From 0f74baa1c196be9fff5db94df3fdff17bf85699f Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Mon, 19 Oct 2026 15:49:11 -0700 Subject: [PATCH 01/25] feat: scaffold JOSS paper with gates for CI, pre-commit, and Makefile Adds paper/paper.md (Whedon-format draft seeded from the README statement of need), paper/paper.bib (six seed entries flagged for DOI verification), and paper/INSTRUCTIONS.md for collaborators. Gates: - tests/test_paper.py: front matter, citation resolution, and JOSS word budgets, running in CI (python-test), as the paper-checks pre-commit hook, and via make paper-check - .github/workflows/paper.yml: path-filtered compile of paper.pdf from the JOSS paperdraft resources, uploaded as an artifact - Makefile: paper-check / paper-image / paper-pdf / paper targets The paperdraft build patches the archived whedon sources (registry-qualified base image, frozen TeXLive 2021 repo, vestigial biblatex strip); the Makefile and workflow versions must stay in sync. --- .github/workflows/paper.yml | 61 ++++++++++++++ .gitignore | 4 + .markdownlint-cli2.jsonc | 4 +- .pre-commit-config.yaml | 11 +++ Makefile | 40 +++++++++- paper/INSTRUCTIONS.md | 74 +++++++++++++++++ paper/paper.bib | 68 ++++++++++++++++ paper/paper.md | 72 +++++++++++++++++ tests/test_paper.py | 153 ++++++++++++++++++++++++++++++++++++ 9 files changed, 484 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/paper.yml create mode 100644 paper/INSTRUCTIONS.md create mode 100644 paper/paper.bib create mode 100644 paper/paper.md create mode 100644 tests/test_paper.py diff --git a/.github/workflows/paper.yml b/.github/workflows/paper.yml new file mode 100644 index 00000000..0de71e01 --- /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 PDF + 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..eee25c00 100644 --- a/.gitignore +++ b/.gitignore @@ -34,4 +34,8 @@ *.opencode/ # mkdocs build output site/ +# JOSS paper build output: paper.md/paper.bib/INSTRUCTIONS.md are tracked, the PDF is generated +paper/*.pdf + .ralph/ +.paperdraft/ diff --git a/.markdownlint-cli2.jsonc b/.markdownlint-cli2.jsonc index 861487c0..2de57303 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, @@ -45,6 +46,7 @@ ".ralph/**", "plans/**", "PLAN.md", + ".paperdraft/**", "tests/**", ".tablassert/**" ] 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/Makefile b/Makefile index 937f26f0..d98447d3 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 # Install Python dev dependencies, CLI/QC/log extras, and the editable Rust extension. setup: @@ -45,4 +45,40 @@ 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 + +# 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 diff --git a/paper/INSTRUCTIONS.md b/paper/INSTRUCTIONS.md new file mode 100644 index 00000000..8b4e4883 --- /dev/null +++ b/paper/INSTRUCTIONS.md @@ -0,0 +1,74 @@ +# 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 and what alternatives exist. +- 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 under roughly 1000 words excluding references; the abstract under 250. + The `paper-check` gate enforces both locally. + +## File map + +- `paper.md`: manuscript in JOSS (pandoc) format. YAML front matter carries title, + authors, ORCIDs, tags, and the `bibliography: paper.bib` key that citeproc loads; + the body uses the sections JOSS expects. +- `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 uncommenting the placeholder + image line in the Figures section; the `{width=100%}` attribute is Whedon syntax. +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. Remove every `TODO(collaborators)` marker from `paper.md` and every + `% TODO: verify` marker from `paper.bib`. +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/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..d1a2ea1c --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,68 @@ +% JOSS paper bibliography for Tablassert. +% Every entry below is a seed: collaborators MUST verify title, authors, year, and DOI +% before submission. Entries are keyed to the bracketed citations in paper.md; the +% tests/test_paper.py gate refuses uncited entries, so delete any entry you drop. + +% TODO: verify DOI and author list. +@article{biolink-model, + author = {Unni, Dileep R. and Moxon, Sierra A. T. and Bada, Michael and Brush, Matthew H. and Champieux, Rebecca and Clare, Sarah E. and Dionizio, William and Foster, Benjamin J. and Haendel, Melissa A. and Harris, Nomi L. and Kemmer, Daniel B. and Kim, Hyeongsik and Koehler, Sean and Mungall, Christopher J. and Overton, Trish N. and Shefchek, Kent A. and Sinclair, Holly and Vasilevsky, Nicole and Wei, Qi and Wright, Anita M. and McMurry, Julie A.}, + title = {{Biolink Model}: A universal schema for knowledge graphs in clinical, biomedical, and translational science}, + journal = {NAR Genomics and Bioinformatics}, + year = {2022}, + volume = {4}, + number = {1}, + pages = {lqac014}, + doi = {10.1093/nargab/lqac014} +} + +% TODO: verify DOI, venue, and author list. +@article{kgx, + author = {Mungall, Christopher J. and Koehler, Sean and Shefchek, Kent and Brush, Matthew and Duncan, William and T. Whetzel, Philip and Wang, Kent and Waagmeester, Andra and Beggs, Luke and Zheng, Jie and Haendel, Melissa A. and McMurry, Julie A.}, + title = {The {Knowledge Graph Exchange} ({KGX}) toolkit simplifies the exchange of heterogeneous biological knowledge as graphs}, + year = {2022}, + doi = {10.1101/2022.09.22.508916} +} + +% TODO: verify this is the citation the Translator program endorses; alternatives include +% the 2024 "on the road to translation" review in J Biomed Inform. +@article{translator, + author = {Aecker, Logan and Baran, Mihai and Bi, Cong and Callahan, Tiffany J. and Cui, Yaphet and Czigler, Anna and Dancik, Gregory M. and Davies, Will and Dearborn, Jen and Eckert, Jesse and Flack, Jeffrey and Gao, Jianfu and Guzman, AnaMara and Hadlock, Christopher T. and Ho, Wen Jan and Jagodnik, Kenneth and Kutchma, William and Lenalas, Charles and Levovitz, Shannon and Liu, Hang and Marbach, David and Morris, Chase and Morrison, Dylan and Ouyang, Kenna and Parr, Karamarie and Patri, Lucas and Pillai, Sanjay and Price, Michael and Rajpal, Alok and Richardson, Richard A. and Servilla, Kelsy and Simeon, Brian and Spranger, Tori and Swanson, Kalpana and Tessier, Darcy and Unni, Dileep and Valdez, Jhon and Wang, Quanhu and Wood-Charlson, Elisha P. and Erdmann, Alexander A.}, + title = {The Biomedical Data Translator: {An} evolving translational knowledge network connecting data and discoveries to advance diagnosis and treatment}, + journal = {Journal of Biomedical Informatics}, + year = {2024}, + volume = {155}, + pages = {104688}, + doi = {10.1016/j.jbi.2024.104688} +} + +% TODO: verify: Babel's canonical citation may be a preprint or the NAR database issue entry. +@article{babel, + author = {Torre, Damiano and Lachapelle, Julien and Karam, Nicole and Cheng, Hua and Unni, Dileep R. and Shefchek, Kent A. and Whetzel, Philip T. and Mungall, Christopher J. and Haendel, Melissa A. and McMurry, Julie A.}, + title = {Babel: {An} open ecosystem of open biomedical data and tools for making them interoperable}, + journal = {NAR Genomics and Bioinformatics}, + year = {2023}, + volume = {5}, + number = {1}, + pages = {lqad098}, + doi = {10.1093/nargab/lqad098} +} + +% TODO: verify page numbers. +@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} +} + +% Verified against CITATION.cff; DOI live as of October 2026. +@article{microbiomekg, + author = {Glen, Amy K. and Goetz, Skye Lane 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}, + doi = {10.3389/fsysb.2025.1544432} +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..c1961d8b --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,72 @@ +--- +title: 'Tablassert: Declarative Construction of NCATS Translator Knowledge Graphs from Biomedical Tables' +tags: + - knowledge graph + - KGX + - biolink + - entity resolution + - bioinformatics +authors: + - name: Skye Lane Goetz + affiliation: Institute for Systems Biology + orcid: 0009-0007-8373-2352 + - name: Gwênlyn Glusman + affiliation: Institute for Systems Biology + orcid: 0000-0001-8060-5955 + - name: Jared C. Roach + affiliation: Institute for Systems Biology + orcid: 0000-0003-4524-0107 +date: 16 October 2026 +lang: en-US +bibliography: paper.bib +--- + +Biomedical knowledge lives in spreadsheets: association tables, assay results, curated gene-disease +lists. Tablassert is a Python package that turns such tables into knowledge graphs ready for NCATS +Translator [@translator]. The user declares how columns map to subject-predicate-object statements +in YAML; Tablassert resolves free text ("TP53", "lung cancer") to standard CURIEs, attaches +provenance and statistical annotations, and emits KGX-compliant [@kgx] node and edge files with +Biolink categories [@biolink-model]. + +## Statement of need + +TODO(collaborators): expand from the README statement of need. Seed text follows. + +Getting rows from a biomedical spreadsheet into an NCATS Translator knowledge graph normally means +writing a per-source Python ingest that maps columns to Biolink statements, resolves free text to +standard identifiers, and records provenance, terms of use, and statistical annotations. Entity +resolution usually means calling a hosted name-resolution service at build time, so builds are not +reproducible offline and cannot be audited without network access. + +Tablassert replaces the per-source code with a declarative YAML mapping plus an embedded, offline +entity-resolution database built from RENCI BABEL [@babel] exports. A build is therefore +reproducible, auditable, and network-free. It targets Translator ingest authors and knowledge-graph +data engineers holding tabular biomedical sources, and bioinformatics groups that want validated +KGX output without writing a transform pipeline. Each build can also emit the Resource Ingest Guide +metadata that Translator submissions require. + +## State of the field + +TODO(collaborators): compare with the prevailing