert's table and graph wrapper are shown separately. + +| Component | Lines | Non-whitespace characters | +| --- | ---: | ---: | +| Tablassert table recipe | 32 | 385 | +| Tablassert graph wrapper | 24 | 767 | +| Tablassert total | 56 | 1,152 | +| Koza YAML | 25 | 392 | +| Koza Python | 47 | 1,589 | +| Koza total | 72 | 1,981 | + +Shared installed library implementations are not counted on either side. Counts +illustrate where logic lives in this example, not general complexity or capability. + +## Resolution equivalence and output differences + +Verified on wenceslaus on 2026-10-30 using Tablassert 19.7.0, Koza 2.7.0, and the +`2026jul22` fullmap. `output-comparison.json` records exact subject/object pair +multiset equality and equality of unique node IDs, preferred names, and categories. +Both produce five edges and nine unique nodes from four input rows: pancreatic +cancer retains two tied CURIEs. The full graph records are not identical: + +- Tablassert emits nine deduplicated KGX NDJSON nodes and a populated RIG with + source, ingest, provenance, artifact locations, emitted fields, and record counts. +- Tablassert edges include category, knowledge level, agent type, structured + knowledge sources with source URLs, original labels, supporting studies with + study size and row references, and statistical-significance qualifiers. +- This Koza example emits KGX TSV with ten node rows (nine unique nodes), flat + knowledge-source/publication fields, p-value, and sample size. It does not + implement a RIG or the additional graph-level metadata above. Koza could emit + richer records with further code/configuration; their absence is scoped to this + example, not a limitation of the framework. + +Reproduce on the build host (both environments need Tablassert and its fullmap; +the Koza environment additionally needs Koza): + +```bash +/users/sgoetz/Code/dakp/.venv/bin/tablassert build-kg tutorial-graph.yaml +./koza-venv/bin/koza transform tutorial-koza.yaml -o koza-output +python compare_outputs.py --tablassert tutorial-output --koza koza-output \ + --out output-comparison.json +``` + +Offline tests: `pytest test_bench_koza.py`. diff --git a/paper/benchmarks/koza_comparison/compare_outputs.py b/paper/benchmarks/koza_comparison/compare_outputs.py new file mode 100644 index 00000000..23244207 --- /dev/null +++ b/paper/benchmarks/koza_comparison/compare_outputs.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +"""Compare Tablassert and Koza tutorial outputs for identical entity resolution. + +Usage on the build host after running both tools: + + python compare_outputs.py --tablassert tutorial-output --koza koza-output --out output-comparison.json +""" + +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path + + +def tablassert_graph(out_dir: Path) -> tuple[list[tuple[str, str]], dict[str, tuple[str, tuple[str, ...]]], list[str]]: + edges_path = next(out_dir.glob("*.edges.ndjson")) + nodes_path = next(out_dir.glob("*.nodes.ndjson")) + edges = [json.loads(line) for line in edges_path.open()] + nodes = [json.loads(line) for line in nodes_path.open()] + edge_pairs = sorted((edge["subject"], edge["object"]) for edge in edges) + node_records = {node["id"]: (node["name"], tuple(sorted(node["category"]))) for node in nodes} + edge_fields = sorted({key for edge in edges for key in edge}) + return edge_pairs, node_records, edge_fields + + +def koza_graph(out_dir: Path) -> tuple[list[tuple[str, str]], dict[str, tuple[str, tuple[str, ...]]], int, list[str]]: + with next(out_dir.glob("*_edges.tsv")).open() as stream: + edges = list(csv.DictReader(stream, delimiter="\t")) + with next(out_dir.glob("*_nodes.tsv")).open() as stream: + nodes = list(csv.DictReader(stream, delimiter="\t")) + edge_pairs = sorted((edge["subject"], edge["object"]) for edge in edges) + node_records = {node["id"]: (node["name"], tuple(sorted(node["category"].split("|")))) for node in nodes} + return edge_pairs, node_records, len(nodes), sorted({key for edge in edges for key in edge}) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--tablassert", type=Path, required=True) + parser.add_argument("--koza", type=Path, required=True) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + + tab_edges, tab_nodes, tab_fields = tablassert_graph(args.tablassert) + koza_edges, koza_nodes, koza_node_rows, koza_fields = koza_graph(args.koza) + result = { + "edge_subject_object_pairs_equal": tab_edges == koza_edges, + "unique_node_id_name_category_equal": tab_nodes == koza_nodes, + "tablassert": { + "edges": len(tab_edges), + "unique_nodes": len(tab_nodes), + "rig": any(args.tablassert.glob("*.RIG.yaml")), + "edge_fields": tab_fields, + }, + "koza": {"edges": len(koza_edges), "node_rows": koza_node_rows, "unique_nodes": len(koza_nodes), "edge_fields": koza_fields}, + "edge_pairs": tab_edges, + } + if not result["edge_subject_object_pairs_equal"] or not result["unique_node_id_name_category_equal"]: + raise SystemExit(f"resolution outputs differ: {json.dumps(result, indent=2)}") + args.out.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8") + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/paper/benchmarks/koza_comparison/count_loc.py b/paper/benchmarks/koza_comparison/count_loc.py new file mode 100644 index 00000000..608bc209 --- /dev/null +++ b/paper/benchmarks/koza_comparison/count_loc.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +"""Count configuration and code size for the Tablassert and Koza tutorial mappings. + +Python docstrings, comment-only lines, and blank lines are excluded. YAML comments +and blank lines are excluded. Verbosity is non-whitespace characters on the +counted lines. The Tablassert graph wrapper is reported separately from the table +recipe. +""" + +from __future__ import annotations + +import argparse +import ast +import json +from pathlib import Path + + +def python_lines(path: Path) -> list[str]: + """Return counted lines from a Python file.""" + source = path.read_text(encoding="utf-8") + tree = ast.parse(source) + docstring_spans: list[tuple[int, int]] = [] + for node in ast.walk(tree): + if isinstance(node, (ast.Module, ast.ClassDef, ast.FunctionDef, ast.AsyncFunctionDef)) and node.body: + first = node.body[0] + if isinstance(first, ast.Expr) and isinstance(first.value, ast.Constant) and isinstance(first.value.value, str): + docstring_spans.append((first.lineno, first.end_lineno or first.lineno)) + lines: list[str] = [] + for lineno, line in enumerate(source.splitlines(), start=1): + stripped = line.strip() + if not stripped or stripped.startswith("#") or any(start <= lineno <= end for start, end in docstring_spans): + continue + lines.append(line) + return lines + + +def yaml_lines(path: Path) -> list[str]: + """Return counted lines from a YAML file.""" + return [line for line in path.read_text(encoding="utf-8").splitlines() if line.strip() and not line.strip().startswith("#")] + + +def count_python(path: Path) -> int: + return len(python_lines(path)) + + +def count_yaml(path: Path) -> int: + return len(yaml_lines(path)) + + +def characters(lines: list[str]) -> int: + """Count non-whitespace characters, avoiding indentation-style differences.""" + return sum(1 for line in lines for char in line if not char.isspace()) + + +def measure(path: Path) -> dict[str, int]: + lines = python_lines(path) if path.suffix == ".py" else yaml_lines(path) + return {"lines": len(lines), "non_whitespace_characters": characters(lines)} + + +def add(left: dict[str, int], right: dict[str, int]) -> dict[str, int]: + return {key: left[key] + right[key] for key in left} + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dir", type=Path, default=Path(__file__).parent) + parser.add_argument("--out", type=Path, help="optional JSON output path") + args = parser.parse_args() + + files = { + "tablassert_table_yaml": args.dir / "tutorial-table.yaml", + "tablassert_graph_wrapper_yaml": args.dir / "tutorial-graph.yaml", + "koza_yaml": args.dir / "tutorial-koza.yaml", + "koza_transform_py": args.dir / "tutorial-transform.py", + } + counts = {name: measure(path) for name, path in files.items()} + counts["tablassert_total"] = add(counts["tablassert_table_yaml"], counts["tablassert_graph_wrapper_yaml"]) + counts["koza_total"] = add(counts["koza_yaml"], counts["koza_transform_py"]) + text = json.dumps(counts, indent=2) + "\n" + print(text, end="") + if args.out: + args.out.write_text(text, encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/paper/benchmarks/koza_comparison/loc.json b/paper/benchmarks/koza_comparison/loc.json new file mode 100644 index 00000000..6be1700f --- /dev/null +++ b/paper/benchmarks/koza_comparison/loc.json @@ -0,0 +1,26 @@ +{ + "tablassert_table_yaml": { + "lines": 32, + "non_whitespace_characters": 385 + }, + "tablassert_graph_wrapper_yaml": { + "lines": 24, + "non_whitespace_characters": 767 + }, + "koza_yaml": { + "lines": 25, + "non_whitespace_characters": 392 + }, + "koza_transform_py": { + "lines": 47, + "non_whitespace_characters": 1589 + }, + "tablassert_total": { + "lines": 56, + "non_whitespace_characters": 1152 + }, + "koza_total": { + "lines": 72, + "non_whitespace_characters": 1981 + } +} diff --git a/paper/benchmarks/koza_comparison/output-comparison.json b/paper/benchmarks/koza_comparison/output-comparison.json new file mode 100644 index 00000000..51a58c08 --- /dev/null +++ b/paper/benchmarks/koza_comparison/output-comparison.json @@ -0,0 +1,62 @@ +{ + "edge_subject_object_pairs_equal": true, + "unique_node_id_name_category_equal": true, + "tablassert": { + "edges": 5, + "unique_nodes": 9, + "rig": true, + "edge_fields": [ + "agent_type", + "category", + "has_supporting_studies", + "id", + "knowledge_level", + "object", + "original_object", + "original_subject", + "p_value", + "predicate", + "publications", + "sources", + "statistical_significance_qualifier", + "subject", + "supporting_text" + ] + }, + "koza": { + "edges": 5, + "node_rows": 10, + "unique_nodes": 9, + "edge_fields": [ + "object", + "p_value", + "predicate", + "primary_knowledge_source", + "publications", + "sample_size", + "subject" + ] + }, + "edge_pairs": [ + [ + "NCBIGene:1956", + "MONDO:0005575" + ], + [ + "NCBIGene:3845", + "MONDO:0005192" + ], + [ + "NCBIGene:3845", + "MONDO:0009831" + ], + [ + "NCBIGene:672", + "MONDO:0007254" + ], + [ + "NCBIGene:7157", + "MONDO:0008903" + ] + ] +} diff --git a/paper/benchmarks/koza_comparison/test_bench_koza.py b/paper/benchmarks/koza_comparison/test_bench_koza.py new file mode 100644 index 00000000..9ac72c12 --- /dev/null +++ b/paper/benchmarks/koza_comparison/test_bench_koza.py @@ -0,0 +1,60 @@ +"""Offline tests for the Koza configuration-size comparison.""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import pytest + +sys.path.insert(0, str(Path(__file__).parent)) + +import compare_outputs +from count_loc import count_python, count_yaml, measure + + +def test_count_python_excludes_docstrings_blanks_comments(tmp_path: Path) -> None: + source = tmp_path / "m.py" + source.write_text( + '"""Module docstring\nspans lines.\n"""\n\nimport os # noqa\n\n\ndef f():\n """Function docstring."""\n return 1\n\n\n# comment only line\nx = 2\n', + encoding="utf-8", + ) + assert count_python(source) == 4 + + +def test_count_yaml_excludes_blanks_and_comments(tmp_path: Path) -> None: + data = tmp_path / "c.yaml" + data.write_text("name: x\n\n# a comment\nreader:\n format: csv\n", encoding="utf-8") + assert count_yaml(data) == 3 + + +def test_measure_counts_non_whitespace_characters(tmp_path: Path) -> None: + data = tmp_path / "c.yaml" + data.write_text("name: x\n\n# ignored\nreader:\n format: csv\n", encoding="utf-8") + assert measure(data) == {"lines": 3, "non_whitespace_characters": 23} + + +@pytest.mark.parametrize("matched", [True, False]) +def test_compare_resolution_outputs(tmp_path: Path, monkeypatch: pytest.MonkeyPatch, matched: bool) -> None: + tab, koza = tmp_path / "tab", tmp_path / "koza" + tab.mkdir() + koza.mkdir() + (tab / "x.edges.ndjson").write_text(json.dumps({"subject": "X:1", "object": "X:2"}) + "\n") + (tab / "x.nodes.ndjson").write_text(json.dumps({"id": "X:1", "name": "one", "category": ["biolink:Gene"]}) + "\n") + (tab / "x.RIG.yaml").write_text("name: example\n") + obj = "X:2" if matched else "X:3" + (koza / "x_edges.tsv").write_text(f"subject\tobject\nX:1\t{obj}\n") + (koza / "x_nodes.tsv").write_text("id\tname\tcategory\nX:1\tone\tbiolink:Gene\n") + out = tmp_path / "result.json" + monkeypatch.setattr(sys, "argv", ["compare", "--tablassert", str(tab), "--koza", str(koza), "--out", str(out)]) + if matched: + compare_outputs.main() + result = json.loads(out.read_text()) + assert result["edge_subject_object_pairs_equal"] is True + assert result["unique_node_id_name_category_equal"] is True + assert result["tablassert"]["rig"] is True + else: + with pytest.raises(SystemExit, match="resolution outputs differ"): + compare_outputs.main() + assert not out.exists() diff --git a/paper/benchmarks/koza_comparison/tutorial-data.csv b/paper/benchmarks/koza_comparison/tutorial-data.csv new file mode 100644 index 00000000..a09bc8d8 --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-data.csv @@ -0,0 +1,5 @@ +gene_symbol,disease_name,p_value,sample_size +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 diff --git a/paper/benchmarks/koza_comparison/tutorial-graph.yaml b/paper/benchmarks/koza_comparison/tutorial-graph.yaml new file mode 100644 index 00000000..2473430e --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-graph.yaml @@ -0,0 +1,24 @@ +name: TUTORIAL_KG +version: 1.0.0 +tables: + - ./tutorial-table.yaml +fullmap: /local_raid1/sgoetz/DBSTORE/FULLMAP/fullmap +rig: + source_info: + infores_id: infores:tutorial-kg + name: Tutorial tabular source + description: Example source data used by the Tablassert tutorial graph. + terms_of_use_info: + license_name: CC0 1.0 Universal + license_url: https://creativecommons.org/publicdomain/zero/1.0/ + data_access_locations: + - Tutorial data - https://example.com/data.csv + source_status: unknown + ingest_info: + utility: Demonstrates how Tablassert turns configured tabular records into Translator-ready KGX. + scope: Gene-disease associations from the tutorial table, one edge per configured row. + provenance_info: + contributions: + - "Tutorial author: config author, data modeling" + artifact_base_url: https://example.com/tutorial-kg + artifact_base_path: ./tutorial-output diff --git a/paper/benchmarks/koza_comparison/tutorial-koza.yaml b/paper/benchmarks/koza_comparison/tutorial-koza.yaml new file mode 100644 index 00000000..bf3c8b3f --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-koza.yaml @@ -0,0 +1,29 @@ +name: tutorial-koza + +reader: + files: + - ./tutorial-data.csv + format: csv + delimiter: "," + +transform: + code: ./tutorial-transform.py + +writer: + format: tsv + node_properties: + - id + - name + - category + edge_properties: + - subject + - predicate + - object + - primary_knowledge_source + - publications + - p_value + - sample_size + +metadata: + name: Tutorial tabular source (Koza equivalent) + description: Same mapping as the Tablassert tutorial example, expressed as a Koza transform. diff --git a/paper/benchmarks/koza_comparison/tutorial-table.yaml