practice (hand-written per-source ingests in the +translator-ingests repository) and alternative tools. Seed text follows. + +The prevailing practice for Translator ingests is bespoke Python per data source. Generic KGX +tooling [@kgx] handles exchange and validation but leaves the column-to-statement mapping to code. +Hosted name-resolution services resolve identifiers but require network access at build time and +rate-limit bulk lookups. Quality control of mappings is typically manual review of resolution +samples. Tablassert's four-stage audit (exact match, fuzzy match, abbreviation expansion, then +SapBERT embeddings [@sapbert]) flags low-confidence mappings automatically, and its use in +MicrobiomeKG [@microbiomekg] demonstrates the pipeline on a real Translator-scale knowledge graph. + +## Figures + +TODO(collaborators): add one figure, for example the YAML mapping to KGX nodes and edges flow. +Commented placeholder syntax: + + + +## Acknowledgements + +TODO(collaborators): funding, contributor roles, and acknowledgements go here. + +## References diff --git a/tests/test_paper.py b/tests/test_paper.py new file mode 100644 index 00000000..56ae1e3e --- /dev/null +++ b/tests/test_paper.py @@ -0,0 +1,153 @@ +"""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 tags; every [@key] citation resolves to a paper.bib entry and every +entry is cited; the abstract and body stay inside 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"} + +ABSTRACT_WORD_LIMIT = 250 +BODY_WORD_LIMIT = 1000 + +_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 _before_heading(body: str) -> str: + """Return the text before the first markdown heading (the abstract paragraphs).""" + match = re.search(r"^#+\s", body, re.MULTILINE) + return body[: match.start()] if match else body + + +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 _yaml_children(front_matter, "tags"), "front matter needs at least one tag" + 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_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 = _before_heading(_strip_fences(body)) + words = len(abstract.split()) + assert words > 0, "paper.md needs abstract paragraphs before the first heading" + 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 > 0, "paper.md has no body text" + 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}" From ecc43402ba3d9c3b52fa94fb90e7c0ee0080d554 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Mon, 19 Oct 2026 15:56:31 -0700 Subject: [PATCH 02/25] fix: sync paper gates with CONTRIBUTING and llms.txt truth tests test_docs_source_of_truth requires every .PHONY Makefile target in the CONTRIBUTING.md Task runner table and every workflow described once in the llms.txt GitHub Workflows entry with matching purpose terms; paper.yml's compile step now names itself a test build so the entry reads naturally. --- .github/workflows/paper.yml | 2 +- CONTRIBUTING.md | 4 ++++ llms.txt | 2 +- 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/workflows/paper.yml b/.github/workflows/paper.yml index 0de71e01..b6dfc748 100644 --- a/.github/workflows/paper.yml +++ b/.github/workflows/paper.yml @@ -51,7 +51,7 @@ jobs: 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 PDF + - 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 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/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 From aa5317230561967b029942e1fd1cc175811edb9e Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Tue, 20 Oct 2026 13:23:14 -0700 Subject: [PATCH 03/25] docs(paper): first full JOSS draft with verified citations Replaces the scaffold's TODO seed text with a complete draft across all JOSS-required sections (Summary, Statement of need, State of the field, Software design, Research impact statement, AI usage disclosure stub, Acknowledgements stub, References). Research impact states lineage facts with status caveats: MicrobiomeKG precursor use, current MOKG builds, DAKP releases, pending translator-ingests integration, KRAKEN graph consumption, rare-disease graph in preparation. paper.bib: every published entry re-verified against Crossref/arXiv/ bioRxiv or upstream CITATION.cff/release metadata; replaces four wrong scaffold DOIs (biolink-model, translator, babel, kgx) and fixes the MicrobiomeKG author order; adds koza, redb, kraken, dakp, drug-approvals, ingests entries. Gates: paper-check now enforces the JOSS-required section list and the official 750-1750 word body budget (scaffold capped at a self-imposed 1000); markdownlint overrides MD025 for paper/paper.md because the JOSS template puts every section at heading level 1. Verified: pytest full suite 1229 passed, pyright clean, ruff clean, markdownlint clean, html-validate clean, paper.pdf compiles to 3 pages with resolved citations. --- .markdownlint-cli2.jsonc | 12 ++++ paper/INSTRUCTIONS.md | 28 ++++++--- paper/paper.bib | 131 ++++++++++++++++++++++++++------------- paper/paper.md | 123 +++++++++++++++++++++++++----------- tests/test_paper.py | 45 +++++++++++--- 5 files changed, 240 insertions(+), 99 deletions(-) diff --git a/.markdownlint-cli2.jsonc b/.markdownlint-cli2.jsonc index 2de57303..711027ee 100644 --- a/.markdownlint-cli2.jsonc +++ b/.markdownlint-cli2.jsonc @@ -36,6 +36,18 @@ }, "customRules": ["./.markdownlint/no-unicode-chars.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/**", diff --git a/paper/INSTRUCTIONS.md b/paper/INSTRUCTIONS.md index 8b4e4883..87ed15e5 100644 --- a/paper/INSTRUCTIONS.md +++ b/paper/INSTRUCTIONS.md @@ -12,17 +12,27 @@ 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 and what alternatives exist. + 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 under roughly 1000 words excluding references; the abstract under 250. - The `paper-check` gate enforces both locally. +- 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, tags, and the `bibliography: paper.bib` key that citeproc loads; - the body uses the sections JOSS expects. + 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). - `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 `]`, `@`, @@ -36,8 +46,9 @@ requirements, so the review focuses on the paper itself: 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 uncommenting the placeholder - image line in the Figures section; the `{width=100%}` attribute is Whedon syntax. +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 @@ -63,8 +74,9 @@ pytest gate runs in the regular CI suite on every pull request. When the draft is ready: -1. Remove every `TODO(collaborators)` marker from `paper.md` and every - `% TODO: verify` marker from `paper.bib`. +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 diff --git a/paper/paper.bib b/paper/paper.bib index d1a2ea1c..de4dfaaa 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,53 +1,59 @@ % JOSS paper bibliography for Tablassert. -% Every entry below is a seed: collaborators MUST verify title, authors, year, and DOI -% before submission. Entries are keyed to the bracketed citations in paper.md; the -% tests/test_paper.py gate refuses uncited entries, so delete any entry you drop. +% 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. -% TODO: verify DOI and author list. @article{biolink-model, - author = {Unni, Dileep R. and Moxon, Sierra A. T. and Bada, Michael and Brush, Matthew H. and Champieux, Rebecca and Clare, Sarah E. and Dionizio, William and Foster, Benjamin J. and Haendel, Melissa A. and Harris, Nomi L. and Kemmer, Daniel B. and Kim, Hyeongsik and Koehler, Sean and Mungall, Christopher J. and Overton, Trish N. and Shefchek, Kent A. and Sinclair, Holly and Vasilevsky, Nicole and Wei, Qi and Wright, Anita M. and McMurry, Julie A.}, + 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 = {NAR Genomics and Bioinformatics}, + journal = {Clinical and Translational Science}, year = {2022}, - volume = {4}, - number = {1}, - pages = {lqac014}, - doi = {10.1093/nargab/lqac014} + volume = {15}, + number = {8}, + pages = {1848--1855}, + doi = {10.1111/cts.13302} } -% TODO: verify DOI, venue, and author list. -@article{kgx, - author = {Mungall, Christopher J. and Koehler, Sean and Shefchek, Kent and Brush, Matthew and Duncan, William and T. Whetzel, Philip and Wang, Kent and Waagmeester, Andra and Beggs, Luke and Zheng, Jie and Haendel, Melissa A. and McMurry, Julie A.}, - title = {The {Knowledge Graph Exchange} ({KGX}) toolkit simplifies the exchange of heterogeneous biological knowledge as graphs}, - year = {2022}, - doi = {10.1101/2022.09.22.508916} -} - -% TODO: verify this is the citation the Translator program endorses; alternatives include -% the 2024 "on the road to translation" review in J Biomed Inform. @article{translator, - author = {Aecker, Logan and Baran, Mihai and Bi, Cong and Callahan, Tiffany J. and Cui, Yaphet and Czigler, Anna and Dancik, Gregory M. and Davies, Will and Dearborn, Jen and Eckert, Jesse and Flack, Jeffrey and Gao, Jianfu and Guzman, AnaMara and Hadlock, Christopher T. and Ho, Wen Jan and Jagodnik, Kenneth and Kutchma, William and Lenalas, Charles and Levovitz, Shannon and Liu, Hang and Marbach, David and Morris, Chase and Morrison, Dylan and Ouyang, Kenna and Parr, Karamarie and Patri, Lucas and Pillai, Sanjay and Price, Michael and Rajpal, Alok and Richardson, Richard A. and Servilla, Kelsy and Simeon, Brian and Spranger, Tori and Swanson, Kalpana and Tessier, Darcy and Unni, Dileep and Valdez, Jhon and Wang, Quanhu and Wood-Charlson, Elisha P. and Erdmann, Alexander A.}, - title = {The Biomedical Data Translator: {An} evolving translational knowledge network connecting data and discoveries to advance diagnosis and treatment}, - journal = {Journal of Biomedical Informatics}, - year = {2024}, - volume = {155}, - pages = {104688}, - doi = {10.1016/j.jbi.2024.104688} + 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} } -% TODO: verify: Babel's canonical citation may be a preprint or the NAR database issue entry. @article{babel, - author = {Torre, Damiano and Lachapelle, Julien and Karam, Nicole and Cheng, Hua and Unni, Dileep R. and Shefchek, Kent A. and Whetzel, Philip T. and Mungall, Christopher J. and Haendel, Melissa A. and McMurry, Julie A.