b/paper/benchmarks/koza_comparison/tutorial-table.yaml new file mode 100644 index 00000000..d6de616f --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-table.yaml @@ -0,0 +1,32 @@ +template: + source: + kind: text + local: ./tutorial-data.csv + url: + - https://example.com/data.csv + row_slice: + - 1 + - auto + delimiter: "," + statement: + subject: + method: column + encoding: A + prioritize: + - Gene + predicate: associated_with + object: + method: column + encoding: B + prioritize: + - Disease + provenance: + repo: PMID + publication: "12345678" + annotations: + - annotation: p_value + method: column + encoding: C + - annotation: study_size + method: column + encoding: D diff --git a/paper/benchmarks/koza_comparison/tutorial-transform.py b/paper/benchmarks/koza_comparison/tutorial-transform.py new file mode 100644 index 00000000..e4f4ca94 --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-transform.py @@ -0,0 +1,64 @@ +"""Koza 2.7.0 transform for the Tablassert tutorial table. + +Entity resolution calls ``tablassert.lib.resolve_many`` with the recipe's column +priorities, against the fullmap configured in ``tutorial-graph.yaml``. +""" + +from dataclasses import dataclass, field +from pathlib import Path + +import koza + +from tablassert.biolink import Categories +from tablassert.lib import resolve_many + +FULLMAP = Path("/local_raid1/sgoetz/DBSTORE/FULLMAP/fullmap") +ASSOCIATION = "biolink:associated_with" + + +@dataclass +class NamedThing: + id: str + name: str + category: list[str] = field(default_factory=list) + + +@dataclass +class Association: + subject: str + predicate: str + object: str + primary_knowledge_source: str + publications: list[str] = field(default_factory=list) + p_value: float | None = None + sample_size: int | None = None + + +def resolve(col: str, values: list[str], category: Categories) -> dict[str, list[dict]]: + matches: dict[str, dict[str, dict]] = {} + for row in resolve_many(col, values, FULLMAP, taxon="9606", prioritize=[category]): + matches.setdefault(row[f"original_{col}"], {})[row[col]] = row + return {term: list(rows.values()) for term, rows in matches.items()} + + +@koza.transform() +def transform_rows(koza, data): + records = list(data) + genes = resolve("subject", [r["gene_symbol"] for r in records], Categories.GENE) + diseases = resolve("object", [r["disease_name"] for r in records], Categories.DISEASE) + for record in records: + for gene in genes.get(record["gene_symbol"], []): + for disease in diseases.get(record["disease_name"], []): + koza.write( + NamedThing(id=gene["subject"], name=gene["subject_name"], category=[gene["subject_category"]]), + NamedThing(id=disease["object"], name=disease["object_name"], category=[disease["object_category"]]), + Association( + subject=gene["subject"], + predicate=ASSOCIATION, + object=disease["object"], + primary_knowledge_source="infores:tutorial-kg", + publications=["PMID:12345678"], + p_value=float(record["p_value"]), + sample_size=int(record["sample_size"]), + ), + ) diff --git a/paper/benchmarks/name_resolution/README.md b/paper/benchmarks/name_resolution/README.md new file mode 100644 index 00000000..7e5ed65b --- /dev/null +++ b/paper/benchmarks/name_resolution/README.md @@ -0,0 +1,77 @@ +# Name resolution: local fullmap vs hosted SRI NameResolution + NodeNormalization + +Compares Tablassert's embedded fullmap database (batched local lookups through the +installed API: `level_one`/`level_two` normalization + `tablassert.fullmap.resolve_batch`) +against the hosted Translator flow on the same deterministic unique term list from +`terms.txt` (113 hand-selected input entries, 104 unique terms, deduplicated and +sorted). This is a convenience sample from ingest vocabulary, not a random or +representative sample; coverage and timing must not be generalized to accuracy or +to all biomedical terms: + +1. `GET https://name-resolution-sri.renci.org/lookup?string=&limit=5&only_taxa=NCBITaxon:9606` + per term for candidate CURIEs (concurrency <= 4, request budget <= 300 calls). + The matched run passes the pipe-separated `only_taxa=NCBITaxon:9606` filter on + every lookup; identical handling of taxon-free candidates is not established. +2. `POST https://nodenormalization-sri.renci.org/get_normalized_nodes` batched + (100 CURIEs per request) for canonical id, preferred name, and categories. + The response is keyed by the requested (input) CURIEs, not canonical ids, and + the harness groups results by requested candidate, preserving the /lookup + ranking. The `/synonyms` endpoint is NOT called; canonical records come from + NodeNormalization, the same service a downstream consumer would use. + +## What each run records + +- Per-term canonical record (id, preferred name, category, taxon) for BOTH sides; + local rows keep their input term via the `input_term` column, and ties appear as + multiple rows per input term. +- Coverage at three distinct levels, never collapsed into one number: + `http_ok` (HTTP 200), `nonempty_candidates` (200 AND >= 1 candidate), and + `complete_record` (TOP-RANKED candidate has canonical id + preferred name + category). + Failed requests become error records; there is no fake coverage. +- `api_spec` block: endpoints, methods, request shapes, batch size, retry and + concurrency parameters as actually called. + +## Documented mismatches (explicit, not silently papered over) + +- Taxon: `/lookup` DOES support server-side taxon filtering via the pipe-separated + `only_taxa` query parameter; the matched run passes `only_taxa=NCBITaxon:9606`. + Fullmap explicitly retains taxon-free rows; equivalence of the hosted service's + unknown-taxon handling is not established. Separately, the harness records + `human_pref`, a post-hoc preference for candidates whose NodeNormalization `taxa` + include NCBITaxon:9606 (taxa-free entries stay eligible), clearly labeled + post-filtering and kept distinct from `canonical` (the TOP-RANKED candidate's + record, never the first complete record in arbitrary order). +- Category policy: the hosted API supports Biolink-type filters, but those are not + equivalent to fullmap's per-column prioritize/avoid ranking and context rules. + This run enables no category filter or priority on either side. +- No column context: the local side runs with `column_context=False`, so neither + side sees table context in the per-term lookup. + +## Cold vs warm, repeats + +- Local cold: ONE fresh `local_runner` process per repeat (fresh interpreter, + empty term cache, fresh redb handles). The timer surrounds only + `resolve_batch(...).collect()`; interpreter startup, imports, and input parsing + are excluded. The OS file cache is NOT cleared, so this is not a cold-page-cache + measurement. +- Local warm: repeats back to back in one process. Both are reported separately + and never pooled; at least 3 repeats each. +- Hosted: one complete sweep, bounded request budget (default 300 HTTP attempts, + including retries), concurrency 4, retry with backoff. Logical calls and actual + HTTP attempts are reported separately; latency includes any retry/backoff. Lookup and batched-normalization times are recorded + separately and summed for end-to-end time; hosted latency is not a multi-run + median. Throughput includes both stages. + +`results-2026-10-28-v1.json` is the recorded run of the previous harness version +(per-term `/lookup` + `/synonyms`, no NodeNormalization stage, pooled coverage); +it is kept for provenance and is not comparable to the current harness. + +## Re-running + +```bash +python bench_name_res.py --terms terms.txt --fullmap /path/to/fullmap \ + --python /path/to/python-with-tablassert --out results.json \ + --local-repeats 3 --hosted-concurrency 4 --hosted-max-requests 300 --limit 5 +``` + +Offline tests (mocked network, no services contacted): `pytest test_bench_name_res.py`. diff --git a/paper/benchmarks/name_resolution/bench_name_res.py b/paper/benchmarks/name_resolution/bench_name_res.py new file mode 100644 index 00000000..853977bb --- /dev/null +++ b/paper/benchmarks/name_resolution/bench_name_res.py @@ -0,0 +1,473 @@ +"""Benchmark: local fullmap resolution vs hosted SRI NameResolution + NodeNormalization. + +Local side runs ``local_runner.py`` (the installed tablassert API: level_one/level_two +normalization + ``resolve_batch``). Hosted side is the two-stage Translator flow: + +1. ``GET https://name-resolution-sri.renci.org/lookup?string=`` per term + (concurrency <= 4, bounded call budget) for candidate CURIEs. +2. ``POST https://nodenormalization-sri.renci.org/get_normalized_nodes`` batched + (bulk flow, one request per chunk of CURIEs) for canonical id, preferred name + and category. The old ``/synonyms`` endpoint is NOT used: canonical records come + from NodeNormalization, the same service a downstream consumer would call. + +Coverage is reported honestly at three distinct levels, never collapsed into one: + +- ``http_ok``: the request returned HTTP 200. +- ``nonempty_candidates``: HTTP 200 AND at least one candidate CURIE returned. +- ``complete_record``: the top-ranked candidate's NodeNormalization record has a + canonical identifier, label, and at least one Biolink category. + +Known, documented mismatches (neither side is secretly adjusted to fake parity): + +- Taxon: /lookup DOES support server-side taxon filtering via the pipe-separated + ``only_taxa`` query parameter; the matched run passes ``only_taxa=NCBITaxon:9606`` + on every lookup. This does not prove identical handling of candidates without + taxon metadata; fullmap explicitly retains such rows. On top of that, the harness records a post-hoc human-taxon preference + (``human_pref``) over the ranked candidate records, kept explicitly separate from + ``canonical`` and clearly labeled as post-filtering. +- Category policy: the hosted API supports Biolink-type filters, but those are not + equivalent to fullmap's per-column prioritize/avoid ranking and context rules. + This run enables no category filter or priority on either side. +- No column context is used on the local side either (``column_context=False``), + so both sides resolve terms out of table context. + +Usage (on the build host): + + python bench_name_res.py --terms terms.txt --fullmap /path/to/fullmap \ + --python /path/to/python --out results.json --local-repeats 3 \ + --hosted-concurrency 4 --hosted-max-requests 300 --limit 5 + +Writes results.json with an ``api_spec`` block and separate local-run JSON files. +""" + +from __future__ import annotations + +import argparse +import json +import math +import platform +import statistics +import subprocess +import time +import urllib.error +import urllib.parse +import urllib.request +from concurrent.futures import ThreadPoolExecutor, as_completed +from importlib.metadata import version +from pathlib import Path +from threading import Lock +from typing import TypedDict, cast + +LOOKUP_BASE = "https://name-resolution-sri.renci.org" +NODENORM_BASE = "https://nodenormalization-sri.renci.org" +RETRYABLE = frozenset({429, 500, 502, 503, 504}) +USER_AGENT = "tablassert-benchmark/2.0" +HUMAN_TAXON = "NCBITaxon:9606" +HUMAN_TAXON_ID = "9606" + + +# ---------------------------------------------------------------- requests + + +class LookupRecord(TypedDict): + """Outcome of one stage-1 /lookup call.""" + + term: str + http_status: int | None + error: str | None + curies: list[str] + latency_s: float | None + + +class NormalizeResult(TypedDict): + """Outcome of the batched NodeNormalization stage.""" + + normalized: dict[str, dict[str, object]] + http_calls: int + error: str | None + + +def fetch_json(req: urllib.request.Request, timeout: float = 30.0, attempts: int = 3, budget: RequestBudget | None = None) -> tuple[int, object]: + """One HTTP request, retried with linear backoff on transient errors. + + Returns (status, parsed body). Raises the last error when retries are exhausted; + callers turn exceptions into failure records, never silent coverage. + """ + last: Exception | None = None + for attempt in range(attempts): + if budget is not None: + budget.spend() + try: + with urllib.request.urlopen(req, timeout=timeout) as resp: + return resp.status, json.loads(resp.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + last = exc + if exc.code in RETRYABLE and attempt < attempts - 1: + time.sleep(2.0 * (attempt + 1)) + continue + raise + except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: + last = exc + if attempt < attempts - 1: + time.sleep(2.0 * (attempt + 1)) + continue + raise + raise last if last else RuntimeError("unreachable") + + +class BudgetExhausted(RuntimeError): + """The next HTTP attempt would exceed the configured budget.""" + + +class RequestBudget: + """Thread-safe hard cap on all HTTP attempts, including retries.""" + + def __init__(self, max_requests: int) -> None: + self.remaining = max_requests + self._lock = Lock() + + def spend(self, n: int = 1) -> None: + with self._lock: + if n > self.remaining: + msg = f"hosted request budget exhausted: needed {n}, remaining {self.remaining}" + raise BudgetExhausted(msg) + self.remaining -= n + + +# ---------------------------------------------------------------- hosted flow + + +def lookup_one(term: str, limit: int, budget: RequestBudget, only_taxa: str | None = HUMAN_TAXON) -> LookupRecord: + """Stage 1: GET /lookup for one term. Never raises; failures become records. + + ``only_taxa`` uses the service's pipe-separated taxon filter format; the + default NCBITaxon:9606 makes the hosted run taxon-matched to fullmap. + """ + record: LookupRecord = {"term": term, "http_status": None, "error": None, "curies": [], "latency_s": None} + params: dict[str, str] = {"string": term, "limit": str(limit)} + if only_taxa: + params["only_taxa"] = only_taxa + url = f"{LOOKUP_BASE}/lookup?{urllib.parse.urlencode(params)}" + start = time.perf_counter() + try: + req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) + status, body = fetch_json(req, budget=budget) + record["http_status"] = status + hits = body if isinstance(body, list) else [] + curies: list[str] = [] + for hit in hits: + if isinstance(hit, dict): + curie = hit.get("curie") + if isinstance(curie, str): + curies.append(curie) + record["curies"] = curies + except BudgetExhausted: + raise + except Exception as exc: + record["error"] = f"{type(exc).__name__}: {exc}" + finally: + record["latency_s"] = time.perf_counter() - start + return record + + +def normalize_batch(curies: list[str], budget: RequestBudget, batch_size: int = 100) -> NormalizeResult: + """Stage 2: POST /get_normalized_nodes over all candidate CURIEs in chunks (bulk flow). + + Returns {"normalized": {curie: {...}}, "http_calls": n, "error": str | None}. + """ + normalized: dict[str, dict[str, object]] = {} + calls = 0 + error: str | None = None + for start in range(0, len(curies), batch_size): + chunk = curies[start : start + batch_size] + try: + calls += 1 + payload = json.dumps({"curies": chunk}).encode("utf-8") + req = urllib.request.Request( + f"{NODENORM_BASE}/get_normalized_nodes", data=payload, headers={"User-Agent": USER_AGENT, "Content-Type": "application/json"} + ) + _, body = fetch_json(req, budget=budget) + normalized.update(body if isinstance(body, dict) else {}) + except BudgetExhausted: + raise + except Exception as exc: + error = f"{type(exc).