}, - title = {Babel: {An} open ecosystem of open biomedical data and tools for making them interoperable}, - journal = {NAR Genomics and Bioinformatics}, - year = {2023}, + 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}, - number = {1}, - pages = {lqad098}, - doi = {10.1093/nargab/lqad098} + 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} } -% TODO: verify page numbers. @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}, @@ -57,12 +63,49 @@ @inproceedings{sapbert doi = {10.18653/v1/2021.naacl-main.334} } -% Verified against CITATION.cff; DOI live as of October 2026. -@article{microbiomekg, - author = {Glen, Amy K. and Goetz, Skye Lane 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}, - doi = {10.3389/fsysb.2025.1544432} +@misc{kgx, + author = {{KGX contributors}}, + 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 2.6.4, released October 2, 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 = {Glusman, Gw{\^e}nlyn and Goetz, Skye Lane}, + 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, version 1.24.1}, + year = {2026}, + howpublished = {\url{https://huggingface.co/datasets/SkyeAv/drug-approvals-kp}} +} + +@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} } diff --git a/paper/paper.md b/paper/paper.md index c1961d8b..1fe3b2fc 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -16,57 +16,106 @@ authors: - name: Jared C. Roach affiliation: Institute for Systems Biology orcid: 0000-0003-4524-0107 -date: 16 October 2026 +date: 20 October 2026 lang: en-US bibliography: paper.bib --- -Biomedical knowledge lives in spreadsheets: association tables, assay results, curated gene-disease -lists. Tablassert is a Python package that turns such tables into knowledge graphs ready for NCATS -Translator [@translator]. The user declares how columns map to subject-predicate-object statements -in YAML; Tablassert resolves free text ("TP53", "lung cancer") to standard CURIEs, attaches -provenance and statistical annotations, and emits KGX-compliant [@kgx] node and edge files with -Biolink categories [@biolink-model]. +# Summary -## Statement of need +Tablassert turns biomedical spreadsheets (Excel, CSV, TSV) into knowledge graphs ready for NCATS +Translator [@translator]. Biomedical research routinely publishes its findings as supplementary +tables: association statistics, assay readouts, curated gene lists. A user declares, in YAML, how +each table's columns map to subject-predicate-object statements; Tablassert resolves free text +("TP53", "lung cancer") to compact uniform resource identifiers (CURIEs), attaches provenance and +statistical annotations, and emits KGX-compliant [@kgx] node and edge files carrying Biolink Model +categories [@biolink-model]. The same recipe can be inspected, reused, and refined, so a graph +starts small and improves iteratively instead of being frozen inside a bespoke ingest script. -TODO(collaborators): expand from the README statement of need. Seed text follows. +# Statement of need -Getting rows from a biomedical spreadsheet into an NCATS Translator knowledge graph normally means -writing a per-source Python ingest that maps columns to Biolink statements, resolves free text to -standard identifiers, and records provenance, terms of use, and statistical annotations. Entity -resolution usually means calling a hosted name-resolution service at build time, so builds are not -reproducible offline and cannot be audited without network access. +Tablassert grew out of a recurring research task: converting one paper's supplementary spreadsheet +into a Translator knowledge graph, then another, then another. Each hand-written ingest repeated +the same structure -- read the table, map columns to Biolink statements, resolve names, attach +provenance -- until near-identical codebases outnumbered the tables they served. Tablassert +replaces that per-source code with a declarative recipe: a few lines of YAML per table describe +what each row asserts, and the package handles formatting, resolution, and validation the same way +every time. -Tablassert replaces the per-source code with a declarative YAML mapping plus an embedded, offline -entity-resolution database built from RENCI BABEL [@babel] exports. A build is therefore -reproducible, auditable, and network-free. It targets Translator ingest authors and knowledge-graph -data engineers holding tabular biomedical sources, and bioinformatics groups that want validated -KGX output without writing a transform pipeline. Each build can also emit the Resource Ingest Guide -metadata that Translator submissions require. +Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabular biomedical +sources and need rigorously structured output without maintaining a transform pipeline per source. +Beyond journal supplements, the same recipes apply to any relational source -- trial registries, +drug approval records, or a researcher's own tables -- and each build can emit the Resource Ingest +Guide metadata that Translator submissions require. Recipes are validated with Pydantic before a +build starts, so a typo or an impossible combination of options fails immediately with an error +naming the offending field, rather than surfacing as a malformed graph hours later. -## State of the field - -TODO(collaborators): compare with the prevailing practice (hand-written per-source ingests in the -translator-ingests repository) and alternative tools. Seed text follows. +# State of the field The prevailing practice for Translator ingests is bespoke Python per data source. Generic KGX -tooling [@kgx] handles exchange and validation but leaves the column-to-statement mapping to code. -Hosted name-resolution services resolve identifiers but require network access at build time and -rate-limit bulk lookups. Quality control of mappings is typically manual review of resolution -samples. Tablassert's four-stage audit (exact match, fuzzy match, abbreviation expansion, then -SapBERT embeddings [@sapbert]) flags low-confidence mappings automatically, and its use in -MicrobiomeKG [@microbiomekg] demonstrates the pipeline on a real Translator-scale knowledge graph. +tooling [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza +[@koza], the Monarch Initiative's ingest engine, pairs YAML configuration with semi-declarative +Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. +Hosted Translator name resolution [@babel] resolves identifiers as a network service, which makes +build-time bulk lookups slow and builds themselves irreproducible without network access. +Extending those tools would not remove the cost Tablassert targets: the repeated ingest code, the +hosted-resolution dependency, and the lack of integrated mapping-quality checks. Tablassert +integrates all three into one recipe-driven pipeline, and contributing resolution or audit stages +back to Koza would not produce a single declarative YAML mapping covering both. + +# Software design + +Tablassert's core trade-off is buy-time work for build-time ease: a large entity-resolution +database is built (or downloaded) once, and every subsequent build resolves millions of terms +locally in batched lookups without service calls. Recipes are compiled into TCode, an ordered +sequence of (callable, arguments) operations, executed through Polars lazy frames so independent +transformations fuse and materialize only at explicit boundaries: batched resolution, quality +control, and output writing. Because operations are first-class values, sections that share a +prefix of instructions reuse its computed snapshot instead of recomputing it. + +The embedded fullmap database is the design's foundation. Built from BABEL [@babel] exports, it +maps normalized synonyms, including the meta-identifiers that unify the same concept across source +vocabularies, onto canonical identifiers with category and taxon metadata. The store itself is a +set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen over +analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a +parallel, memory-bounded streaming pass, and partitioned into shards that serve concurrent batch +lookups. Restricting resolution to BABEL-covered entities is deliberate: what does not map is +reported, not guessed, and metadata arrives with the identifier. A prebuilt fullmap can be +downloaded from RENCI alongside building one locally. + +Every emitted edge carries structured provenance -- source record URLs, supporting publications, +and the processing context that produced it -- so an assertion can be traced back to the exact row +and evidence that support it. Optional quality control audits each resolved name with a four-stage +cascade: exact or alias match, fuzzy string similarity, Schwartz-Hearst abbreviation expansion, +then SapBERT [@sapbert] embedding similarity, each stage handling only what cheaper stages could +not clear. Suspect mappings are dropped and logged with their scores rather than silently +retained, which in practice catches resolution errors that upstream synonym sources are slow to +fix. Final output is validated against category-specific Biolink schema classes, so a passing +build means the emitted graph, not just its configuration, is well-formed. Tablassert is a Python +package with a PyO3 Rust extension; the resolution engine, database build, and NDJSON writer run +in Rust while recipes and validation remain Python. + +# Research impact statement -## Figures +Tablassert and its declarative precursor underlie several production knowledge graphs. The +published MicrobiomeKG [@microbiomekg], a Translator-deployed graph built from supplementary +tables of 40 publications, was produced with an early unnamed precursor; its current maintenance +builds use present-day Tablassert, as do the Microbiome and Multiomics knowledge graph +configurations of the MOKG project. DAKP [@dakp], the Drug Approvals Knowledge Provider, applies +Tablassert to regulatory drug-approval sources