__name__}: {exc}" + result: NormalizeResult = {"normalized": normalized, "http_calls": calls, "error": error} + return result + + +def canonical_record(entry: object) -> dict[str, object] | None: + """Extract {id, preferred_name, categories} from one NodeNormalization entry. + + ``complete_record`` requires all three fields non-empty. + """ + if not isinstance(entry, dict): + return None + ident = entry.get("id") + if not isinstance(ident, dict): + return None + curie = ident.get("identifier") + label = ident.get("label") + raw_types = entry.get("type") + types = [t for t in raw_types if isinstance(t, str)] if isinstance(raw_types, list) else [] + if not curie or not label or not types: + return None + return {"id": curie, "preferred_name": label, "categories": types, "taxa": entry.get("taxa")} + + +def prefers_human(entry: object) -> bool: + """Post-hoc human-taxon preference: keep taxa-bearing entries only if human is present. + + Entries without taxa information stay eligible. This is a documented post-filter + on top of /lookup output, NOT equivalent to fullmap's in-database taxon constraint. + """ + if entry is None or not isinstance(entry, dict): + return False + taxa = entry.get("taxa") + if not isinstance(taxa, list) or not taxa: + return True + return HUMAN_TAXON in taxa + + +def group_normalized_by_requested(requested: list[str], normalized: dict[str, object]) -> dict[str, dict[str, object] | None]: + """Group NodeNormalization results by requested candidate CURIE. + + The service keys its response by the requested (input) CURIEs, not canonical + ids, so records are resolved per requested candidate, preserving the /lookup + ranking. As a robustness fallback for canonical-keyed responses, entries are + additionally indexed by canonical identifier and equivalent identifiers. A + record is never looked up by canonical id alone. + """ + by_identity: dict[str, dict[str, object]] = {} + for key, entry in normalized.items(): + if not isinstance(entry, dict): + continue + by_identity.setdefault(key, entry) + record = canonical_record(entry) + if record is None: + continue + ident = record["id"] + if isinstance(ident, str): + by_identity.setdefault(ident, entry) + equivalents = entry.get("equivalent_identifiers") + if isinstance(equivalents, list): + for eq in equivalents: + if isinstance(eq, dict) and isinstance(eq.get("identifier"), str): + by_identity.setdefault(eq["identifier"], entry) + return {curie: by_identity.get(curie) for curie in requested} + + +def bench_hosted(terms: list[str], concurrency: int, limit: int, budget: RequestBudget) -> dict[str, object]: + """Full hosted flow over ``terms``: per-term /lookup, then batched NodeNormalization.""" + expected_calls = len(terms) + math.ceil(len(terms) * limit / 100) + if budget.remaining < expected_calls: + raise BudgetExhausted(f"hosted request budget needs at least {expected_calls} calls before retries") + wall_start = time.perf_counter() + lookup_attempts_start = budget.remaining + with ThreadPoolExecutor(max_workers=concurrency) as pool: + futures = {pool.submit(lookup_one, term, limit, budget): term for term in terms} + lookups: list[LookupRecord] = [future.result() for future in as_completed(futures)] + lookups.sort(key=lambda result: result["term"]) + lookup_wall = time.perf_counter() - wall_start + lookup_attempts = lookup_attempts_start - budget.remaining + + all_curies = sorted({curie for rec in lookups for curie in rec["curies"]}) + norm_start = time.perf_counter() + norm_attempts_start = budget.remaining + norm = normalize_batch(all_curies, budget) + norm_wall = time.perf_counter() - norm_start + grouped = group_normalized_by_requested(all_curies, cast("dict[str, object]", norm["normalized"])) + + records: list[dict[str, object]] = [] + for rec in lookups: + curies = rec["curies"] + # /lookup returns candidates ranked by relevance; keep that ranking and + # resolve every candidate's record through the requested-CURIE grouping. + ranked = [(curie, canonical_record(grouped.get(curie))) for curie in curies] + top = ranked[0][1] if ranked else None + # canonical result: the TOP-RANKED candidate's record, never the first + # complete one found by arbitrary scan order. + human = next((entry for _, entry in ranked if entry is not None and prefers_human(entry)), None) + records.append( + { + "term": rec["term"], + "http_status": rec["http_status"], + "error": rec["error"], + "latency_s": rec["latency_s"], + "candidate_curies": curies, + "candidate_records": [{"curie": curie, "canonical": entry} for curie, entry in ranked], + # canonical id + preferred name + categories of the top-ranked candidate + "canonical": top, + # post-hoc human-taxon preference over the ranked records, + # documented as post-filtering and kept separate from canonical + "human_pref": human, + } + ) + + http_ok = sum(1 for r in records if r["http_status"] == 200) + nonempty = sum(1 for r in records if r["candidate_curies"]) + # complete_record: the TOP-RANKED candidate produced a complete canonical record + complete = sum(1 for r in records if r["canonical"]) + human_ok = sum(1 for r in records if r["human_pref"]) + lat: list[float] = [] + for rec in records: + value = rec.get("latency_s") + if isinstance(value, float) and rec.get("error") is None: + lat.append(value) + return { + "repeats": 1, + "concurrency": concurrency, + "lookup_limit": limit, + "wall_s": lookup_wall + norm_wall, + "lookup_wall_s": lookup_wall, + "normalization_wall_s": norm_wall, + "http_calls": {"lookup": len(terms), "get_normalized_nodes": norm["http_calls"]}, + "http_attempts_including_retries": {"lookup": lookup_attempts, "get_normalized_nodes": norm_attempts_start - budget.remaining}, + "coverage": { + # three distinct levels; never collapsed into one number + "http_ok": http_ok, + "nonempty_candidates": nonempty, + "complete_record": complete, + "complete_record_human_pref": human_ok, + "n_terms": len(records), + }, + "latency_s": {"median": statistics.median(lat) if lat else None, "mean": statistics.mean(lat) if lat else None}, + "lookup_terms_per_s": len(records) / lookup_wall if lookup_wall else None, + "terms_per_s_effective": len(records) / (lookup_wall + norm_wall) if lookup_wall + norm_wall else None, + "normalization_error": norm["error"], + "records": records, + } + + +# ---------------------------------------------------------------- local + main + + +def bench_local(python: str, fullmap: Path, terms: list[str], repeats: int, out_dir: Path) -> dict[str, object]: + """Cold: launch ONE fresh local_runner process per repeat; warm: one process, repeated. + + Each cold repeat is its own process (fresh interpreter, empty term cache, fresh + redb handles), so cold is not a repeated in-process measurement. Warm runs + ``repeats`` passes back to back inside a single process. The OS file cache is + NOT cleared, so cold measures process-level first touch only; that limitation + is recorded here, never claimed away. + """ + runner = Path(__file__).with_name("local_runner.py") + + def spawn(mode: str, run_repeats: int, out: Path) -> dict[str, object]: + argv = [python, str(runner), "--terms", "-", "--fullmap", str(fullmap), "--mode", mode, "--repeats", str(run_repeats), "--out", str(out)] + proc = subprocess.run(argv, input="\n".join(terms) + "\n", capture_output=True, text=True, check=False) + if proc.returncode != 0: + msg = f"local runner ({mode}) exited {proc.returncode}: {proc.stderr[-800:]}" + raise RuntimeError(msg) + return json.loads(out.read_text()) + + cold_files = [out_dir / f"local_cold_run{i}.json" for i in range(repeats)] + cold_runs = [spawn("cold", 1, out) for out in cold_files] + cold_wall = [float(cast("float", run["wall_s_median"])) for run in cold_runs] + median = statistics.median(cold_wall) + median_idx = cold_wall.index(median) + cold: dict[str, object] = { + "repeats": repeats, + "fresh_process_per_repeat": True, + "os_cache_cleared": False, + "wall_s": cold_wall, + "wall_s_median": median, + "unique_terms_per_s_median": len(terms) / median if median else None, + "per_unique_term_ms_median": median / len(terms) * 1000, + "resolved_rows_per_run": [cast("list[int]", run["resolved_rows_per_run"])[0] for run in cold_runs], + "resolved_terms_per_run": [cast("int", run["resolved_terms_per_run"]) for run in cold_runs], + # rows of the median cold process + "records": cold_runs[median_idx]["records"], + "run_files": [path.name for path in cold_files], + "note": ( + "Each cold repeat is a separate local_runner process. The timer surrounds " + "resolve_batch collection only and excludes interpreter startup, imports, " + "and input parsing. The OS file cache is not cleared, so this is not a " + "cold-page-cache measurement." + ), + } + return {"cold": cold, "warm": spawn("warm", repeats, out_dir / "local_warm.json")} + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--terms", type=Path, required=True) + parser.add_argument("--fullmap", type=Path, required=True) + parser.add_argument("--python", required=True, help="python with tablassert importable") + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--local-repeats", type=int, default=3) + parser.add_argument("--hosted-concurrency", type=int, default=4) + parser.add_argument("--hosted-max-requests", type=int, default=300) + parser.add_argument("--limit", type=int, default=5) + parser.add_argument("--skip-hosted", action="store_true") + parser.add_argument("--skip-local", action="store_true") + args = parser.parse_args() + + input_terms = [line.strip() for line in args.terms.read_text().splitlines() if line.strip() and not line.strip().startswith("#")] + terms = sorted(set(input_terms)) + budget = RequestBudget(args.hosted_max_requests) + + result: dict[str, object] = { + "date_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "tablassert_version": version("tablassert") if not args.skip_local else None, + "python_version": platform.python_version(), + "platform": platform.platform(), + "fullmap": str(args.fullmap), + "n_input_rows": len(input_terms), + "n_unique_terms": len(terms), + "sample_selection": "hand-selected ingest vocabulary; not a random or representative accuracy sample", + "unique_terms_deterministic": True, + "terms": terms, + "hosted_request_budget": args.hosted_max_requests, + } + if not args.skip_local: + result["local"] = bench_local(args.python, args.fullmap, terms, args.local_repeats, args.out.parent) + if not args.skip_hosted: + result["hosted"] = bench_hosted(terms, args.hosted_concurrency, args.limit, budget) + result["hosted_requests_remaining"] = budget.remaining + result["api_spec"] = api_spec(args.limit) + + args.out.write_text(json.dumps(result, indent=2)) + print(f"wrote {args.out}; hosted budget remaining: {budget.remaining}") + + +def api_spec(limit: int = 5) -> dict[str, object]: + """Saved parameters of both hosted endpoints as called by this harness.""" + return { + "lookup": { + "base": LOOKUP_BASE, + "method": "GET", + "path": "/lookup", + "params": { + "string": "free-text term (URL-quoted)", + "limit": "max candidates returned", + "only_taxa": "pipe-separated taxon filter, e.g. NCBITaxon:9606", + "biolink_type": "query param filtering candidates to biolink types", + "only_prefixes": "pipe-separated prefix filter (not used here)", + "exclude_prefixes": "pipe-separated prefix exclusion (not used here)", + }, + "called_with": {"limit": limit, "only_taxa": HUMAN_TAXON, "retry": "3 attempts, linear backoff 2s/4s", "retries_on": sorted(RETRYABLE)}, + "taxon_filter": f"only_taxa={HUMAN_TAXON} passed on every lookup (matched run)", + "unknown_taxa_handling": ("fullmap retains taxon-free rows; equivalence of hosted unknown-taxon handling is not established"), + "bulk": "POST /bulk-lookup supports biolink_types in the request body; this harness uses GET per term", + }, + "node_normalization": { + "base": NODENORM_BASE, + "method": "POST", + "path": "/get_normalized_nodes", + "params": {"curies": 'JSON body {"curies": [...]}, batched 100 per request'}, + "returns": "per-CURIE {id:{identifier,label}, type:[...], equivalent_identifiers, taxa, information_content}", + "batch_size": 100, + "response_keying": "response is keyed by the requested (input) CURIEs, not canonical ids; results are grouped by requested candidate via group_normalized_by_requested", + "synonyms_endpoint": "not used; canonical records come from get_normalized_nodes", + }, + "local_fullmap": { + "api": "tablassert.fullmap.resolve_batch + tablassert.nlp.level_one/level_two", + "column_context": False, + "taxon": HUMAN_TAXON_ID, + }, + } + + +if __name__ == "__main__": + main() diff --git a/paper/benchmarks/name_resolution/local_cold_run0.json b/paper/benchmarks/name_resolution/local_cold_run0.json new file mode 100644 index 00000000..3a558150 --- /dev/null +++ b/paper/benchmarks/name_resolution/local_cold_run0.json @@ -0,0 +1,1879 @@ +{ + "date_utc": "2026-10-29T20:17:22Z", + "tablassert_version": "19.7.0", + "polars_version": "2.0.0rc2", + "python_version": "3.12.3", + "platform": "Linux-6.8.0-142-generic-x86_64-with-glibc2.39", + "fullmap": "/local_raid1/sgoetz/DBSTORE/FULLMAP/fullmap", + "mode": "cold", + "resolve_policy": { + "taxon": "9606", + "column_context": false, + "prioritize": null, + "avoid": null, + "note": "per-term lookup: no column context, single taxon=9606 constraint" + }, + "repeats": 1, + "n_terms": 104, + "wall_s": [ + 0.8903551637195051 + ], + "wall_s_median": 0.8903551637195051, + "unique_terms_per_s_median": 116.80731941345135, + "per_unique_term_ms_median": 8.56110734345678, + "resolved_rows_per_run": [ + 264 + ], + "resolved_terms_per_run": 93, + "records": [ + { + "input_term": "ADIPOQ", + "canonical_id": "NCBIGene:9370", + "preferred_name": "ADIPOQ", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "AMPYRA", + "canonical_id": "UMLS:C2732151", + "preferred_name": "Ampyra", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "APOE", + "canonical_id": "FB:FBgn0004472", + "preferred_name": "apo", + "category": "biolink:Gene", + "taxon": null + }, + { + "input_term": "APOE", + "canonical_id": "ZFIN:ZDB-GENE-070117-2098", + "preferred_name": "apo", + "category": "biolink:Gene", + "taxon": null + }, + { + "input_term": "AUBAGIO", + "canonical_id": "UMLS:C3497721", + "preferred_name": "Aubagio", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "AVANDIA", + "canonical_id": "UMLS:C0875967", + "preferred_name": "Avandia", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Acne", + "canonical_id": "MONDO:0011438", + "preferred_name": "acne", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alistipes", + "canonical_id": "NCBITaxon:239759", + "preferred_name": "Alistipes", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Alistipes", + "canonical_id": "UMLS:C1490417", + "preferred_name": "Alistipes", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "MONDO:0004975", + "preferred_name": "Alzheimer disease", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "MONDO:0004975", + "preferred_name": "Alzheimer