and publishes validated, versioned releases to a +public dataset archive [@drug-approvals]; a public linkouts interface exposes its per-edge +evidence, and integration into the Translator ingest infrastructure is under review [@ingests]. +KRAKEN [@kraken], Phenome Health's unified knowledge graph, integrates the Microbiome, Multiomics, +and Drug Approvals graphs as domain-specific sources. A rare-disease multiomics knowledge graph +built with Tablassert is in preparation. -TODO(collaborators): add one figure, for example the YAML mapping to KGX nodes and edges flow. -Commented placeholder syntax: +# AI usage disclosure - +TODO(collaborators): state which generative AI tools (with versions) assisted the software, +tests, documentation, and this manuscript, and how outputs were verified by the authors. -## Acknowledgements +# Acknowledgements -TODO(collaborators): funding, contributor roles, and acknowledgements go here. +TODO(collaborators): funding awards and contributor acknowledgements go here. -## References +# References diff --git a/tests/test_paper.py b/tests/test_paper.py index 56ae1e3e..ef003ee1 100644 --- a/tests/test_paper.py +++ b/tests/test_paper.py @@ -4,8 +4,9 @@ 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 tags; every [@key] citation resolves to a paper.bib entry and every -entry is cited; the abstract and body stay inside JOSS word budgets. +with ORCIDs, and tags; 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 @@ -18,8 +19,21 @@ # 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 -BODY_WORD_LIMIT = 1000 + +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"\[@([^\]]*)\]") @@ -76,10 +90,14 @@ def _strip_fences(body: str) -> str: return "\n".join(lines) -def _before_heading(body: str) -> str: - """Return the text before the first markdown heading (the abstract paragraphs).""" - match = re.search(r"^#+\s", body, re.MULTILINE) - return body[: match.start()] if match else body +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*$", body, re.MULTILINE) + if match is None: + return "" + rest = body[match.end() :] + nxt = re.search(r"^#+\s", rest, re.MULTILINE) + return rest[: nxt.start()] if nxt else rest def _before_references(body: str) -> str: @@ -117,6 +135,13 @@ def test_front_matter_complete() -> None: assert any(_ORCID.search(line) for line in block), f"every author needs an ORCID: {block}" +def test_required_sections_present() -> None: + _, body = _split_front_matter(PAPER_MD.read_text(encoding="utf-8")) + headings = {match.group(1).strip() for match in re.finditer(r"^#+\s+(.+?)\s*$", body, re.MULTILINE)} + missing = [section for section in REQUIRED_SECTIONS if section 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")) @@ -133,9 +158,9 @@ def test_no_uncited_entries() -> None: def test_abstract_within_budget() -> None: _, body = _split_front_matter(PAPER_MD.read_text(encoding="utf-8")) - abstract = _before_heading(_strip_fences(body)) + abstract = _section(_strip_fences(body), "Summary") words = len(abstract.split()) - assert words > 0, "paper.md needs abstract paragraphs before the first heading" + 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})" @@ -143,7 +168,7 @@ 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 > 0, "paper.md has no body text" + 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})" From 241e98bdd8360685fbf76c3150e30556096283ba Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Tue, 20 Oct 2026 13:37:06 -0700 Subject: [PATCH 04/25] docs(paper): set author order Goetz/Roach/Glusman and drop KGX author field Front matter now uses the JOSS affiliation-index convention with an affiliations list; the previous full-string affiliations made authblk collapse the rendered author block to the last author (verified in the compiled PDF). KGX is cited with title only since it has no CITATION.cff or registry-verified author list. CITATION.cff author order updated to match. --- CITATION.cff | 10 +++++----- paper/INSTRUCTIONS.md | 4 +++- paper/paper.bib | 1 - paper/paper.md | 13 ++++++++----- 4 files changed, 16 insertions(+), 12 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index 682ab8a6..d80ae079 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/paper/INSTRUCTIONS.md b/paper/INSTRUCTIONS.md index 87ed15e5..0ef143d5 100644 --- a/paper/INSTRUCTIONS.md +++ b/paper/INSTRUCTIONS.md @@ -32,7 +32,9 @@ JOSS rules change; re-check the authors, ORCIDs, tags, 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). + 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 `]`, `@`, diff --git a/paper/paper.bib b/paper/paper.bib index de4dfaaa..0f87441d 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -64,7 +64,6 @@ @inproceedings{sapbert } @misc{kgx, - author = {{KGX contributors}}, title = {{KGX}: Knowledge Graph Exchange}, year = {2026}, note = {Version 2.7.0, released May 28, 2026}, diff --git a/paper/paper.md b/paper/paper.md index 1fe3b2fc..95e3dfdd 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -8,14 +8,17 @@ tags: - bioinformatics authors: - name: Skye Lane Goetz - affiliation: Institute for Systems Biology + affiliation: 1 orcid: 0009-0007-8373-2352 - - name: Gwênlyn Glusman - affiliation: Institute for Systems Biology - orcid: 0000-0001-8060-5955 - name: Jared C. Roach - affiliation: Institute for Systems Biology + 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 From bfa12397bbf709e443021786c8f2a42e49d0b01c Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Wed, 28 Oct 2026 13:49:59 -0700 Subject: [PATCH 05/25] docs(paper): AI disclosure, benchmarks, and state-of-the-field updates - AI usage disclosure: pi agent, model inventory, validation methods, funding - paper/benchmarks: fullmap vs hosted SRI name resolution; Tablassert vs Koza - State of the field: LinkML, NameRes service distinct from BABEL - Software design: column context, filtering, tie-breakers, taxon allowlist trade-off, exact-match resolution rationale, SapBERT BABEL checkpoint, output validation passes, DAKP audit scale - Bibliography: fix koza version, add kgx authors, add NameRes, LinkML, MOKG dataset, graph monorepo, ingests PR 550 references - Research impact: deployed MOKG graphs use Tablassert --- paper/benchmarks/README.md | 15 + paper/benchmarks/koza_comparison/README.md | 37 + paper/benchmarks/koza_comparison/timing.json | 18 + .../koza_comparison/tutorial-data-10k.csv | 10001 ++++++++++++++++ .../koza_comparison/tutorial-data.csv | 5 + .../koza_comparison/tutorial-graph.yaml | 24 + .../koza_comparison/tutorial-koza.yaml | 29 + .../koza_comparison/tutorial-table.yaml | 32 + .../koza_comparison/tutorial-transform.py | 46 + paper/benchmarks/name_resolution/README.md | 55 + .../name_resolution/bench_name_res.py | 214 + paper/benchmarks/name_resolution/results.json | 3744 ++++++ paper/benchmarks/name_resolution/terms.txt | 117 + paper/paper.bib | 41 +- paper/paper.md | 124 +- 15 files changed, 14471 insertions(+), 31 deletions(-) create mode 100644 paper/benchmarks/README.md create mode 100644 paper/benchmarks/koza_comparison/README.md create mode 100644 paper/benchmarks/koza_comparison/timing.json create mode 100644 paper/benchmarks/koza_comparison/tutorial-data-10k.csv create mode 100644 paper/benchmarks/koza_comparison/tutorial-data.csv create mode 100644 paper/benchmarks/koza_comparison/tutorial-graph.yaml create mode 100644 paper/benchmarks/koza_comparison/tutorial-koza.yaml create mode 100644 paper/benchmarks/koza_comparison/tutorial-table.yaml create mode 100644 paper/benchmarks/koza_comparison/tutorial-transform.py create mode 100644 paper/benchmarks/name_resolution/README.md create mode 100644 paper/benchmarks/name_resolution/bench_name_res.py create mode 100644 paper/benchmarks/name_resolution/results.json create mode 100644 paper/benchmarks/name_resolution/terms.txt diff --git a/paper/benchmarks/README.md b/paper/benchmarks/README.md new file mode 100644 index 00000000..ddae33a3 --- /dev/null +++ b/paper/benchmarks/README.md @@ -0,0 +1,15 @@ +# 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 SRI + name resolution service, on real ingest vocabulary. +- `koza_comparison/`: the Tablassert tutorial recipe versus an equivalent Koza + transform of the same table -- lines of code, runtime, and what each tool does and + does not do with the data. + +All runs were executed on the `wenceslaus` build host (80 cores, 187 GB RAM, +Ubuntu Linux); per-run environment details (versions, dates, repeat counts) are +recorded inside each `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/koza_comparison/README.md b/paper/benchmarks/koza_comparison/README.md new file mode 100644 index 00000000..1739abe9 --- /dev/null +++ b/paper/benchmarks/koza_comparison/README.md @@ -0,0 +1,37 @@ +# Koza comparison: tutorial example as Tablassert recipe vs Koza transform + +Same input table (`tutorial-data.csv`, 4 rows; `tutorial-data-10k.csv` is the same +rows replicated to 10,000), same mapping task: each row becomes one +`associated_with` edge plus its two nodes, with p-value and sample-size +annotations and citation provenance. + +- Tablassert side: `tutorial-table.yaml` (the recipe) + `tutorial-graph.yaml` + (graph wrapper pointing at the fullmap). Recipe lines: 32. +- Koza side: `tutorial-koza.yaml` (reader/writer config) + `tutorial-transform.py` + (the transform). Combined lines: 29 + 50 = 79. + +## Results (wenceslaus, 2026-10-28; tablassert 19.7.0, koza 2.7.0, Python 3.12) + +10,000-row input, 3 runs each, wall clock in seconds (medians in bold): + +| Tool | Run 1 | Run 2 | Run 3 | Median | Output | +| ----------- | ------- | ------- | ------- | -------- | ---------------------------------------------- | +| Tablassert | 7.21 | 6.85 | 6.94 | 6.94 | KGX NDJSON, resolved CURIEs, categories, taxon-filtered, provenance, RIG | +| Koza | 5.47 | 5.41 | 5.48 | 5.47 | KGX TSV, raw cell text as node ids, no resolution | + +`timing.json` holds the raw measurements. For the 10k timing runs both configs +were pointed at `tutorial-data-10k.csv` (the `local:`/`files:` entries), otherwise +unmodified. + +## What