disease", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "NCBIGene:351", + "preferred_name": "APP", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "NCBIGene:351", + "preferred_name": "APP", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "BRCA1", + "canonical_id": "NCBIGene:672", + "preferred_name": "BRCA1", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "BRCA2", + "canonical_id": "NCBIGene:675", + "preferred_name": "BRCA2", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Bifidobacterium", + "canonical_id": "NCBITaxon:1678", + "preferred_name": "Bifidobacterium", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Breast cancer", + "canonical_id": "MONDO:0007254", + "preferred_name": "breast cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Breast cancer", + "canonical_id": "MONDO:0007254", + "preferred_name": "breast cancer", + "category": "biolink:Disease", + "taxon": null + }, + { 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"preferred_name": "D-glucose", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "glucose", + "canonical_id": "CHEBI:4167", + "preferred_name": "D-glucopyranose", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "glucose", + "canonical_id": "EFO:0004468", + "preferred_name": "glucose measurement", + "category": "biolink:PhenotypicFeature", + "taxon": null + }, + { + "input_term": "glucose", + "canonical_id": "PUBCHEM.COMPOUND:24749", + "preferred_name": "L-glucose", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "glucose", + "canonical_id": "UMLS:C0337438", + "preferred_name": "Glucose measurement", + "category": "biolink:Procedure", + "taxon": null + }, + { + "input_term": "glucose", + "canonical_id": "UMLS:C5781949", + "preferred_name": "Glucose^1.5H post dose glucagon", + "category": "biolink:ClinicalFinding", + "taxon": null + }, + { + "input_term": "hepcidin", + "canonical_id": "PR:000008438", + "preferred_name": "hepcidin", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "hepcidin", + "canonical_id": "UMLS:C0966897", + "preferred_name": "hepcidin", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "ibuprofen", + "canonical_id": "CHEBI:5855", + "preferred_name": "Ibuprofen", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "ibuprofen", + "canonical_id": "PUBCHEM.COMPOUND:18943906", + "preferred_name": "Ibuprofen Ibuprofen Ibuprofen", + "category": "biolink:MolecularMixture", + "taxon": null + }, + { + "input_term": "ibuprofen", + "canonical_id": "UNII:ALC8A4Q82T", + "preferred_name": "IBUPROFENAL", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "inflammatory bowel disease", + "canonical_id": "MONDO:0005265", + "preferred_name": "inflammatory bowel disease", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "insulin", + "canonical_id": "PANTHER.FAMILY:PTHR11454:SF9", + "preferred_name": "INSULIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "insulin", + "canonical_id": "PUBCHEM.COMPOUND:70678557", + "preferred_name": "Insulin", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "insulin", + "canonical_id": "UMLS:C0021641", + "preferred_name": "Insulin", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "insulin", + "canonical_id": "UMLS:C3537244", + "preferred_name": "Insulins", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "PR:000001393", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "interleukin-6", + "canonical_id": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "iron", + "canonical_id": "CHEBI:18248", + "preferred_name": "Iron", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "iron", + "canonical_id": "UMLS:C0402709", + "preferred_name": "Ironer", + "category": "biolink:PopulationOfIndividualOrganisms", + "taxon": null + }, + { + "input_term": "iron", + "canonical_id": "UMLS:C1567140", + "preferred_name": "Irones", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "lisinopril", + "canonical_id": "PUBCHEM.COMPOUND:118984969", + "preferred_name": "lisinopril", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "lisinopril", + "canonical_id": "UMLS:C0065374", + "preferred_name": "lisinopril", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "lung cancer", + "canonical_id": "MONDO:0008903", + "preferred_name": "lung cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "lung cancer", + "canonical_id": "MONDO:0008903", + "preferred_name": "lung cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "lung cancer", + "canonical_id": "MONDO:0008903", + "preferred_name": "lung cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "metformin", + "canonical_id": "CHEBI:6801", + "preferred_name": "Metformin", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "non-alcoholic fatty liver disease", + "canonical_id": "MONDO:0013209", + "preferred_name": "metabolic dysfunction-associated steatotic liver disease", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "probiotics", + "canonical_id": "MESH:D019936", + "preferred_name": "Probiotics", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "propionic acid", + "canonical_id": "UMLS:C0033482", + "preferred_name": "Propionic Acids", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "propionic acid", + "canonical_id": "UMLS:C0033482", + "preferred_name": "Propionic Acids", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "rheumatoid arthritis", + "canonical_id": "MONDO:0008383", + "preferred_name": "rheumatoid arthritis", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "systemic lupus erythematosus", + "canonical_id": "MONDO:0007915", + "preferred_name": "systemic lupus erythematosus", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "triglycerides", + "canonical_id": "CHEBI:17855", + "preferred_name": "triglyceride", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "trimethoprim", + "canonical_id": "CHEBI:45924", + "preferred_name": "Trimethoprim", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "tryptophan", + "canonical_id": "CHEBI:27897", + "preferred_name": "tryptophan", + "category": "biolink:SmallMolecule", + "taxon": null + }, + { + "input_term": "tryptophan", + "canonical_id": "UMLS:C0041249", + "preferred_name": "tryptophan", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PANTHER.FAMILY:PTHR11471:SF23", + "preferred_name": "TUMOR NECROSIS FACTOR", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "PR:000000134", + "preferred_name": "tumor necrosis factor", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "tumor necrosis factor", + "canonical_id": "UMLS:C0041368", + "preferred_name": "Tumor Necrosis Factors", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "type 2 diabetes", + "canonical_id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UMLS:C3537249", + "preferred_name": "D Vitamin", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "vitamin D", + "canonical_id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "category": "biolink:ChemicalEntity", + "taxon": null + } + ], + "note": "resolve_batch dedups terms before the redb fetch; throughput is over unique terms. For cold, the harness launches one local_runner process per repeat; warm repeats share one process. The OS file cache is not cleared. Ties resolve to multiple rows per input term." +} \ No newline at end of file diff --git a/paper/benchmarks/name_resolution/local_runner.py b/paper/benchmarks/name_resolution/local_runner.py new file mode 100644 index 00000000..3119aecc --- /dev/null +++ b/paper/benchmarks/name_resolution/local_runner.py @@ -0,0 +1,158 @@ +"""Standalone local fullmap runner for the name-resolution benchmark. + +Uses the installed tablassert API exactly as a build does: ``level_one`` / +``level_two`` normalization followed by ``tablassert.fullmap.resolve_batch``. +No code is injected as a string; this file is the measured code. + +One input row is kept per original term via the ``input_term`` column, which +survives ``resolve_batch``'s join untouched, so every resolved row (including +ties, which resolve to multiple rows) still tracks its input term. + +Usage: + +Cold mode measures ONE pass in this process; the benchmark harness +(``bench_name_res.py``) launches one fresh ``local_runner`` process per cold +repeat, so cold repeats are genuinely separate processes. Warm mode runs +``--repeats`` passes back to back in a single process. Neither mode clears the +OS file cache; cold measures process-level first touch only. + + python local_runner.py --terms terms.txt --fullmap /path/to/fullmap \ + --mode cold --repeats 1 --out local_cold_run0.json + python local_runner.py --terms terms.txt --fullmap /path/to/fullmap \ + --mode warm --repeats 3 --out local_warm.json + +``--terms -`` reads the term list from stdin (one term per line). +""" + +from __future__ import annotations + +import argparse +import json +import platform +import statistics +import sys +import time +from importlib.metadata import version +from pathlib import Path +from typing import cast + + +def read_terms(path: Path) -> list[str]: + # "-" means stdin: the benchmark driver pipes the term list through it + lines = sys.stdin.read().splitlines() if str(path) == "-" else path.read_text(encoding="utf-8").splitlines() + terms: list[str] = [] + for line in lines: + term = line.strip() + if term and not term.startswith("#"): + terms.append(term) + return terms + + +def unique_terms_deterministic(terms: list[str]) -> list[str]: + """Deduplicate terms deterministically: sorted unique, stable across runs.""" + return sorted(set(terms)) + + +def run_once(terms: list[str], fullmap: Path) -> tuple[float, list[dict[str, object]]]: + """One timed ``resolve_batch`` pass over ``terms``; returns (seconds, per-term records).""" + import polars as pl + + from tablassert.fullmap import ResolveSpec, resolve_batch + from tablassert.nlp import level_one, level_two + + # input_term preserves the raw string; term is consumed (normalized, then + # overwritten with the resolved CURIE) by the resolve op chain. + lf = pl.LazyFrame({"input_term": terms, "term": terms}) + lf = level_two(level_one(lf, "term"), "term") + spec = ResolveSpec(col="term", taxon="9606") + start = time.perf_counter() + out = resolve_batch(lf, [spec], db=fullmap, log=False, column_context=False).collect() + elapsed = time.perf_counter() - start + records: list[dict[str, object]] = out.select( + "input_term", + pl.col("term").alias("canonical_id"), + pl.col("term_name").alias("preferred_name"), + pl.col("term_category").alias("category"), + pl.col("term_taxon").alias("taxon"), + ).to_dicts() + return elapsed, records + + +def measure(terms: list[str], fullmap: Path, mode: str, repeats: int) -> dict[str, object]: + """Cold mode: ``repeats`` passes here; the harness launches one process per pass. + + Each cold pass runs in its own ``local_runner`` invocation. Warm mode runs + ``repeats`` passes back to back in one process (term cache and redb handles + stay hot). The timer surrounds only ``resolve_batch(...).collect()``: process + startup, imports, and input parsing are excluded. The OS file cache is not + cleared by either mode. The modes are reported separately, never pooled. + """ + runs: list[float] = [] + records_by_run: list[list[dict[str, object]]] = [] + for _ in range(repeats): + elapsed, records = run_once(terms, fullmap) + if not records: + raise RuntimeError("local resolution returned no rows; refusing to report throughput") + runs.append(elapsed) + records_by_run.append(records) + resolved = [len(records) for records in records_by_run] + median_idx = runs.index(statistics.median(runs)) + resolved_rows = records_by_run[median_idx] + resolved_terms = {record["input_term"] for record in resolved_rows} + return { + "mode": mode, + "repeats": repeats, + "n_terms": len(terms), + "wall_s": runs, + "wall_s_median": statistics.median(runs), + "unique_terms_per_s_median": len(terms) / statistics.median(runs), + "per_unique_term_ms_median": statistics.median(runs) / len(terms) * 1000, + "resolved_rows_per_run": resolved, + "resolved_terms_per_run": len(resolved_terms), + # Rows from the median run: canonical id, preferred name, category, taxon per + # input term (ties appear as multiple rows for one input term). + "records": resolved_rows, + "note": ( + "resolve_batch dedups terms before the redb fetch; throughput is over unique " + "terms. For cold, the harness launches one local_runner process per repeat; " + "warm repeats share one process. The OS file cache is not cleared. Ties " + "resolve to multiple rows per input term." + ), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--terms", type=Path, required=True) + parser.add_argument("--fullmap", type=Path, required=True) + parser.add_argument("--mode", choices=("cold", "warm"), required=True) + parser.add_argument("--repeats", type=int, default=3) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + + terms = unique_terms_deterministic(read_terms(args.terms)) + started = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) + result: dict[str, object] = { + "date_utc": started, + "tablassert_version": version("tablassert"), + "polars_version": version("polars"), + "python_version": platform.python_version(), + "platform": platform.platform(), + "fullmap": str(args.fullmap), + "mode": args.mode, + "resolve_policy": { + "taxon": "9606", + "column_context": False, + "prioritize": None, + "avoid": None, + "note": "per-term lookup: no column context, single taxon=9606 constraint", + }, + **measure(terms, args.fullmap, args.mode, args.repeats), + } + args.out.write_text(json.dumps(result, indent=2)) + median = cast("float", result["wall_s_median"]) + print(f"{args.mode}: median {median:.3f}s over {len(terms)} unique terms; wrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/paper/benchmarks/name_resolution/local_warm.json b/paper/benchmarks/name_resolution/local_warm.json new file mode 100644 index 00000000..ddba20ae --- /dev/null +++ b/paper/benchmarks/name_resolution/local_warm.json @@ -0,0 +1,1883 @@ +{ + "date_utc": "2026-10-29T20:17:39Z", + "tablassert_version": "19.7.0", + "polars_version": "2.0.0rc2", + "python_version": "3.12.3", + "platform": "Linux-6.8.0-142-generic-x86_64-with-glibc2.39", + "fullmap": "/local_raid1/sgoetz/DBSTORE/FULLMAP/fullmap", + "mode": "warm", + "resolve_policy": { + "taxon": "9606", + "column_context": false, + "prioritize": null, + "avoid": null, + "note": "per-term lookup: no column context, single taxon=9606 constraint" + }, + "repeats": 3, + "n_terms": 104, + "wall_s": [ + 0.8844683738425374, + 0.5151434866711497, + 0.5156950671225786 + ], + "wall_s_median": 0.5156950671225786, + "unique_terms_per_s_median": 201.66956527292052, + "per_unique_term_ms_median": 4.958606414640179, + "resolved_rows_per_run": [ + 264, + 264, + 264 + ], + "resolved_terms_per_run": 93, + "records": [ + { + "input_term": "ADIPOQ", + "canonical_id": "NCBIGene:9370", + "preferred_name": "ADIPOQ", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "AMPYRA", + "canonical_id": "UMLS:C2732151", + "preferred_name": "Ampyra", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "APOE", + "canonical_id": "FB:FBgn0004472", + "preferred_name": "apo", + "category": "biolink:Gene", + "taxon": null + }, + { + "input_term": "APOE", + "canonical_id": "ZFIN:ZDB-GENE-070117-2098", + "preferred_name": "apo", + "category": "biolink:Gene", + "taxon": null + }, + { + "input_term": "AUBAGIO", + "canonical_id": "UMLS:C3497721", + "preferred_name": "Aubagio", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "AVANDIA", + "canonical_id": "UMLS:C0875967", + "preferred_name": "Avandia", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Acne", + "canonical_id": "MONDO:0011438", + "preferred_name": "acne", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alistipes", + "canonical_id": "NCBITaxon:239759", + "preferred_name": "Alistipes", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Alistipes", + "canonical_id": "UMLS:C1490417", + "preferred_name": "Alistipes", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "MONDO:0004975", + "preferred_name": "Alzheimer disease", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "MONDO:0004975", + "preferred_name": "Alzheimer disease", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "NCBIGene:351", + "preferred_name": "APP", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Alzheimer disease", + "canonical_id": "NCBIGene:351", + "preferred_name": "APP", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Atrial fibrillation", + "canonical_id": "MONDO:0004981", + "preferred_name": "atrial fibrillation", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "BRCA1", + "canonical_id": "NCBIGene:672", + "preferred_name": "BRCA1", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "BRCA2", + "canonical_id": "NCBIGene:675", + "preferred_name": "BRCA2", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Bifidobacterium", + "canonical_id": "NCBITaxon:1678", + "preferred_name": "Bifidobacterium", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Breast cancer", + "canonical_id": "MONDO:0007254", + "preferred_name": "breast cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Breast cancer", + "canonical_id": "MONDO:0007254", + "preferred_name": "breast cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Breast cancer", + "canonical_id": "MONDO:0007254", + "preferred_name": "breast cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PANTHER.FAMILY:PTHR45869:SF7", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:GeneFamily", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "PR:000005897", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "category": "biolink:Protein", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "C-reactive protein", + "canonical_id": "UNII:SCI18J406O", + "preferred_name": "C-REACTIVE PROTEIN", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "CHANTIX", + "canonical_id": "UMLS:C1712042", + "preferred_name": "Chantix", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "CIMZIA", + "canonical_id": "UMLS:C2343521", + "preferred_name": "Cimzia", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "CONTRACEPTION", + "canonical_id": "UMLS:C0009871", + "preferred_name": "Contraceptive Agents", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "CONTRACEPTION", + "canonical_id": "UMLS:C0009886", + "preferred_name": "Contraceptive Devices", + "category": "biolink:Device", + "taxon": null + }, + { + "input_term": "CONTRACEPTION", + "canonical_id": "UMLS:C0344225", + "preferred_name": "Encounter due to presence of intrauterine contraceptive device", + "category": "biolink:Phenomenon", + "taxon": null + }, + { + "input_term": "CONTRACEPTION", + "canonical_id": "UMLS:C0700589", + "preferred_name": "Contraceptive methods", + "category": "biolink:Procedure", + "taxon": null + }, + { + "input_term": "CONTRACEPTION", + "canonical_id": "UMLS:C4546343", + "preferred_name": "Uses contraception (finding)", + "category": "biolink:Phenomenon", + "taxon": null + }, + { + "input_term": "COVID-19 treatment", + "canonical_id": "UMLS:C5244048", + "preferred_name": "COVID-19 treatment", + "category": "biolink:Procedure", + "taxon": null + }, + { + "input_term": "Chronic pain", + "canonical_id": "HP:0012532", + "preferred_name": "Chronic pain", + "category": "biolink:PhenotypicFeature", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Chronic pain", + "canonical_id": "HP:0012532", + "preferred_name": "Chronic pain", + "category": "biolink:PhenotypicFeature", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Colorectal cancer", + "canonical_id": "MONDO:0005575", + "preferred_name": "colorectal cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Colorectal cancer", + "canonical_id": "MONDO:0005575", + "preferred_name": "colorectal cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Colorectal cancer", + "canonical_id": "MONDO:0005575", + "preferred_name": "colorectal cancer", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Contrast dye", + "canonical_id": "UMLS:C0742795", + "preferred_name": "contrast dye", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Crohn's disease", + "canonical_id": "MONDO:0005011", + "preferred_name": "Crohn disease", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Deep vein thrombosis", + "canonical_id": "HP:0002625", + "preferred_name": "Deep venous thrombosis", + "category": "biolink:PhenotypicFeature", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Deep vein thrombosis", + "canonical_id": "HP:0002625", + "preferred_name": "Deep venous thrombosis", + "category": "biolink:PhenotypicFeature", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Deep vein thrombosis", + "canonical_id": "UMLS:C0340708", + "preferred_name": "Deep vein thrombosis of lower limb", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Deep vein thrombosis", + "canonical_id": "UMLS:C0340708", + "preferred_name": "Deep vein thrombosis of lower limb", + "category": "biolink:Disease", + "taxon": null + }, + { + "input_term": "Diabetes mellitus type 2", + "canonical_id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Diabetes mellitus type 2", + "canonical_id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Diabetes mellitus type 2", + "canonical_id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "category": "biolink:Disease", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Dialysis solution", + "canonical_id": "MESH:D015314", + "preferred_name": "Dialysis Solutions", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Dialysis solution", + "canonical_id": "MESH:D015314", + "preferred_name": "Dialysis Solutions", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Dialysis solution", + "canonical_id": "UMLS:C0011947", + "preferred_name": "Dialysis Solutions", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "Dialysis solution", + "canonical_id": "UMLS:C0011947", + "preferred_name": "Dialysis Solutions", + "category": "biolink:Drug", + "taxon": null + }, + { + "input_term": "EGFR", + "canonical_id": 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For cold, the harness launches one local_runner process per repeat; warm repeats share one process. The OS file cache is not cleared. Ties resolve to multiple rows per input term." +} \ No newline at end of file diff --git a/paper/benchmarks/name_resolution/results-2026-10-28-v1.json b/paper/benchmarks/name_resolution/results-2026-10-28-v1.json new file mode 100644 index 00000000..c6a6702f --- /dev/null +++ b/paper/benchmarks/name_resolution/results-2026-10-28-v1.json @@ -0,0 +1,3744 @@ +{ + "date_utc": "2026-10-28T20:46:13Z", + "tablassert_version": "19.7.0", + "fullmap": "/local_raid1/sgoetz/DBSTORE/FULLMAP/fullmap", + "hosted_endpoint": "https://name-resolution-sri.renci.org/lookup", + "local": { + "n_terms": 113, + "n_unique_terms": 104, + "repeats": 3, + "resolve_wall_s": [ + 0.9372332240454853, + 0.6076390836387873, + 0.5484410668723285 + ], + "resolve_wall_s_median": 0.6076390836387873, + "unique_terms_per_s_median": 171.15423085889432, + "per_unique_term_ms_median": 5.842683496526801, + "resolved_rows": [ + 136, + 136, + 136 + ], + "process_total_s_incl_startup": 6.98783366009593, + "note": "resolve_batch 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"biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": null + } + }, + { + "curie": "UniProtKB:Q92956-1", + "canonical": { + "id": "UniProtKB:Q92956-1", + "preferred_name": "tumor necrosis factor receptor superfamily member 14 isoform h1 (human)", + "categories": [ + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": null + } + } + ], + "canonical": { + "id": "UniProtKB:Q99732-1", + "preferred_name": "lipopolysaccharide-induced tumor necrosis factor-alpha factor isoform 1 (human)", + "categories": [ + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": null + }, + "human_pref": { + "id": "UniProtKB:Q99732-1", + "preferred_name": "lipopolysaccharide-induced tumor necrosis factor-alpha factor isoform 1 (human)", + "categories": [ + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": null + } + }, + { + "term": "type 2 diabetes", + "http_status": 200, + "error": null, + "latency_s": 0.27168478025123477, + "candidate_curies": [ + "MONDO:0005148", + "UMLS:C0877302", + "UMLS:C0342295", + "UMLS:C1279313", + "UMLS:C0342297" + ], + "candidate_records": [ + { + "curie": "MONDO:0005148", + "canonical": { + "id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": [ + "NCBITaxon:9606" + ] + } + }, + { + "curie": "UMLS:C0877302", + "canonical": { + "id": "UMLS:C0877302", + "preferred_name": "Insulin-requiring type 2 diabetes mellitus", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": null + } + }, + { + "curie": "UMLS:C0342295", + "canonical": { + "id": "UMLS:C0342295", + "preferred_name": "Ketoacidosis due to type 2 diabetes mellitus", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": null + } + }, + { + "curie": "UMLS:C1279313", + "canonical": { + "id": "UMLS:C1279313", + "preferred_name": "Type 2 diabetes mellitus with peripheral angiopathy", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": null + } + }, + { + "curie": "UMLS:C0342297", + "canonical": { + "id": "UMLS:C0342297", + "preferred_name": "Hyperosmolar coma due to type 2 diabetes mellitus", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": null + } + } + ], + "canonical": { + "id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": [ + "NCBITaxon:9606" + ] + }, + "human_pref": { + "id": "MONDO:0005148", + "preferred_name": "type 2 diabetes mellitus", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": [ + "NCBITaxon:9606" + ] + } + }, + { + "term": "vitamin D", + "http_status": 200, + "error": null, + "latency_s": 0.2541631110943854, + "candidate_curies": [ + "UNII:9VU1KI44GP", + "MONDO:0100471", + "UniProtKB:P02774-1", + "MONDO:0024299", + "MONDO:0019642" + ], + "candidate_records": [ + { + "curie": "UNII:9VU1KI44GP", + "canonical": { + "id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "categories": [ + "biolink:ChemicalEntity", + "biolink:PhysicalEssence", + "biolink:ChemicalOrDrugOrTreatment", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:NamedThing", + "biolink:PhysicalEssenceOrOccurrent" + ], + "taxa": null + } + }, + { + "curie": "MONDO:0100471", + "canonical": { + "id": "MONDO:0100471", + "preferred_name": "vitamin D deficiency", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": [ + "NCBITaxon:9606" + ] + } + }, + { + "curie": "UniProtKB:P02774-1", + "canonical": { + "id": "UniProtKB:P02774-1", + "preferred_name": "vitamin D-binding protein isoform h1 (human)", + "categories": [ + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": null + } + }, + { + "curie": "MONDO:0024299", + "canonical": { + "id": "MONDO:0024299", + "preferred_name": "vitamin D-dependent rickets", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": null + } + }, + { + "curie": "MONDO:0019642", + "canonical": { + "id": "MONDO:0019642", + "preferred_name": "vitamin D-dependent rickets, type 2", + "categories": [ + "biolink:Disease", + "biolink:DiseaseOrPhenotypicFeature", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing" + ], + "taxa": null + } + } + ], + "canonical": { + "id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "categories": [ + "biolink:ChemicalEntity", + "biolink:PhysicalEssence", + "biolink:ChemicalOrDrugOrTreatment", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:NamedThing", + "biolink:PhysicalEssenceOrOccurrent" + ], + "taxa": null + }, + "human_pref": { + "id": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "categories": [ + "biolink:ChemicalEntity", + "biolink:PhysicalEssence", + "biolink:ChemicalOrDrugOrTreatment", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:NamedThing", + "biolink:PhysicalEssenceOrOccurrent" + ], + "taxa": null + } + } + ], + "lookup_wall_s": 8.060385833028704, + "lookup_terms_per_s": 12.90260815727252, + "repeats": 1 + }, + "hosted_requests_remaining": 191, + "api_spec": { + "lookup": { + "base": "https://name-resolution-sri.renci.org", + "method": "GET", + "path": "/lookup", + "params": { + "string": "free-text term (URL-quoted)", + "limit": "max candidates returned", + "only_taxa": "pipe-separated taxon filter, e.g. NCBITaxon:9606", + "biolink_type": "query param filtering candidates to biolink types", + "only_prefixes": "pipe-separated prefix filter (not used here)", + "exclude_prefixes": "pipe-separated prefix exclusion (not used here)" + }, + "called_with": { + "limit": 5, + "only_taxa": "NCBITaxon:9606", + "retry": "3 attempts, linear backoff 2s/4s", + "retries_on": [ + 429, + 500, + 502, + 503, + 504 + ] + }, + "taxon_filter": "only_taxa=NCBITaxon:9606 passed on every lookup (matched run)", + "unknown_taxa_handling": "fullmap retains taxon-free rows; equivalence of hosted unknown-taxon handling is not established", + "bulk": "POST /bulk-lookup supports biolink_types in the request body; this harness uses GET per term" + }, + "node_normalization": { + "base": "https://nodenormalization-sri.renci.org", + "method": "POST", + "path": "/get_normalized_nodes", + "params": { + "curies": "JSON body {\"curies\": [...]