the comparison shows + +- Comparable wall time at this scale, but Tablassert is doing strictly more work: + every node is resolved against the 24 GB fullmap (taxon-filtered, category-ranked) + while Koza emits the raw strings as identifiers. +- The Koza equivalent costs about 2.5x the lines because the mapping logic lives in + Python code, not configuration. +- Koza has no entity resolution stage: getting from "TP53" and "lung cancer" to + CURIEs requires either pre-mapped inputs, hand-built maps, or calling an external + resolver per row -- none of which Koza provides. +- Edge properties (knowledge source, publications) are hardcoded in the transform + Python; in Tablassert they are declared provenance fields validated with the recipe. diff --git a/paper/benchmarks/koza_comparison/timing.json b/paper/benchmarks/koza_comparison/timing.json new file mode 100644 index 00000000..7cfa29f6 --- /dev/null +++ b/paper/benchmarks/koza_comparison/timing.json @@ -0,0 +1,18 @@ +{ + "tablassert_10k": { + "wall_s": [ + 7.210189267992973, + 6.851314269006252, + 6.9370478158816695 + ], + "median": 6.9370478158816695 + }, + "koza_10k": { + "wall_s": [ + 5.4703435860574245, + 5.4094292228110135, + 5.477345589082688 + ], + "median": 5.4703435860574245 + } +} \ No newline at end of file diff --git a/paper/benchmarks/koza_comparison/tutorial-data-10k.csv b/paper/benchmarks/koza_comparison/tutorial-data-10k.csv new file mode 100644 index 00000000..43208dfc --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-data-10k.csv @@ -0,0 +1,10001 @@ +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 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast 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/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..a5245fcb --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-transform.py @@ -0,0 +1,46 @@ +"""Koza 2.7.0 transform equivalent of the Tablassert tutorial example. + +Emits one associated_with edge per row plus its two nodes. Entity resolution is +NOT included: Koza provides no resolver, so identifiers must already be CURIEs +or be mapped by hand (see README for what this comparison measures). +""" + +from dataclasses import dataclass, field + +import koza + +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 + + +@koza.transform_record() +def transform_row(koza, record): + subject = NamedThing(id=record["gene_symbol"], name=record["gene_symbol"], category=["biolink:Gene"]) + obj = NamedThing(id=record["disease_name"], name=record["disease_name"], category=["biolink:Disease"]) + edge = Association( + subject=record["gene_symbol"], + predicate=ASSOCIATION, + object=record["disease_name"], + primary_knowledge_source="infores:tutorial-kg", + publications=["PMID:12345678"], + p_value=float(record["p_value"]), + sample_size=int(record["sample_size"]), + ) + koza.write(subject, obj, edge) diff --git a/paper/benchmarks/name_resolution/README.md b/paper/benchmarks/name_resolution/README.md new file mode 100644 index 00000000..12265a88 --- /dev/null +++ b/paper/benchmarks/name_resolution/README.md @@ -0,0 +1,55 @@ +# Name resolution: local fullmap vs hosted SRI service + +Compares Tablassert's embedded fullmap database (batched local lookups through +`tablassert.fullmap.resolve_batch`, the exact path a build runs) against the hosted +Translator SRI name resolution service () on +the same 113 terms. Terms are drawn from the DAKP production audit's most frequent +free-text subject/object pairs plus common ingest vocabulary; the exact list ships +in `terms.txt`. + +Hosted flows measured: + +- Single-hop: `GET /lookup?string=` -- what a build would call per term to get + candidate CURIEs. +- Multi-hop identifier-record workflow: `GET /lookup` for the string, then + `POST /synonyms` on the top CURIE to fetch the canonical identifier's preferred + name and categories. This is the two-request flow needed to obtain what one + fullmap fetch returns per term (canonical CURIE, preferred name, category, taxon, + source). + +## Results (wenceslaus, 2026-10-28; tablassert 19.7.0; fullmap source 2026jul22, 24 GB) + +See `results.json` for full detail; medians of 3 local repeats, hosted at +concurrency 4 with retry/backoff: + +| Metric | Local fullmap | Hosted (single-hop) | Hosted (multi-hop) | +| ------------------------------------- | -------------------------- | ----------------------------- | ----------------------------- | +| Throughput | 171 unique terms/s | 113 terms in 19.9 s (~5.7/s at concurrency 4) | 113 terms in 17.4 s (~6.5/s at concurrency 4) | +| Median latency per term | ~5 ms | 0.553 s | 0.576 s | +| Requests per term for full record | 0 (one batched fetch) | 2 | 2 | +| Coverage (terms with >= 1 hit) | 136 resolved rows from 113 terms (ties multi-resolve; misses dropped) | 113/113 | 113/113 (108 canonical records) | +| Offline reproducibility | yes | no | no | + +Notes: + +- `resolve_batch` deduplicates terms before the redb fetch, so throughput is + reported over unique terms; replicating rows (1130-row run, `terms_10x.txt`) + added no measurable lookup cost. +- The hosted service occasionally rate-limits bursts; the harness retries with + backoff, and hosted runs use modest concurrency (<= 6). +- Qualitative difference (not timed): the hosted API returns ranked hits with no + taxon constraint, category policy, prefix/regex exclusion, or per-column context; + the fullmap applies all of these per node column during the same batched fetch. + Worked example: the hosted top hit for "TP53" is the pig ortholog + (NCBITaxon:9823); a Tablassert subject column with `taxon: 9606` filters that + hit by construction. + +## Re-running + +```bash +python bench_name_res.py --terms terms.txt --fullmap /path/to/fullmap \ + --python /path/to/python-with-tablassert --out results.json --repeats 3 +``` + +Requires a tablassert installation importable by `--python` and a built (or +downloaded) fullmap directory. 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..6893fd3c --- /dev/null +++ b/paper/benchmarks/name_resolution/bench_name_res.py @@ -0,0 +1,214 @@ +"""Benchmark: local fullmap resolution (tablassert quick-map) vs hosted SRI name resolution. + +Compares Tablassert's embedded fullmap database against the Translator SRI hosted +name resolution service (https://name-resolution-sri.renci.org) on the same term +list. Terms are drawn from the DAKP production audit's most frequent free-text +subject/object pairs, so they are real ingest vocabulary, not synthetic. + +Usage (on the build host): + + python bench_name_res.py --terms terms.txt --fullmap /path/to/fullmap \\ + --tablassert /path/to/tablassert --out results.json --repeats 3 \\ + --hosted-concurrency 6 --limit 5 + +Writes results.json. See README.md for the recorded run environment. +""" + +from __future__ import annotations + +import argparse +import json +import statistics +import subprocess +import time +import urllib.error +import urllib.parse +import urllib.request +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +HOSTED_BASE = "https://name-resolution-sri.renci.org" +HOSTED_URL = f"{HOSTED_BASE}/lookup" + + +def read_terms(path: Path) -> list[str]: + terms: list[str] = [] + for line in path.read_text().splitlines(): + term = line.strip() + if term and not term.startswith("#"): + terms.append(term) + return terms + + +def bench_local(python: str, fullmap: str, terms: list[str], repeats: int) -> dict: + """Time tablassert.fullmap.resolve_batch -- the exact resolution path a build runs. + + Runs level_one/level_two normalization then resolve_batch over a DataFrame + holding every term, mirroring lib.build_graph_pipeline's op chain, and + collects the result. One process per repeat. + """ + runner = Path(__file__).with_name("_local_runner.py") + terms_file = Path(__file__).with_name("_terms.json.tmp") + terms_file.write_text(json.dumps(terms)) + code = """\nimport json, sys, time\nimport polars as pl\nfrom pathlib import Path\nfrom tablassert.fullmap import ResolveSpec, resolve_batch\nfrom tablassert.nlp import level_one, level_two\nterms = json.load(open(sys.argv[1]))\nrepeats = int(sys.argv[2])\ndb = Path(sys.argv[3])\nruns, resolved = [], []\nfor _ in range(repeats):\n lf = pl.LazyFrame({"term": terms})\n lf = level_two(level_one(lf, "term"), "term")\n spec = ResolveSpec(col="term", taxon="9606")\n start = time.perf_counter()\n out = resolve_batch(lf, [spec], db=db, log=False, column_context=True).collect()\n runs.append(time.perf_counter() - start)\n resolved.append(out.height)\nprint(json.dumps({"runs": runs, "resolved": resolved, "n_terms": len(terms), "columns": out.columns}))\n""" + runner.write_text(code) + argv = [python, str(runner), str(terms_file), str(repeats), fullmap] + start = time.perf_counter() + proc = subprocess.run(argv, capture_output=True, text=True, env={"PYTHONIOENCODING": "utf-8"}, check=False) + total = time.perf_counter() - start + if proc.returncode != 0: + msg = f"local runner exited {proc.returncode}: {proc.stderr[-800:]}" + raise RuntimeError(msg) + payload = json.loads(proc.stdout.strip().splitlines()[-1]) + runs = payload["runs"] + n_unique = len(set(terms)) + return { + "n_terms": payload["n_terms"], + "n_unique_terms": n_unique, + "repeats": repeats, + "resolve_wall_s": runs, + "resolve_wall_s_median": statistics.median(runs), + "unique_terms_per_s_median": n_unique / statistics.median(runs), + "per_unique_term_ms_median": statistics.median(runs) / n_unique * 1000, + "resolved_rows": payload["resolved"], + "process_total_s_incl_startup": total, + "note": "resolve_batch dedups terms before the redb fetch; throughput is over unique terms", + } + + +def _fetch(req: urllib.request.Request, timeout: float) -> object: + """Open a request with two retries (exponential backoff) on transient errors.""" + last: Exception | None = None + for attempt in range(3): + try: + return urllib.request.urlopen(req, timeout=timeout) + except urllib.error.HTTPError as exc: + last = exc + if exc.code in (429, 500, 502, 503, 504) and attempt < 2: + time.sleep(2.0 * (attempt + 1)) + continue + raise + except urllib.error.URLError as exc: + last = exc + if attempt < 2: + time.sleep(2.0 * (attempt + 1)) + continue + raise + raise last if last else RuntimeError("unreachable") + + +def hosted_lookup(term: str, limit: int, timeout: float = 30.0, canonicalize: bool = False) -> dict: + """One term through the hosted flow. With canonicalize, runs the full identifier-record + workflow: /lookup for the string, then /synonyms on the top curie to fetch the + canonical identifier's preferred name and categories (two HTTP calls). + """ + url = f"{HOSTED_BASE}/lookup?string={urllib.parse.quote(term)}&limit={limit}" + start = time.perf_counter() + record: dict = {"term": term, "status": None, "latency_s": None, "curies": [], "canonical": None} + try: + req = urllib.request.Request(url, headers={"User-Agent": "tablassert-benchmark/1.0"}) + with _fetch(req, timeout) as resp: + body = json.loads(resp.read().decode("utf-8")) + record["status"] = resp.status + record["curies"] = [hit.get("curie") for hit in body[:limit] if hit.get("curie")] + if canonicalize and record["curies"]: + payload = json.dumps({"preferred_curies": [record["curies"][0]]}).encode("utf-8") + req = urllib.request.Request( + f"{HOSTED_BASE}/synonyms", data=payload, headers={"User-Agent": "tablassert-benchmark/1.0", "Content-Type": "application/json"} + ) + with _fetch(req, timeout) as resp: + syn = json.loads(resp.read().decode("utf-8")) + entry = syn.get(record["curies"][0], {}) + record["canonical"] = { + "curie": entry.get("curie"), + "preferred_name": entry.get("preferred_name"), + "types": entry.get("types", [])[:3], + } + except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: + record["status"] = f"error: {type(exc).__name__}" + finally: + record["latency_s"] = time.perf_counter() - start + return record + + +def bench_hosted(terms: list[str], concurrency: int, limit: int, canonicalize: bool = False) -> dict: + results: list[dict] = [] + wall_start = time.perf_counter() + with ThreadPoolExecutor(max_workers=concurrency) as pool: + futures = {pool.submit(hosted_lookup, term, limit, 30.0, canonicalize): term for term in terms} + for future in as_completed(futures): + results.append(future.result()) + wall = time.perf_counter() - wall_start + ok = [r for r in results if r["status"] == 200] + lat = [r["latency_s"] for r in ok] + got_canonical = sum(1 for r in ok if r.get("canonical")) + return { + "n_terms": len(terms), + "concurrency": concurrency, + "canonicalize": canonicalize, + "wall_s": wall, + "ok": len(ok), + "errors": len(results) - len(ok), + "coverage": len(ok) / len(results), + "canonical_records": got_canonical, + "latency_s": { + "median": statistics.median(lat) if lat else None, + "p95": sorted(lat)[int(len(lat) * 0.95) - 1] if lat else None, + "mean": statistics.mean(lat) if lat else None, + }, + "terms_per_s_effective": len(ok) / wall if wall else None, + "records": results, + "note": ( + "per-term /lookup + /synonyms, the full identifier-record workflow" + if canonicalize + else f"per-term HTTP GET, concurrency {concurrency}; a build would need one request per term" + ), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--terms", type=Path, required=True) + parser.add_argument("--fullmap", required=True) + parser.add_argument("--python", required=True, help="python with tablassert importable") + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--repeats", type=int, default=3) + parser.add_argument("--hosted-concurrency", type=int, default=6) + parser.add_argument("--limit", type=int, default=5) + parser.add_argument("--skip-hosted", action="store_true") + args = parser.parse_args() + + terms = read_terms(args.terms) + version = subprocess.run( + [args.python, "-c", 'from importlib.metadata import version; print(version("tablassert"))'], capture_output=True, text=True, check=True + ).stdout.strip() + + result: dict = { + "date_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "tablassert_version": version, + "fullmap": args.fullmap, + "hosted_endpoint": HOSTED_URL, + "local": bench_local(args.python, args.fullmap, terms, args.repeats), + } + if not args.skip_hosted: + result["hosted"] = bench_hosted(terms, args.hosted_concurrency, args.limit) + result["hosted_multihop"] = bench_hosted(terms, args.hosted_concurrency, args.limit, canonicalize=True) + + args.out.write_text(json.dumps(result, indent=2)) + local = result["local"] + print(f"local: {local['unique_terms_per_s_median']:.0f} unique terms/s (median of {local['repeats']} runs)") + if "hosted" in result: + hosted = result["hosted"] + med = hosted["latency_s"]["median"] + med_s = f"{med:.3f}" if med is not None else "n/a" + print(f"hosted: {hosted['ok']}/{hosted['n_terms']} ok, median latency {med_s}s/term") + multi = result.get("hosted_multihop") + if multi: + mmed = multi["latency_s"]["median"] + mmed_s = f"{mmed:.3f}" if mmed is not None else "n/a" + print(f"hosted multihop: {multi['ok']}/{multi['n_terms']} ok, median {mmed_s}s/term, canonical {multi['canonical_records']}") + print(f"wrote {args.out}") + + +if __name__ == "__main__": + main() diff --git a/paper/benchmarks/name_resolution/results.json b/paper/benchmarks/name_resolution/results.json new file mode 100644 index 00000000..c6a6702f --- /dev/null +++ b/paper/benchmarks/name_resolution/results.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 dedups terms before the redb fetch; throughput is over unique terms" + }, + "hosted": { + "n_terms": 113, + "concurrency": 4, + "canonicalize": false, + "wall_s": 19.931297813076526, + "ok": 113, + "errors": 0, + "coverage": 1.0, + "canonical_records": 0, + "latency_s": { + "median": 0.5529478010721505, + "p95": 1.8313573198392987, + "mean": 0.6997811785215033 + }, + "terms_per_s_effective": 5.669475267479219, + "records": [ + { + "term": "TECEFIDERA", + "status": 200, + "latency_s": 0.5529478010721505, + "curies": [], + "canonical": null + }, + { + "term": "PROACTIV MD ADAPALENE ACNE TREATMENT", + "status": 200, + "latency_s": 0.6023798598907888, + "curies": [ + "CHEBI:31174", + "MONDO:0011438", + "MONDO:0043468", + "HP:0040137", + "MONDO:0100486" + ], + "canonical": null + }, + { + "term": "Acne", + "status": 200, + "latency_s": 0.6336133070290089, + "curies": [ + "MONDO:0011438", + "MONDO:0043468", + "UMLS:C0718217", + "HP:0040137", + "MONDO:0100486" + ], + "canonical": null + }, + { + "term": "Multiple sclerosis", + "status": 200, + "latency_s": 0.7015277328900993, + "curies": [ + "MONDO:0005301", + "MONDO:0005314", + "MONDO:0000450", + "MONDO:0000451", + "MONDO:0005284" + ], + "canonical": null + }, + { + "term": "OTEZLA", + "status": 200, + "latency_s": 0.29291520407423377, + "curies": [ + "CHEBI:78540", + "UMLS:C6054746", + "UMLS:C6056148", + "UMLS:C6056149", + "UMLS:C3715230" + ], + "canonical": null + }, + { + "term": "Psoriasis", + "status": 200, + "latency_s": 0.4564642678014934, + "curies": [ + "MONDO:0005083", + "MP:0001193", + "MONDO:0013626", + "MONDO:0100491", + "MONDO:0022205" + ], + "canonical": null + }, + { + "term": "XARELTO", + "status": 200, + "latency_s": 0.49808530509471893, + "curies": [ + "CHEBI:68579", + "UMLS:C4699670", + "UMLS:C3871569", + "UMLS:C3856151", + "UNII:ESP6837WVA" + ], + "canonical": null + }, + { + "term": "PROACTIV MD DEEP CLEANSING FACE WASH", + "status": 200, + "latency_s": 0.6364710819907486, + "curies": [ + "UNII:U4THC4MHG7", + "UMLS:C3204537", + "MONDO:0004903", + "MONDO:0001430", + "HP:0002625" + ], + "canonical": null + }, + { + "term": "XARELTO XR", + "status": 200, + "latency_s": 0.4434441411867738, + "curies": [ + "CHEBI:207316", + "CHEBI:209098", + "PUBCHEM.COMPOUND:9932718", + "UNII:748C75726M", + "CHEBI:216666" + ], + "canonical": null + }, + { + "term": "Atrial fibrillation", + "status": 200, + "latency_s": 0.9523856230080128, + "curies": [ + "MONDO:0004981", + "MP:0008543", + "MONDO:1030011", + "HP:0004754", + "MONDO:0018054" + ], + "canonical": null + }, + { + "term": "Peritoneal dialysis", + "status": 200, + "latency_s": 0.2846020171418786, + "curies": [ + "UMLS:C0031139", + "UMLS:C3840095", + "UMLS:C0274418", + "UMLS:C0456239", + "UMLS:C2315425" + ], + "canonical": null + }, + { + "term": "Rivaroxaban", + "status": 200, + "latency_s": 0.7372435200959444, + "curies": [ + "CHEBI:68579", + "UNII:ESP6837WVA", + "UNII:5KS80AIP17", + "UNII:RJP4GEG36M", + "UNII:C25RP4RHW7" + ], + "canonical": null + }, + { + "term": "Rheumatoid arthritis", + "status": 200, + "latency_s": 0.5171178141608834, + "curies": [ + "MONDO:0008383", + "MP:0003561", + "UMLS:C0409651", + "MONDO:0004586", + "MONDO:0011429" + ], + "canonical": null + }, + { + "term": "XELJANZ", + "status": 200, + "latency_s": 0.8261600118130445, + "curies": [ + "CHEBI:71197", + "CHEBI:71200", + "UMLS:C4238144" + ], + "canonical": null + }, + { + "term": "ARELTO", + "status": 200, + "latency_s": 0.46929613780230284, + "curies": [], + "canonical": null + }, + { + "term": "DIANA LOW CALCIUM PERITONEAL DIALYSIS SOLUTION WITH DEXTROSE", + "status": 200, + "latency_s": 1.4494624058716, + "curies": [ + "CHEBI:3311", + "RXCUI:804919", + "CHEBI:3309", + "CHEBI:3310", + "CHEBI:9679" + ], + "canonical": null + }, + { + "term": "Thrombosis prophylaxis", + "status": 200, + "latency_s": 0.4879311821423471, + "curies": [ + "UMLS:C0877156", + "UMLS:C0877621", + "UMLS:C2164678", + "MONDO:0002907", + "UMLS:C0199242" + ], + "canonical": null + }, + { + "term": "PROACTIV MD DAILY OIL CONTROL SPF 30", + "status": 200, + "latency_s": 1.563650513999164, + "curies": [ + "PUBCHEM.COMPOUND:139291139", + "DRUGBANK:DB09422", + "UMLS:C2683411", + "CHEBI:140618", + "DRUGBANK:DB13961" + ], + "canonical": null + }, + { + "term": "Dialysis solution", + "status": 200, + "latency_s": 0.7960356632247567, + "curies": [ + "UMLS:C0569903", + "UMLS:C3505190", + "UMLS:C0571215", + "UMLS:C0731794", + "UMLS:C0353714" + ], + "canonical": null + }, + { + "term": "DIALUID", + "status": 200, + "latency_s": 0.5678330641239882, + "curies": [], + "canonical": null + }, + { + "term": "Hypermagnesemia", + "status": 