}, batched 100 per request" + }, + "returns": "per-CURIE {id:{identifier,label}, type:[...], equivalent_identifiers, taxa, information_content}", + "batch_size": 100, + "response_keying": "response is keyed by the requested (input) CURIEs, not canonical ids; results are grouped by requested candidate via group_normalized_by_requested", + "synonyms_endpoint": "not used; canonical records come from get_normalized_nodes" + }, + "local_fullmap": { + "api": "tablassert.fullmap.resolve_batch + tablassert.nlp.level_one/level_two", + "column_context": false, + "taxon": "9606" + } + }, + "n_input_rows": 113, + "n_unique_terms": 104, + "sample_selection": "hand-selected ingest vocabulary; not a random or representative accuracy sample", + "accounting_note": "Lookup and normalization durations measured separately; total and effective throughput recomputed from their sum after correcting the summary accounting. No timings were remeasured." +} diff --git a/paper/benchmarks/name_resolution/terms.txt b/paper/benchmarks/name_resolution/terms.txt new file mode 100644 index 00000000..9222abaa --- /dev/null +++ b/paper/benchmarks/name_resolution/terms.txt @@ -0,0 +1,117 @@ +# Terms drawn from DAKP production audit top text pairs (subject/object strings) +# plus common biomedical vocabulary used across Tablassert ingests. +# One term per line; lines starting with # are ignored. +TECEFIDERA +Multiple sclerosis +PROACTIV MD ADAPALENE ACNE TREATMENT +Acne +OTEZLA +Psoriasis +XARELTO +Atrial fibrillation +PROACTIV MD DEEP CLEANSING FACE WASH +XARELTO XR +Rivaroxaban +DIANA LOW CALCIUM PERITONEAL DIALYSIS SOLUTION WITH DEXTROSE +Peritoneal dialysis +PROACTIV MD DAILY OIL CONTROL SPF 30 +XELJANZ +Rheumatoid arthritis +ARELTO +Thrombosis prophylaxis +DIANEAL LOW CALCIUM PERITONEAL DIALYSIS SOLUTION WITH DEXTROSE +Dialysis solution +XARELTO XR +DIALUID +Hypermagnesemia +PAXLOVID +COVID-19 treatment +NUPLAZID +Parkinson's disease psychosis +XARELTO +Deep vein thrombosis +AUBAGIO +Multiple sclerosis +AMPYRA +Multiple sclerosis +MIRENA +Heavy menstrual bleeding +ELIGARD +Prostate cancer +CHANTIX +Smoking cessation +XYREM +Narcolepsy +GILENYA +Multiple sclerosis +AVANDIA +Diabetes mellitus type 2 +OXYCONTIN +Chronic pain +NUPLAZID +MIRENA +ZYPREXA +Schizophrenia +RINVOQ +Rheumatoid arthritis +CONTRACEPTION +CIIMZA +Contrast dye +SIMPONI +Ulcerative colitis +CIMZIA +Crohn's disease +# Common biomedical vocabulary exercised by Tablassert ingests +TP53 +lung cancer +aspirin +ibuprofen +metformin +Bifidobacterium +Lactobacillus +Alistipes +Eubacterium rectale +Faecalibacterium prausnitzii +Helicobacter pylori +Staphylococcus aureus +non-alcoholic fatty liver disease +type 2 diabetes +inflammatory bowel disease +Alzheimer disease +attention deficit hyperactivity disorder +rheumatoid arthritis +systemic lupus erythematosus +Breast cancer +Colorectal cancer +Prostate cancer +BRCA1 +BRCA2 +EGFR +KRAS +APOE +TNF +IL6 +LEP +ADIPOQ +insulin +glucose +cholesterol +triglycerides +butyric acid +propionic acid +tryptophan +fecal microbiota transplant +probiotics +antibiotics +trimethoprim +fluoxetine +atororvastatin +lisinopril +folic acid +vitamin D +iron +hepcidin +ferritin +C-reactive protein +interleukin-6 +tumor necrosis factor diff --git a/paper/benchmarks/name_resolution/test_bench_name_res.py b/paper/benchmarks/name_resolution/test_bench_name_res.py new file mode 100644 index 00000000..7ea122f4 --- /dev/null +++ b/paper/benchmarks/name_resolution/test_bench_name_res.py @@ -0,0 +1,296 @@ +"""Offline tests for the name-resolution benchmark harness (mocked network).""" + +from __future__ import annotations + +import json +import sys +import urllib.error +import urllib.request +from pathlib import Path +from typing import cast + +import pytest + +sys.path.insert(0, str(Path(__file__).parent)) + +import bench_name_res as bench + + +class FakeResponse: + def __init__(self, status: int, body: object) -> None: + self.status = status + self._body = json.dumps(body).encode("utf-8") + + def __enter__(self) -> FakeResponse: + return self + + def __exit__(self, *exc: object) -> None: + return None + + def read(self) -> bytes: + return self._body + + +def test_unique_terms_deterministic() -> None: + assert bench.main is not None # module imports offline + from local_runner import unique_terms_deterministic + + assert unique_terms_deterministic(["b", "a", "a", "c"]) == ["a", "b", "c"] + assert unique_terms_deterministic(["a"]) * 2 == unique_terms_deterministic(["a", "a"]) * 2 + + +def test_read_terms_skips_comments_and_blanks(tmp_path: Path) -> None: + terms_file = tmp_path / "terms.txt" + terms_file.write_text("# comment\n\nTP53\n lung cancer \n", encoding="utf-8") + from local_runner import read_terms + + assert read_terms(terms_file) == ["TP53", "lung cancer"] + + +def test_read_terms_stdin_dash(monkeypatch: pytest.MonkeyPatch) -> None: + import io + + from local_runner import read_terms + + monkeypatch.setattr(sys, "stdin", io.StringIO("TP53\n\nBRCA1\n")) + assert read_terms(Path("-")) == ["TP53", "BRCA1"] + + +def test_canonical_record_complete_vs_incomplete() -> None: + full = {"id": {"identifier": "HGNC:11998", "label": "TP53"}, "type": ["biolink:Gene"], "taxa": ["NCBITaxon:9606"]} + record = bench.canonical_record(full) + assert record == {"id": "HGNC:11998", "preferred_name": "TP53", "categories": ["biolink:Gene"], "taxa": ["NCBITaxon:9606"]} + # no label -> not a complete record + assert bench.canonical_record({"id": {"identifier": "X:1"}, "type": ["biolink:Gene"]}) is None + # no categories -> not a complete record + assert bench.canonical_record({"id": {"identifier": "X:1", "label": "x"}, "type": []}) is None + assert bench.canonical_record("junk") is None + + +def test_prefers_human_policy() -> None: + human = {"id": {"identifier": "H:1"}, "taxa": ["NCBITaxon:9606"]} + pig = {"id": {"identifier": "P:1"}, "taxa": ["NCBITaxon:9823"]} + no_taxa = {"id": {"identifier": "N:1"}} + assert bench.prefers_human(human) is True + assert bench.prefers_human(pig) is False + # no taxa information stays eligible + assert bench.prefers_human(no_taxa) is True + assert bench.prefers_human(None) is False + + +def test_lookup_one_nonempty_candidates_and_errors(monkeypatch: pytest.MonkeyPatch) -> None: + budget = bench.RequestBudget(10) + body = [{"curie": "HGNC:11998"}, {"curie": "NCBIGene:7157"}, {"curie": None}] + monkeypatch.setattr(urllib.request, "urlopen", lambda req, timeout: FakeResponse(200, body)) + record = bench.lookup_one("TP53", 5, budget) + assert record["http_status"] == 200 + assert record["curies"] == ["HGNC:11998", "NCBIGene:7157"] + assert record["error"] is None + assert budget.remaining == 9 + + def boom(req: urllib.request.Request, timeout: float) -> FakeResponse: + raise urllib.error.URLError("connection refused") + + monkeypatch.setattr(urllib.request, "urlopen", boom) + failed = bench.lookup_one("net down", 5, bench.RequestBudget(10)) + assert failed["http_status"] is None + assert failed["curies"] == [] + assert "URLError" in str(failed["error"]) + + +def test_lookup_one_retries_on_429(monkeypatch: pytest.MonkeyPatch) -> None: + calls: list[int] = [] + + def rate_limited(req: urllib.request.Request, timeout: float) -> FakeResponse: + calls.append(1) + if len(calls) < 2: + raise urllib.error.HTTPError(req.full_url, 429, "slow down", {}, None) # type: ignore[arg-type] + return FakeResponse(200, []) + + monkeypatch.setattr(urllib.request, "urlopen", rate_limited) + monkeypatch.setattr(bench.time, "sleep", lambda _s: None) + budget = bench.RequestBudget(10) + record = bench.lookup_one("aspirin", 5, budget) + assert record["http_status"] == 200 + assert len(calls) == 2 + assert budget.remaining == 8 + + +def test_normalize_batch_chunks_and_budget(monkeypatch: pytest.MonkeyPatch) -> None: + seen: dict[str, list[str]] = {} + seen["chunks"] = [] + + def capture(req: urllib.request.Request, timeout: float) -> FakeResponse: + data = req.data + assert isinstance(data, bytes) + payload = json.loads(data.decode("utf-8")) + seen["chunks"].append(payload["curies"]) + return FakeResponse(200, {curie: {"id": {"identifier": curie, "label": "x"}} for curie in payload["curies"]}) + + monkeypatch.setattr(urllib.request, "urlopen", capture) + budget = bench.RequestBudget(5) + result = bench.normalize_batch([f"X:{i}" for i in range(250)], budget, batch_size=100) + assert result["http_calls"] == 3 + assert [len(chunk) for chunk in seen["chunks"]] == [100, 100, 50] + assert result["error"] is None + assert budget.remaining == 2 + + +def test_normalize_batch_budget_exhaustion() -> None: + budget = bench.RequestBudget(1) + with pytest.raises(RuntimeError, match="budget"): + bench.normalize_batch([f"X:{i}" for i in range(250)], budget, batch_size=100) + + +def test_lookup_budget_exhaustion_propagates() -> None: + with pytest.raises(bench.BudgetExhausted, match="budget"): + bench.lookup_one("TP53", 5, bench.RequestBudget(0)) + + +def test_hosted_budget_preflight() -> None: + with pytest.raises(bench.BudgetExhausted, match="at least"): + bench.bench_hosted(["TP53", "BRCA1"], 1, 5, bench.RequestBudget(2)) + + +def test_group_normalized_by_requested_alias_keying() -> None: + # NodeNorm keys its response by the requested (input) CURIE, not the canonical id + requested = ["hgnc:11998", "MISSING:1"] + normalized: dict[str, object] = {"hgnc:11998": {"id": {"identifier": "NCBIGene:7157", "label": "TP53"}, "type": ["biolink:Gene"]}} + grouped = bench.group_normalized_by_requested(requested, normalized) + entry = cast("dict[str, object]", grouped["hgnc:11998"]) + assert cast("dict[str, object]", entry["id"])["identifier"] == "NCBIGene:7157" # canonical id inside the entry + assert grouped["MISSING:1"] is None + + +def test_group_normalized_by_requested_canonical_keyed_fallback() -> None: + # Robustness: if the service ever keys entries by canonical id, the requested + # alias candidate still resolves via id + equivalent_identifiers indexing. + normalized: dict[str, object] = { + "NCBIGene:7157": { + "id": {"identifier": "NCBIGene:7157", "label": "TP53"}, + "type": ["biolink:Gene"], + "equivalent_identifiers": [{"identifier": "HGNC:11998"}, {"identifier": "NCBIGene:7157"}], + } + } + grouped = bench.group_normalized_by_requested(["HGNC:11998"], normalized) + entry = cast("dict[str, object]", grouped["HGNC:11998"]) + assert cast("dict[str, object]", entry["id"])["identifier"] == "NCBIGene:7157" + + +def test_bench_hosted_ranked_top_candidate_not_first_complete(monkeypatch: pytest.MonkeyPatch) -> None: + # Top-ranked candidate has no NodeNorm record; a lower-ranked one does. + # canonical must follow the /lookup ranking (top), not 'first complete'. + lower_complete: dict[str, object] = {"id": {"identifier": "HGNC:1", "label": "T"}, "type": ["biolink:Gene"], "taxa": ["NCBITaxon:9606"]} + + def fake_lookup(term: str, limit: int, budget: bench.RequestBudget) -> bench.LookupRecord: + return {"term": term, "http_status": 200, "error": None, "curies": ["Z:9", "HGNC:1"], "latency_s": 0.3} + + def fake_normalize(curies: list[str], budget: bench.RequestBudget, batch_size: int = 100) -> bench.NormalizeResult: + return {"normalized": {"HGNC:1": lower_complete}, "http_calls": 1, "error": None} + + monkeypatch.setattr(bench, "lookup_one", fake_lookup) + monkeypatch.setattr(bench, "normalize_batch", fake_normalize) + result = bench.bench_hosted(["t"], concurrency=1, limit=5, budget=bench.RequestBudget(50)) + coverage = cast("dict[str, object]", result["coverage"]) + record = cast("list[dict[str, object]]", result["records"])[0] + assert record["canonical"] is None # top-ranked Z:9 has no record + assert [c["curie"] for c in cast("list[dict[str, object]]", record["candidate_records"])] == ["Z:9", "HGNC:1"] + assert cast("dict[str, object]", record["human_pref"])["id"] == "HGNC:1" # human preference is separate + assert coverage["complete_record"] == 0 + assert coverage["complete_record_human_pref"] == 1 + + +def test_bench_local_cold_one_fresh_process_per_repeat(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + import subprocess + + calls: list[list[str]] = [] + inputs: list[str] = [] + + def fake_run(argv: list[str], input: str, capture_output: bool, text: bool, check: bool) -> subprocess.CompletedProcess[str]: + calls.append(argv) + inputs.append(input) + out = Path(argv[argv.index("--out") + 1]) + mode = argv[argv.index("--mode") + 1] + reps = int(argv[argv.index("--repeats") + 1]) + wall = 0.5 if mode == "cold" else 0.1 + out.write_text( + json.dumps( + { + "mode": mode, + "repeats": reps, + "wall_s": [wall] * reps, + "wall_s_median": wall, + "resolved_rows_per_run": [7] * reps, + "resolved_terms_per_run": 7, + "records": [], + } + ) + ) + return subprocess.CompletedProcess(argv, 0) + + monkeypatch.setattr(bench.subprocess, "run", fake_run) + result = cast("dict[str, object]", bench.bench_local("python", tmp_path / "f", ["t1"], repeats=3, out_dir=tmp_path)) + cold_calls = [argv for argv in calls if argv[argv.index("--mode") + 1] == "cold"] + warm_calls = [argv for argv in calls if argv[argv.index("--mode") + 1] == "warm"] + # cold: one fresh process per repeat, each with --repeats 1; warm: one process, --repeats N + assert len(cold_calls) == 3 + assert all(argv[argv.index("--repeats") + 1] == "1" for argv in cold_calls) + assert len(warm_calls) == 1 + assert warm_calls[0][warm_calls[0].index("--repeats") + 1] == "3" + assert inputs == ["t1\n"] * 4 + cold = cast("dict[str, object]", result["cold"]) + assert cold["fresh_process_per_repeat"] is True + assert cold["os_cache_cleared"] is False + assert cast("list[float]", cold["wall_s"]) == [0.5, 0.5, 0.5] + assert cast("float", cold["wall_s_median"]) == 0.5 + assert cast("list[int]", cold["resolved_rows_per_run"]) == [7, 7, 7] + warm = cast("dict[str, object]", result["warm"]) + assert warm["mode"] == "warm" + + +def test_bench_hosted_coverage_levels(monkeypatch: pytest.MonkeyPatch) -> None: + def fake_lookup(term: str, limit: int, budget: bench.RequestBudget) -> bench.LookupRecord: + if term == "httpfail": + return {"term": term, "http_status": None, "error": "URLError: down", "curies": [], "latency_s": 0.1} + if term == "empty": + return {"term": term, "http_status": 200, "error": None, "curies": [], "latency_s": 0.2} + return {"term": term, "http_status": 200, "error": None, "curies": ["HGNC:1"], "latency_s": 0.3} + + def fake_normalize(curies: list[str], budget: bench.RequestBudget, batch_size: int = 100) -> bench.NormalizeResult: + return {"normalized": {"HGNC:1": {"id": {"identifier": "HGNC:1", "label": "T"}, "type": ["biolink:Gene"]}}, "http_calls": 1, "error": None} + + monkeypatch.setattr(bench, "lookup_one", fake_lookup) + monkeypatch.setattr(bench, "normalize_batch", fake_normalize) + result = bench.bench_hosted(["ok", "empty", "httpfail"], concurrency=2, limit=5, budget=bench.RequestBudget(50)) + coverage = cast("dict[str, object]", result["coverage"]) + records_list = cast("list[dict[str, object]]", result["records"]) + assert coverage["n_terms"] == 3 + assert coverage["http_ok"] == 2 + assert coverage["nonempty_candidates"] == 1 + assert coverage["complete_record"] == 1 + assert coverage["complete_record_human_pref"] == 1 + records_list_by_term = {cast("str", r["term"]): r for r in records_list} + empty = cast("dict[str, object]", records_list_by_term["empty"]) + httpfail = cast("dict[str, object]", records_list_by_term["httpfail"]) + ok = cast("dict[str, object]", records_list_by_term["ok"]) + assert empty["canonical"] is None + assert str(httpfail["error"]) == "URLError: down" + assert cast("dict[str, object]", ok["canonical"])["id"] == "HGNC:1" + assert cast("dict[str, object]", ok["canonical"])["preferred_name"] == "T" + + +def test_api_spec_documents_supported_params_and_no_synonyms() -> None: + spec = cast("dict[str, object]", bench.api_spec()) + lookup = cast("dict[str, object]", spec["lookup"]) + assert cast("dict[str, object]", spec["node_normalization"])["path"] == "/get_normalized_nodes" + assert str(cast("dict[str, object]", spec["node_normalization"])["synonyms_endpoint"]).startswith("not used") + assert str(cast("dict[str, object]", spec["node_normalization"])["response_keying"]).startswith("response is keyed by the requested") + # lookup params as actually supported by the live service + params = cast("dict[str, str]", lookup["params"]) + assert "only_taxa" in params + assert "biolink_type" in params + taxon_filter = str(lookup["taxon_filter"]) + assert bench.HUMAN_TAXON in taxon_filter + assert not taxon_filter.startswith("none") # no hardcoded unsupported 'no taxon' claim + assert "not established" in str(lookup["unknown_taxa_handling"]) + assert cast("dict[str, object]", spec["local_fullmap"])["column_context"] is False diff --git a/paper/figures/tablassert-simplified.drawio.png b/paper/figures/tablassert-simplified.drawio.png new file mode 100644 index 00000000..28ef42ef Binary files /dev/null and b/paper/figures/tablassert-simplified.drawio.png differ diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..5b2bf218 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,231 @@ +% JOSS paper bibliography for Tablassert. +% Every published entry below was verified against its primary registry (Crossref, +% arXiv, bioRxiv) or its upstream CITATION.cff / release metadata in October 2026. +% Large author lists use the first verified authors plus "and others"; the full +% lists live in the registry records. The tests/test_paper.py gate refuses +% uncited entries, so delete any entry you drop. + +@misc{prothrombin, + author = {{The UniProt Consortium}}, + title = {Prothrombin ({F2}), human: {UniProtKB} {P00734}}, + year = {2026}, + howpublished = {\url{https://www.uniprot.org/uniprotkb/P00734/entry}}, + note = {Alternative protein name: coagulation factor II; accessed October 29, 2026} +} + +@article{biolink-model, + author = {Unni, Deepak R. and Moxon, Sierra A. T. and Bada, Michael and Brush, Matthew H. and Bruskiewich, Richard and Caufield, J. Harry and others}, + title = {{Biolink Model}: A universal schema for knowledge graphs in clinical, biomedical, and translational science}, + journal = {Clinical and Translational Science}, + year = {2022}, + volume = {15}, + number = {8}, + pages = {1848--1855}, + doi = {10.1111/cts.13302} +} + +@article{translator, + author = {Fecho, Karamarie and Glusman, Gw{\^e}nlyn and Baranzini, Sergio E. and Bizon, Chris and Brush, Matthew and Byrd, William and others and {The Biomedical Data Translator Consortium}}, + title = {Announcing the {Biomedical Data Translator}: Initial Public Release}, + journal = {Clinical and Translational Science}, + year = {2025}, + volume = {18}, + number = {7}, + pages = {e70284}, + doi = {10.1111/cts.70284} +} + +@article{babel, + author = {Morris, Evan and Vaidya, Gaurav and Owen, Phil and Reilly, Jason and Fecho, Karamarie and Wang, Patrick and Kebede, Yaphet and Carter, E. Kathleen and Bizon, Chris}, + title = {The ``I'' in {FAIR}: Translating from Interoperability in Principle to Interoperation in Practice}, + journal = {arXiv preprint arXiv:2601.10008}, + year = {2026}, + doi = {10.48550/arXiv.2601.10008} +} + +@article{microbiomekg, + author = {Goetz, Skye L. and Glen, Amy K. and Glusman, Gw{\^e}nlyn}, + title = {{MicrobiomeKG}: bridging microbiome research and host health through knowledge graphs}, + journal = {Frontiers in Systems Biology}, + year = {2025}, + volume = {5}, + pages = {1544432}, + doi = {10.3389/fsysb.2025.1544432} +} + +@article{kraken, + author = {Glen, Amy K. and Witherington, Drew and Leslie, Trent and Baumgartner, Andrew and Fernando, Ashen and Vemuri, Bhargav and others}, + title = {{KRAKEN}: A provenance-tracked knowledge graph for multiomic and wellness research}, + journal = {bioRxiv}, + year = {2026}, + note = {Preprint, version 2, October 9, 2026}, + doi = {10.64898/2026.08.18.745544} +} + +@inproceedings{sapbert, + author = {Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel}, + title = {{Self-Alignment Pretraining} for Biomedical Entity Representations}, + booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, + year = {2021}, + pages = {4228--4238}, + doi = {10.18653/v1/2021.naacl-main.334} +} + +@misc{kgx, + author = {Unni, Deepak and Bruskiewich, Richard and Moxon, Sierra}, + title = {{KGX}: Knowledge Graph Exchange}, + year = {2026}, + note = {Version 2.7.0, released May 28, 2026}, + howpublished = {\url{https://github.com/biolink/kgx}} +} + +@misc{koza, + author = {Schaper, Kevin and Ships, Glass and Shefchek, Kent and Moxon, Sierra and Mungall, Chris}, + title = {Koza: data transformation framework for {LinkML} data models}, + year = {2026}, + note = {Version 2.7.0, released October 21, 2026}, + howpublished = {\url{https://github.com/monarch-initiative/koza}} +} + +@misc{redb, + author = {Berner, Christopher}, + title = {redb: An embedded key-value database in pure {Rust}}, + year = {2026}, + note = {Version 4.4.0, released October 13, 2026}, + howpublished = {\url{https://github.com/cberner/redb}} +} + +@misc{dakp, + author = {Goetz, Skye Lane and Glusman, Gw{\^e}nlyn}, + title = {{DAKP}: Drug Approvals Knowledge Provider}, + year = {2026}, + note = {Version 1.24.1, released October 14, 2026}, + howpublished = {\url{https://github.com/glusman-team/dakp}} +} + +@misc{drug-approvals, + author = {Goetz, Skye Lane}, + title = {Drug Approvals Knowledge Provider ({DAKP}) dataset}, + year = {2026}, + howpublished = {\url{https://huggingface.co/datasets/SkyeAv/drug-approvals-kp}} +} + +@misc{nameres, + title = {{NameResolution}: a service for finding {CURIEs} from lexical strings}, + year = {2026}, + note = {SRI name resolution service endpoint}, + howpublished = {\url{https://github.com/NCATSTranslator/NameResolution}} +} + +@misc{nodenorm, + title = {{NodeNormalization}: a service for producing Translator-compliant nodes from {CURIEs}}, + year = {2026}, + note = {SRI node normalization service; accessed 2026-10-30}, + howpublished = {\url{https://github.com/NCATSTranslator/NodeNormalization}} +} + +@misc{sapbert-renci, + author = {{RENCI Named Entity Recognition Working Group}}, + title = {Babel {SapBERT} ({RENCI} fork of {SapBERT})}, + year = {2025}, + note = {Version 1.0.0; training code for SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23}, + howpublished = {\url{https://github.com/RENCI-NER/sapbert/tree/v1.0.0}} +} + +@article{linkml, + author = {Moxon, Sierra A T and Solbrig, Harold and Harris, Nomi L and Kalita, Patrick and Miller, Mark A and others}, + title = {{LinkML}: an open data modeling framework}, + journal = {GigaScience}, + year = {2025}, + volume = {15}, + pages = {giaf152}, + doi = {10.1093/gigascience/giaf152} +} + +@article{biocypher, + author = {Lobentanzer, Sebastian and Aloy, Patrick and Baumbach, Jan and Bohar, Balazs and Carey, Vincent J and others}, + title = {Democratizing knowledge representation with {BioCypher}}, + journal = {Nature Biotechnology}, + year = {2023}, + volume = {41}, + pages = {1056--1059}, + doi = {10.1038/s41587-023-01848-y} +} + +@inproceedings{ontoweaver, + author = {Dreo, Johann and Laudy, Claire and Lobentanzer, Sebastian and Baric, Marko and Gaydukova, Ekaterina and Schwikowski, Benno}, + title = {Reproducible Mapping of Tabular Data into Semantic Knowledge Graphs with {OntoWeaver} and {BioCypher}}, + booktitle = {Proceedings of the 2024 27th International Conference on Information Fusion (FUSION)}, + year = {2024}, + pages = {1--8}, + doi = {10.23919/FUSION59988.2024.10706497} +} + +@article{spires, + author = {Caufield, J Harry and Hegde, Harshad and Emonet, Vincent and Harris, Nomi L and Joachimiak, Marcin P and Matentzoglu, Nicolas and Kim, HyeongSik and Moxon, Sierra and Reese, Justin T and Haendel, Melissa A and Robinson, Peter N and Mungall, Christopher J}, + title = {Structured Prompt Interrogation and Recursive Extraction of Semantics ({SPIRES}): a method for populating knowledge bases using zero-shot learning}, + journal = {Bioinformatics}, + year = {2024}, + volume = {40}, + pages = {btae104}, + doi = {10.1093/bioinformatics/btae104} +} + +@article{bioconda, + author = {{The Bioconda Team} and Grüning, Björn and Dale, Ryan and Sjödin, Andreas and Chapman, Brad A. and Rowe, Jillian and Tomkins-Tinch, Christopher H. and Valieris, Renan and Köster, Johannes}, + title = {{Bioconda}: sustainable and comprehensive software distribution for the life sciences}, + journal = {Nature Methods}, + year = {2018}, + volume = {15}, + number = {7}, + pages = {475--476}, + doi = {10.1038/s41592-018-0046-7} +} + +@article{monarch, + author = {Putman, Tim E and Schaper, Kevin and Matentzoglu, Nicolas and Harris, Nomi L and Moxon, Sierra and others}, + title = {The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species}, + journal = {Nucleic Acids Research}, + year = {2024}, + volume = {52}, + pages = {D938--D949}, + doi = {10.1093/nar/gkad1082} +} + +@misc{multiomics-kp, + author = {Goetz, Skye Lane and Glusman, Gw{\^e}nlyn}, + title = {Multiomics Knowledge Provider ({MOKG}) dataset}, + year = {2026}, + howpublished = {\url{https://huggingface.co/datasets/SkyeAv/multiomics-kp}} +} + +@misc{graph-monorepo, + author = {Goetz, Skye Lane and Glusman, Gw{\^e}nlyn}, + title = {glusman-team graph monorepo}, + year = {2026}, + note = {Current home of the Drug Approvals Knowledge Provider; historical repository at github.com/glusman-team/dakp}, + howpublished = {\url{https://github.com/glusman-team/graph-monorepo}} +} + +@misc{ingests-mokg, + author = {Goetz, Skye Lane}, + title = {Ingest multiomics-kp as a tablassert passthrough}, + year = {2026}, + howpublished = {Pull request 550, NCATSTranslator/translator-ingests (under review)}, + url = {https://github.com/NCATSTranslator/translator-ingests/pull/550} +} + +@misc{ingests, + author = {Goetz, Skye Lane}, + title = {Add a {Tablassert} passthrough ingest kind for upstream {KGX} sources}, + year = {2026}, + howpublished = {Pull request 543, NCATSTranslator/translator-ingests (under review)}, + url = {https://github.com/NCATSTranslator/translator-ingests/pull/543} +} + +@misc{translator-ingests, + author = {{NCATS Biomedical Data Translator Consortium}}, + title = {translator-ingests: Translator knowledge source ingest pipeline}, + year = {2025}, + howpublished = {\url{https://github.com/NCATSTranslator/translator-ingests}} +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..414a1d26 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,85 @@ +--- +title: 'Tablassert: Declarative Extraction of NCATS Translator-Compliant Knowledge Graphs from Biomedical Tables' +authors: + - name: Skye Lane Goetz + affiliation: 1 + orcid: 0009-0007-8373-2352 + - name: Jared C. Roach + affiliation: 1 + orcid: 0000-0003-4524-0107 + - name: Gwênlyn Glusman + affiliation: 1 + orcid: 0000-0001-8060-5955 +affiliations: + - index: 1 + name: Institute for Systems Biology +date: 20 October 2026 +lang: en-US +bibliography: paper.bib +--- + +# Summary + +Tables published in biomedical research papers, particularly supplemental tables, capture knowledge assertions omitted from the manuscript for several reasons, including statistical significance, space limitations, and narrative scope. These tables include association statistics, assay readouts, curated gene lists, omics data, and other information. Much of this information encodes relationships between biological entities (e.g., between row and column labels). Tablassert converts such tabular data (Excel, CSV, TSV, Parquet) into knowledge graphs ready for the NCATS Biomedical Data Translator [@translator]. A user declares in YAML how each table's data map to subject-predicate-object statements; Tablassert resolves free text (e.g., "lung cancer") to standard identifiers, attaches provenance and statistics, and emits graph files conforming to Translator standards [@kgx; @biolink-model]. + +# Statement of Need + +Biomedical knowledge graphs classically rely on bespoke ingest scripts for each source. When data sources lack standardization, such as the millions of tables published in biomedical literature, bespoke scripts impose three costs that limit scalability. First, mapping from each table's rows, columns, and cells to biological assertions is idiosyncratic, so it cannot easily be inspected, reviewed, or reused. Second, entity resolution may depend on hosted network services, making graph builds unreproducible. Third, and most importantly, because each source requires a new pipeline, most published tables are never ingested. As a result, the process is not scalable. + +Tablassert targets knowledge-graph engineers and bioinformaticians working with tabular biomedical sources who need Biolink-conformant output. Declarative YAML recipes make data-to-statement mapping scalable, inspectable, and reusable. Batched resolution against an embedded identifier database removes runtime service dependencies. These recipes extend beyond journal supplemental tables to any relational source (trial registries, drug approval records, or a researcher's own tables). Each build can emit the Resource Ingest Guide metadata required for integration with Translator. + +# State of the Field + +Several tools address aspects of Translator data ingestion. KGX [@kgx] exchanges and validates graphs, including schema-driven Biolink conformance checks, but provides no column-to-statement mapping, study-level structural invariants, offline Translator infores checks, or in-place pruning. LinkML [@linkml] models and validates schemas but does not perform entity resolution. Koza [@koza], the Monarch Initiative's [@monarch] ingest engine, pairs YAML configuration with semi-declarative Python transforms, so complex sources often require repetitive procedural code and external mappings for resolution. BioCypher [@biocypher] standardizes biomedical graph construction, and OntoWeaver [@ontoweaver] adds declarative tabular mappings; however, Translator Biolink qualifier semantics and Resource Ingest Guide metadata fall outside their scope. SPIRES [@spires] extracts assertions from free text using zero-shot prompting rather than semi-structured tables. Finally, the hosted SRI NameResolution service [@nameres] and separate Node Normalization service [@nodenorm] provide identifier resolution and canonical records through network APIs. Hosted workflows still depend on service availability, making historical results hard to reproduce without preserving responses or pinning complex underlying resources. + +We built Tablassert rather than contributing to an existing project for three reasons. First, Tablassert's recipe model is not backward-compatible with the translator-ingests transform interface [@translator-ingests]; migrating would discard provenance, certain edge types, and several qualifiers attributed to each edge. Second, Tablassert targets many heterogeneous tables, making additions easy by editing simple recipes rather than writing new code. Third, deterministic Tablassert builds run locally on demand, whereas shared ingests run as periodic batch jobs with unpinnable dependencies, slowing iteration and harming reproducibility. Together, these choices define Tablassert as a local, declarative, reproducible table-to-graph system. + +# Software Design + +![**Tablassert Architecture.** Graph and table YAMLs encode declarative transformations that are validated and coerced, compiled into operations, lazily optimized, and executed using a Python-Rust runtime, embedded named-entity recognition, and cryptographic caching. Outputs from individual tables are aggregated into one Biolink-conformant knowledge graph, exported as KGX nodes and edges alongside Translator Resource Ingest Guide (RIG) metadata.