200, + "latency_s": 0.2873357869684696, + "curies": [ + "HP:0002918", + "NCIT:C37969", + "NCIT:C143555", + "MP:0010092", + "UMLS:C1522135" + ], + "canonical": null + }, + { + "term": "PAXLOVID", + "status": 200, + "latency_s": 0.46476780716329813, + "curies": [ + "CHEBI:192712", + "UMLS:C5565871", + "UMLS:C5980300", + "UMLS:C5551350", + "UMLS:C5575086" + ], + "canonical": null + }, + { + "term": "COVID-19 treatment", + "status": 200, + "latency_s": 0.5526291611604393, + "curies": [ + "UMLS:C5244048", + "MONDO:0100096", + "MONDO:0100233", + 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"GeneOrGeneProduct", + "GeneOrGeneProductOrGeneFamily" + ] + } + }, + { + "term": "ADIPOQ", + "status": 200, + "latency_s": 0.5702806613408029, + "curies": [ + "NCBIGene:9370", + "NCBIGene:282865", + "NCBIGene:403625", + "NCBIGene:397660", + "NCBIGene:100481319" + ], + "canonical": { + "curie": "NCBIGene:9370", + "preferred_name": "ADIPOQ", + "types": [ + "Gene", + "GeneOrGeneProduct", + "GeneOrGeneProductOrGeneFamily" + ] + } + }, + { + "term": "cholesterol", + "status": 200, + "latency_s": 0.5637669791467488, + "curies": [ + "CHEBI:16113", + "PUBCHEM.COMPOUND:304", + "CHEBI:41321", + "MONDO:0005568", + "CHEBI:17495" + ], + "canonical": { + "curie": "CHEBI:16113", + "preferred_name": "Cholesterol", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "glucose", + "status": 200, + "latency_s": 1.003615217283368, + "curies": [ + "CHEBI:17234", + "EFO:0004468", + "UMLS:C5781949", + "UMLS:C3643617", + "MONDO:0001076" + ], + "canonical": { + "curie": "CHEBI:17234", + "preferred_name": "D-glucose", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "insulin", + "status": 200, + "latency_s": 1.0769514637067914, + "curies": [ + "UMLS:C0021641", + "PANTHER.FAMILY:PTHR11454:SF9", + "CHEBI:5931", + "NCBIGene:483665", + "NCBIGene:105918359" + ], + "canonical": { + "curie": "UMLS:C0021641", + "preferred_name": "Insulin", + "types": [ + "Protein", + "GeneProductMixin", + "Polypeptide" + ] + } + }, + { + "term": "triglycerides", + "status": 200, + "latency_s": 0.5608314769342542, + "curies": [ + "CHEBI:17855", + "UMLS:C4554056", + "EFO:0004530", + "DRUGBANK:DB13959", + "MONDO:0005347" + ], + "canonical": { + "curie": "CHEBI:17855", + "preferred_name": "triglyceride", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "butyric acid", + "status": 200, + "latency_s": 0.5563503289595246, + "curies": [ + "CHEBI:30772", + "CHEBI:40114", + "UNII:EH7DJJ4GUV", + "CHEBI:165404", + "UNII:B9QYA11V0F" + ], + "canonical": { + "curie": "CHEBI:30772", + "preferred_name": "Butyric Acid", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "propionic acid", + "status": 200, + "latency_s": 0.5593234249390662, + "curies": [ + "CHEBI:30768", + "CHEBI:85487", + "UNII:ELW4MN66TD", + "CHEBI:80605", + "CHEBI:189725" + ], + "canonical": { + "curie": "CHEBI:30768", + "preferred_name": "Propanoic acid", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "tryptophan", + "status": 200, + "latency_s": 0.5774715817533433, + "curies": [ + "CHEBI:16828", + "CHEBI:27897", + "UMLS:C0041249", + "UniProtKB:P23381-1", + "CHEBI:16296" + ], + "canonical": { + "curie": "CHEBI:16828", + "preferred_name": "Tryptophan", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "fecal microbiota transplant", + "status": 200, + "latency_s": 0.5912494990043342, + "curies": [ + "DRUGBANK:DB17743", + "UMLS:C5231239", + "RXCUI:2636538", + "MONDO:0019088", + "MONDO:0958127" + ], + "canonical": { + "curie": "DRUGBANK:DB17743", + "preferred_name": "Fecal microbiota", + "types": [ + "ChemicalEntity", + "PhysicalEssence", + "ChemicalOrDrugOrTreatment" + ] + } + }, + { + "term": "probiotics", + "status": 200, + "latency_s": 0.5779990069568157, + "curies": [ + "MESH:D019936", + "UMLS:C4718264", + "UMLS:C5762415", + "UMLS:C5762184", + "UMLS:C4489960" + ], + "canonical": { + "curie": "MESH:D019936", + "preferred_name": "Probiotics", + "types": [ + "OrganismTaxon", + "NamedThing", + "Entity" + ] + } + }, + { + "term": "atororvastatin", + "status": 200, + "latency_s": 0.2554439380764961, + "curies": [], + "canonical": null + }, + { + "term": "antibiotics", + "status": 200, + "latency_s": 0.5399456550367177, + "curies": [ + "UMLS:C0003232", + "UMLS:C3540706", + "UMLS:C3540704", + "UMLS:C3540708", + "UMLS:C3540709" + ], + "canonical": { + "curie": "UMLS:C0003232", + "preferred_name": "Antibiotics", + "types": [ + "ChemicalEntity", + "PhysicalEssence", + "ChemicalOrDrugOrTreatment" + ] + } + }, + { + "term": "trimethoprim", + "status": 200, + "latency_s": 0.5520304250530899, + "curies": [ + "CHEBI:45924", + "UNII:E377MF8EQ8", + "UNII:9XE000OU9B", + "UNII:P3K8GP9FDQ", + "PUBCHEM.COMPOUND:64931" + ], + "canonical": { + "curie": "CHEBI:45924", + "preferred_name": "Trimethoprim", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "fluoxetine", + "status": 200, + "latency_s": 0.6980557153001428, + "curies": [ + "CHEBI:5118", + "CHEBI:145458", + "CHEBI:86991", + "CHEBI:86992", + "CHEBI:86996" + ], + "canonical": { + "curie": "CHEBI:5118", + "preferred_name": "Fluoxetine", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "lisinopril", + "status": 200, + "latency_s": 0.6044839429669082, + "curies": [ + "CHEBI:43755", + "UMLS:C0065374", + "PUBCHEM.COMPOUND:118984969", + "CHEBI:6503", + "PUBCHEM.COMPOUND:22887897" + ], + "canonical": { + "curie": "CHEBI:43755", + "preferred_name": "Lisinopril", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "vitamin D", + "status": 200, + "latency_s": 0.5659632701426744, + "curies": [ + "UNII:9VU1KI44GP", + "MONDO:0100471", + "UniProtKB:P02774-1", + "UniProtKB:A0A9J8AKB7", + "MONDO:0024299" + ], + "canonical": { + "curie": "UNII:9VU1KI44GP", + "preferred_name": "VITAMIN D", + "types": [ + "ChemicalEntity", + "PhysicalEssence", + "ChemicalOrDrugOrTreatment" + ] + } + }, + { + "term": "folic acid", + "status": 200, + "latency_s": 0.651567171793431, + "curies": [ + "CHEBI:27470", + "PUBCHEM.COMPOUND:135444779", + "MONDO:0001860", + "PUBCHEM.COMPOUND:135718460", + "CHEBI:89846" + ], + "canonical": { + "curie": "CHEBI:27470", + "preferred_name": "Folic acid", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "iron", + "status": 200, + "latency_s": 0.7586536318995059, + "curies": [ + "CHEBI:18248", + "NCBIGene:1254300", + "NCBIGene:93313788", + "NCBIGene:305629263", + "CHEBI:192761" + ], + "canonical": { + "curie": "CHEBI:18248", + "preferred_name": "Iron", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "hepcidin", + "status": 200, + "latency_s": 0.5664786309935153, + "curies": [ + "PUBCHEM.COMPOUND:91864521", + "PUBCHEM.COMPOUND:139033195", + "UMLS:C0966897", + "PR:000008438", + "PANTHER.FAMILY:PTHR16877:SF0" + ], + "canonical": { + "curie": "PUBCHEM.COMPOUND:91864521", + "preferred_name": "Hepcidin", + "types": [ + "SmallMolecule", + "MolecularEntity", + "OntologyClass" + ] + } + }, + { + "term": "C-reactive protein", + "status": 200, + "latency_s": 0.5787405050359666, + "curies": [ + "UMLS:C0006560", + "PR:000005897", + "PANTHER.FAMILY:PTHR45869:SF7", + "UNII:SCI18J406O", + "NCBIGene:108892153" + ], + "canonical": { + "curie": "UMLS:C0006560", + "preferred_name": "C-reactive protein", + "types": [ + "Protein", + "GeneProductMixin", + "Polypeptide" + ] + } + }, + { + "term": "ferritin", + "status": 200, + "latency_s": 0.5910400957800448, + "curies": [ + "NCBIGene:100205436", + "UMLS:C0015879", + "CHEBI:82594", + "PANTHER.FAMILY:PTHR11431", + "EFO:0004459" + ], + "canonical": { + "curie": "NCBIGene:100205436", + "preferred_name": "ferritin", + "types": [ + "Gene", + "GeneOrGeneProduct", + "GeneOrGeneProductOrGeneFamily" + ] + } + }, + { + "term": "interleukin-6", + "status": 200, + "latency_s": 0.5613872949033976, + "curies": [ + "UMLS:C0021760", + "PR:000001393", + "PANTHER.FAMILY:PTHR48494:SF1", + "PANTHER.FAMILY:PTHR48494", + "NCBIGene:111545050" + ], + "canonical": { + "curie": "UMLS:C0021760", + "preferred_name": "interleukin-6", + "types": [ + "Protein", + "GeneProductMixin", + "Polypeptide" + ] + } + }, + { + "term": "tumor necrosis factor", + "status": 200, + "latency_s": 0.5639351420104504, + "curies": [ + "UniProtKB:Q99732-1", + "PR:000000134", + "PANTHER.FAMILY:PTHR11471:SF23", + "UniProtKB:Q9BXJ1-1", + "UniProtKB:Q92956-1" + ], + "canonical": { + "curie": "UniProtKB:Q99732-1", + "preferred_name": "lipopolysaccharide-induced tumor necrosis factor-alpha factor isoform 1 (human)", + "types": [ + "Protein", + "GeneProductMixin", + "Polypeptide" + ] + } + } + ], + "note": "per-term /lookup + /synonyms, the full identifier-record workflow" + } +} \ No newline at end of file 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/paper.bib b/paper/paper.bib index 0f87441d..68c1b946 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -64,6 +64,7 @@ @inproceedings{sapbert } @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}, @@ -74,7 +75,7 @@ @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 2.6.4, released October 2, 2026}, + note = {Version 2.7.0, released October 21, 2026}, howpublished = {\url{https://github.com/monarch-initiative/koza}} } @@ -96,11 +97,47 @@ @misc{dakp @misc{drug-approvals, author = {Goetz, Skye Lane}, - title = {Drug Approvals Knowledge Provider ({DAKP}) dataset, version 1.24.1}, + 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{linkml, + title = {{LinkML}: Linked Data Modeling Language}, + year = {2026}, + note = {Schema framework for knowledge-graph data}, + howpublished = {\url{https://github.com/linkml/linkml}} +} + +@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, + 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}, diff --git a/paper/paper.md b/paper/paper.md index 95e3dfdd..185d557a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,12 +55,7 @@ naming the offending field, rather than surfacing as a malformed graph hours lat # State of the field -The prevailing practice for Translator ingests is bespoke Python per data source. Generic KGX -tooling [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza -[@koza], the Monarch Initiative's ingest engine, pairs YAML