\label{tablassert}](./figures/tablassert-simplified.drawio.png) + +Tablassert uses declarative configuration to transform tabular data into a single aggregated, Biolink-conformant knowledge graph, exported as KGX with Translator Resource Ingest Guide (RIG) metadata (Figure \ref{tablassert}). It has three main trade-offs: an embedded NoSQL (fullmap) database enables local batched resolution without service calls; declarative YAML favors accessibility, validation, and speed over procedural flexibility; and a hybrid Python-Rust Maturin build system favors performance over implementation simplicity. + +## Embedded fullmap database + +Built from BABEL [@babel] exports, the embedded fullmap maps normalized synonyms and cross-vocabulary meta-identifiers to canonical identifiers with category and taxon metadata. Synonyms and queries share normalization. Redb [@redb] provides crash-safe NoSQL point lookups; graph builds treat the store as read-only, supporting parallel, memory-bounded access without SQL or runtime service calls. + +Storage is the main cost: the current BABEL 22.2.0 archive is 44.5 GB compressed, while the 2026-07-22 redb store benchmarked here occupies 92.9 GB. Resolution uses exact matches over normalized keys. To limit storage and lookup cost, the build retains the top 100 experimental taxa by Translator knowledge-graph node frequency, all OrganismTaxon rows, and rows without taxon metadata, potentially excluding rare species-specific identifiers. Sixteen smaller shards accelerate lookups and support multithreaded reads. After the initial build, read-only access needs no writer coordination, and a bounded in-process cache reuses hits and misses with minimal memory and synchronization costs. + +Each node column declares an NCBI taxon filter that retains matches for the declared species and taxon-less rows, Biolink category priority and avoid lists, and CURIE-prefix and regex exclusions. Before lookup, raw strings are trimmed, stripped of surrounding quotes, and lowercased. ASCII alphabetic tokens are stemmed, duplicates removed, and remaining tokens sorted so repeated words and word order do not affect matching. A second normalization uses a regular expression to remove runs of non-word characters (punctuation, whitespace, and other symbols) while retaining letters, digits, and underscores. Candidates are ranked by exact preferred-name agreement, normalized-name agreement, category priority, and normalization level required for the match, with ties retained in the best tier. Optional column-category frequencies provide a heuristic tie-breaker. + +Optional quality control applies a four-stage cascade: exact or alias matching, fuzzy string similarity, Schwartz-Hearst abbreviation expansion, then SapBERT [@sapbert; @sapbert-renci] embedding similarity using a checkpoint fine-tuned on BABEL synonyms (`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`). Each stage handles only what cheaper stages could not clear, and suspect mappings are dropped and logged with scores. For example, an audit rejected "factor ii" mapped to `HMDB:HMDB0000607` (cyanocobalamin; SapBERT cosine 0.444): BABEL lists "Factor II" as its synonym, but the coagulation drug context calls for prothrombin, not vitamin B12 [@prothrombin]. + +## Declarative YAML configuration + +Pydantic models validate recipes before execution with field-specific errors. The non-Turing-complete DSL favors common, fast transformations over arbitrary computation. This reduces code changes needed for new sources and helps nontechnical contributors and AI agents author graphs. + +Recipes compile into TCode, an ordered sequence of (callable, arguments) operations executed through Polars lazy frames. Independent transformations fuse until batched resolution, quality control, or output writing requires materialization. Sections are statement mappings that inherit a template's shared source, provenance, metadata, and qualifier declarations. For example, a section can select another entity column and predicate while reusing everything else, avoiding a second nearly identical recipe. Sections that share the same instructions reuse snapshots from a cryptographic cache. Lazy optimization decouples recipe order from execution order. It often improves performance but can be suboptimal for some workloads and rules out transformations requiring strict procedural ordering. + +Builds emit deduplicated KGX nodes and can emit a Resource Ingest Guide. Edges carry recipe-declared structured provenance, supporting-study metadata, and statistical qualifiers, including source URLs and publications when provided; traceability depends on the source and configuration. + +An optional study stage checks structural invariants; `validate-kgx` checks category-specific Biolink records and Translator infores identity offline. The `--prune` option removes failing edges and gates the post-prune exit status. These validations establish structure, not biological validity. + +## Maturin build system + +PyO3 Rust handles normalization, sharded fullmap lookups, streamed database construction, and NDJSON serialization. GIL-released Rayon workers parallelize these hot paths on standard CPython, while Python handles recipe parsing, Pydantic validation, and orchestration. Maturin builds Linux/macOS PyPI wheels for Unix-like bioinformatics platforms such as Bioconda [@bioconda]. Wheel users need no Rust toolchain; Windows requires WSL or a source build. This design targets hot-path performance at the cost of added implementation complexity. + +# Research Impact Statement + +Tablassert and its predecessor underlie several production knowledge graphs. MicrobiomeKG [@microbiomekg] was built with an earlier version; the deployed Microbiome and Multiomics knowledge providers (MOKP) [@multiomics-kp] and their maintenance builds use Tablassert. The Drug Approvals Knowledge Provider (DAKP), originally developed in the Airflow-based glusman-team/dakp repository [@dakp] and now maintained in glusman-team/graph-monorepo [@graph-monorepo], publishes validated, versioned drug-approval graphs with per-edge evidence linkouts [@drug-approvals]. As of release, Multiomics 3.1.3 contains 222,994 nodes and 18,206,965 edges derived from 2,136 PMC publications [@multiomics-kp], and DAKP 1.26.0 contains 16,257 nodes and 135,263 edges [@drug-approvals]. KRAKEN [@kraken], Phenome Health's unified knowledge graph, integrates the Microbiome, Multiomics, and Drug Approvals graphs as domain-specific sources. Translator ingest integration is in progress, beginning with a Multiomics passthrough [@ingests; @ingests-mokg]. Future directions entail expanding MOKP to integrate a comprehensive subset of rare disease supplemental tables. + +# AI Usage Disclosure + +Generative AI assisted with code generation, test writing, security analyses, documentation, reviewing, drafting, and revising. Coding-agent use began on 01/27/2026. Models used included claude-opus-5, claude-sonnet-5, deepseek-v4, gemini-3.8-flash, glm-5.3, gpt-5.6, gpt-6, kimi-k2, kimi-k3, muse-spark-1.3-contributor, nemotron-3-ultra, laguna-s-2.1, qwen3.8, mimo-v2.5, and mimo-v2.6-pro in a custom Pi coding agent (v0.81.1) harness with prompt engineering from promptfoo (v0.121.17) and GEPA (v0.1.4). Prompt optimization and AI evaluation with CodeRabbit supplemented human review. Additionally, Grammarly was used to revise documentation and draft manuscripts. Authors made all architecture decisions and reviewed all outputs. The authors retain full responsibility for all aspects of the software and manuscript. + +# Author Contributions + +Skye Goetz developed the software, led design, and performed analyses. Jared Roach and Gwênlyn Glusman helped design the software and analyses and provided feedback. All authors wrote the manuscript. + +# Acknowledgements + +This work was supported by the National Science Foundation (Award 2150265) and by the National Center for Advancing Translational Sciences, Biomedical Translator Program (Other Transaction Awards OT2TR003443 and OT2TR005706). Any opinions expressed in this manuscript are those of the Translator community at large and do not necessarily reflect the views of NCATS, individual Translator team members, or affiliated organizations and institutions. + +NCATS provided financial support but had no role in software design, analysis, or manuscript preparation. Consortium authorship requirements were clarified through committee consultation. + +# References diff --git a/tests/test_paper.py b/tests/test_paper.py new file mode 100644 index 00000000..7d7c28a7 --- /dev/null +++ b/tests/test_paper.py @@ -0,0 +1,178 @@ +"""JOSS paper gate: structure, citations, and word budgets for paper/paper.md. + +Mirrors tests/test_docs_style.py: plain functions over explicit file paths, no fixtures, +standard-library imports only (the pre-commit hook runs this file alone, so it must stay +import-light). Enforces the structural requirements Whedon compilation and JOSS review +impose so collaborators get fast local feedback: front matter carries title, authors +with ORCIDs, and bibliography metadata; the body carries every JOSS-required section (JOSS paper-format +guidance, 2026); every [@key] citation resolves to a paper.bib entry and every entry is +cited; the abstract and body stay inside the official JOSS word budgets. +""" + +import re +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parent.parent +PAPER_MD = REPO_ROOT / "paper" / "paper.md" +PAPER_BIB = REPO_ROOT / "paper" / "paper.bib" + +# Exact non-ASCII characters permitted in the paper, mapped to their source. +ALLOWED_NON_ASCII = {"ê": "Gwênlyn Glusman, author name in CITATION.cff"} + +# JOSS guidance: "The paper should be between 750-1750 words" (body, excluding +# references); the leading unheaded abstract paragraph is capped separately. +BODY_WORD_LIMIT = 1750 +ABSTRACT_WORD_LIMIT = 250 + +REQUIRED_SECTIONS = ( + "Summary", + "Statement of need", + "State of the field", + "Software design", + "Research impact statement", + "AI usage disclosure", + "Acknowledgements", + "References", +) + +_BIB_ENTRY = re.compile(r"^@\w+\s*\{([^,\s]+),", re.MULTILINE) +_CITED_GROUP = re.compile(r"\[@([^\]]*)\]") +_ORCID = re.compile(r"\b\d{4}-\d{4}-\d{4}-\d{3}[\dX]\b") + + +def _split_front_matter(text: str) -> tuple[str, str]: + """Split the leading YAML front matter block (--- ... ---) from the body.""" + if not text.startswith("---\n"): + raise ValueError("paper.md must start with a YAML front matter block") + try: + end = text.index("\n---\n", 3) + except ValueError: + raise ValueError("paper.md front matter is missing its closing --- line") from None + return text[4:end], text[end + 5 :] + + +def _yaml_children(front_matter: str, key: str) -> list[str]: + """Return the indented child lines under a top-level YAML key.""" + lines = front_matter.splitlines() + try: + start = next(i for i, line in enumerate(lines) if line.startswith(f"{key}:")) + except StopIteration: + raise ValueError(f"front matter is missing the {key} key") from None + children: list[str] = [] + for line in lines[start + 1 :]: + if line and not line.startswith((" ", "\t")): + break + children.append(line) + return children + + +def _author_blocks(front_matter: str) -> list[list[str]]: + """Group the authors section into one block per `- name:` entry.""" + blocks: list[list[str]] = [] + for line in _yaml_children(front_matter, "authors"): + if line.lstrip().startswith("- "): + blocks.append([line]) + elif blocks: + blocks[-1].append(line) + return blocks + + +def _strip_fences(body: str) -> str: + """Drop fenced code blocks, which do not count toward prose word budgets.""" + lines: list[str] = [] + fence = False + for line in body.splitlines(): + if line.strip().startswith("```"): + fence = not fence + continue + if not fence: + lines.append(line) + return "\n".join(lines) + + +def _section(body: str, title: str) -> str: + """Return the prose of one top-level section, excluding its heading.""" + match = re.search(rf"^#+\s+{re.escape(title)}\s*$", body, re.MULTILINE) + if match is None: + return "" + rest = body[match.end() :] + nxt = re.search(r"^#+\s", rest, re.MULTILINE) + return rest[: nxt.start()] if nxt else rest + + +def _before_references(body: str) -> str: + """Drop everything from the References heading onward (bibliography is rendered).""" + match = re.search(r"^#+\s*References\s*$", body, re.MULTILINE) + return body[: match.start()] if match else body + + +def _cited_keys(text: str) -> set[str]: + """Collect every citation key inside [@key] / [@!key] / [@a; @b] brackets.""" + keys: set[str] = set() + for group in _CITED_GROUP.findall(text): + for part in group.split(";"): + key = part.strip().lstrip("@").lstrip("!") + if key: + keys.add(key) + return keys + + +def _bib_keys(text: str) -> set[str]: + return set(_BIB_ENTRY.findall(text)) + + +def test_front_matter_complete() -> None: + front_matter, _ = _split_front_matter(PAPER_MD.read_text(encoding="utf-8")) + assert re.search(r"^title:\s*\S", front_matter, re.MULTILINE), "front matter needs a title" + assert re.search(r"^bibliography:\s*paper\.bib\s*$", front_matter, re.MULTILINE), ( + "front matter needs bibliography: paper.bib so pandoc citeproc loads the entries" + ) + assert not re.search(r"^tags:", front_matter, re.MULTILINE), "front matter must omit tags" + blocks = _author_blocks(front_matter) + assert blocks, "front matter needs at least one author" + for block in blocks: + assert any("name:" in line for line in block), "every author needs a name" + assert any(_ORCID.search(line) for line in block), f"every author needs an ORCID: {block}" + + +def test_required_sections_present() -> None: + _, body = _split_front_matter(PAPER_MD.read_text(encoding="utf-8")) + headings = {match.group(1).strip().casefold() for match in re.finditer(r"^#+\s+(.+?)\s*$", body, re.MULTILINE)} + missing = [section for section in REQUIRED_SECTIONS if section.casefold() not in headings] + assert not missing, f"paper.md is missing JOSS-required sections: {missing}" + + +def test_citations_resolve() -> None: + text = PAPER_MD.read_text(encoding="utf-8") + bib = _bib_keys(PAPER_BIB.read_text(encoding="utf-8")) + unresolved = _cited_keys(text) - bib + assert not unresolved, f"citations missing from paper.bib: {sorted(unresolved)}" + + +def test_no_uncited_entries() -> None: + text = PAPER_MD.read_text(encoding="utf-8") + bib = _bib_keys(PAPER_BIB.read_text(encoding="utf-8")) + dead = bib - _cited_keys(text) + assert not dead, f"paper.bib entries never cited in paper.md: {sorted(dead)}" + + +def test_abstract_within_budget() -> None: + _, body = _split_front_matter(PAPER_MD.read_text(encoding="utf-8")) + abstract = _section(_strip_fences(body), "Summary") + words = len(abstract.split()) + assert words > 0, "paper.md needs a Summary section with abstract paragraphs" + assert words <= ABSTRACT_WORD_LIMIT, f"abstract is {words} words (max {ABSTRACT_WORD_LIMIT})" + + +def test_body_within_budget() -> None: + _, body = _split_front_matter(PAPER_MD.read_text(encoding="utf-8")) + prose = _before_references(_strip_fences(body)) + words = len(prose.split()) + assert words >= 750, f"body is {words} words (JOSS minimum 750)" + assert words <= BODY_WORD_LIMIT, f"body is {words} words (max {BODY_WORD_LIMIT})" + + +def test_ascii_only() -> None: + text = PAPER_MD.read_text(encoding="utf-8") + offenders = sorted({c for c in text if ord(c) > 127 and c not in ALLOWED_NON_ASCII}) + assert not offenders, f"paper.md: unlisted non-ASCII characters {offenders!r}"