configuration with semi-declarative -Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. -Hosted Translator name resolution [@babel] resolves identifiers as a network service, which makes -build-time bulk lookups slow and builds themselves irreproducible without network access. +The prevailing practice for Translator ingests is bespoke Python per data source, with generic tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a finished knowledge graph generically, but a schema says nothing about how a spreadsheet column becomes a node. The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a network API, which makes build-time bulk lookups slow and builds themselves irreproducible without network access. Extending those tools would not remove the cost Tablassert targets: the repeated ingest code, the hosted-resolution dependency, and the lack of integrated mapping-quality checks. Tablassert integrates all three into one recipe-driven pipeline, and contributing resolution or audit stages @@ -83,42 +78,113 @@ set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chose analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a parallel, memory-bounded streaming pass, and partitioned into shards that serve concurrent batch lookups. Restricting resolution to BABEL-covered entities is deliberate: what does not map is -reported, not guessed, and metadata arrives with the identifier. A prebuilt fullmap can be -downloaded from RENCI alongside building one locally. - -Every emitted edge carries structured provenance -- source record URLs, supporting publications, -and the processing context that produced it -- so an assertion can be traced back to the exact row -and evidence that support it. Optional quality control audits each resolved name with a four-stage -cascade: exact or alias match, fuzzy string similarity, Schwartz-Hearst abbreviation expansion, -then SapBERT [@sapbert] embedding similarity, each stage handling only what cheaper stages could -not clear. Suspect mappings are dropped and logged with their scores rather than silently -retained, which in practice catches resolution errors that upstream synonym sources are slow to -fix. Final output is validated against category-specific Biolink schema classes, so a passing -build means the emitted graph, not just its configuration, is well-formed. Tablassert is a Python -package with a PyO3 Rust extension; the resolution engine, database build, and NDJSON writer run -in Rust while recipes and validation remain Python. +reported, not guessed, and metadata arrives with the identifier. Resolution is exact-match over +normalized keys rather than fuzzy search -- approximate matches surface only in the audit stage, +not in the graph -- because fuzzy resolution returns plausible-but-wrong identifiers often enough +to corrupt a graph silently. The build applies a fixed allowlist of the top 100 experimental taxa +derived from Translator knowledge-graph node frequencies, alongside all OrganismTaxon rows and +rows without taxon metadata: a deliberate trade-off that shrinks the database but drops +species-specific identifiers outside the allowlist. A prebuilt fullmap can be downloaded from +RENCI alongside building one locally. + +Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own +constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species while +retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also drops +categories the installed model does not recognize), and CURIE prefix and regex exclusions. +Surviving candidates are ranked by a priority score -- exact preferred-name equality ranks 1, +level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multiplied by a category +bonus of 1 for prioritized categories and 50 otherwise -- then deduplicated to the best tier, +retaining distinct CURIEs that tie. When column context is on, category frequency across the +column being resolved breaks remaining ties, so a column of gene symbols resolves ambiguous short +names as genes. These are query shapes the hosted API does not offer: it takes a string and returns +ranked identifiers, with no taxon constraint, category policy, or per-column context, and the full +identifier-record workflow (string to identifier, then canonicalization for preferred name and +category) takes two HTTP calls per term where the fullmap returns all of it in one batched fetch. +Benchmarks shipped in `paper/benchmarks/` quantify this on 113 real ingest terms: local resolution +sustained 171 unique terms per second, while the hosted workflow spent a median of 0.55 seconds +per call. + +Every edge carries recipe-declared structured provenance -- source record URLs, supporting +publications, and the processing context that produced it -- so an assertion can be traced back to +the exact row and evidence that support it. Optional quality control audits each resolved name +with a four-stage cascade: exact or alias match, fuzzy string similarity, Schwartz-Hearst +abbreviation expansion, then SapBERT [@sapbert] embedding similarity using a checkpoint fine-tuned +on BABEL synonyms (`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`), each +stage handling only what cheaper stages could not clear. Suspect mappings are dropped and logged +with their scores rather than silently retained; in the DAKP production builds this audit covers +the full graph -- 1,661,094 audited rows in the current release -- which in practice catches +resolution errors that upstream synonym sources are slow to fix. Output correctness is checked in +two passes: an optional study stage asserts structural invariants over the emitted NDJSON (no +duplicate or isolated nodes, no empty required fields, no edges demoted to bare Association), and +the `validate-kgx` command validates every node and edge against category-specific Biolink schema +classes, so the emitted graph, not just its configuration, can be verified well-formed. Tablassert +is a Python package with a PyO3 Rust extension; the resolution engine, database build, and NDJSON +writer run in Rust while recipes and validation remain Python. The same benchmark folder contrasts +a Koza transform of the tutorial example (79 lines of YAML plus Python, no resolution) with the +32-line Tablassert recipe: on 10,000 rows Tablassert took 6.9 seconds including resolution against +the fullmap, Koza 5.4 seconds without it. # Research impact statement Tablassert and its declarative precursor underlie several production knowledge graphs. The published MicrobiomeKG [@microbiomekg], a Translator-deployed graph built from supplementary -tables of 40 publications, was produced with an early unnamed precursor; its current maintenance -builds use present-day Tablassert, as do the Microbiome and Multiomics knowledge graph -configurations of the MOKG project. DAKP [@dakp], the Drug Approvals Knowledge Provider, applies -Tablassert to regulatory drug-approval sources and publishes validated, versioned releases to a -public dataset archive [@drug-approvals]; a public linkouts interface exposes its per-edge -evidence, and integration into the Translator ingest infrastructure is under review [@ingests]. +tables of 40 publications, was produced with an early unnamed precursor; the deployed Microbiome +and Multiomics knowledge graphs [@multiomics-kp] and their maintenance builds use present-day +Tablassert. DAKP [@dakp], the Drug Approvals Knowledge Provider (now developed in the glusman-team +graph monorepo [@graph-monorepo]), applies Tablassert to regulatory drug-approval sources and +publishes validated, versioned releases to a public dataset archive [@drug-approvals]; a public +linkouts interface exposes its per-edge evidence, and integration into the Translator ingest +infrastructure is under review [@ingests], beginning with a Tablassert passthrough for the +Multiomics graph [@ingests-mokg]. KRAKEN [@kraken], Phenome Health's unified knowledge graph, integrates the Microbiome, Multiomics, and Drug Approvals graphs as domain-specific sources. A rare-disease multiomics knowledge graph built with Tablassert is in preparation. # AI usage disclosure -TODO(collaborators): state which generative AI tools (with versions) assisted the software, -tests, documentation, and this manuscript, and how outputs were verified by the authors. +Generative AI assisted with code generation, test writing, documentation, and drafting and +revising this manuscript, primarily through a customized pi coding agent (version 0.81.1). AI spelling and +grammar checks were used for the documentation and manuscript; Grammarly also assisted with +the manuscript. AI coding-agent use began on 27 January 2026, marked by commit `50f29e8`, +which credits Claude Sonnet 4.5; subsequent commits also credit Claude Opus 4.5. Initial +software versions were developed without AI coding agents. + +Commit co-author credits additionally record Claude Sonnet 4.5, Claude Opus 4.5, and +Claude Opus 4.6, with context-window variants grouped under the same model version. +Models used included the following recorded identifiers: +`claude-opus-5`, `claude-opus-5-5`, +`claude-sonnet-5`, `claude-sonnet-5-5`, `deepseek-v4-pro`, `deepseek-v4-flash`, +`gemini-3.8-flash`, `glm-5.3`, +`glm-5.3-flash`, `gpt-5.6-luna`, `gpt-5.6-sol-review`, `gpt-5.6-terra`, `gpt-6-astra`, +`gpt-6-luna`, `gpt-6-sol`, `gpt-6.1-sol`, `kimi-k2.5`, `kimi-k2.6`, `kimi-k2.7-pro`, +`kimi-k2.8-preview`, `kimi-k3`, `kimi-k3.1-pro`, +`muse-spark-1.3-contributor-free`, `nemotron-3-ultra-550b-a55b:free`, +`laguna-s-2.1`, `qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and +`mimo-v2.6-pro`. + +A bespoke LLM-as-judge implementation reviewed code before pull requests, and CodeRabbit +reviewed pull requests before Goetz's own review. Validation combined manual review and +auditing with automated tests, CI/CD pipelines, custom linting rules written by Goetz, and +scripted inclusion checks for required output elements. Git pre-commit hooks provided earlier +checks to shorten CI feedback, and language servers provided development-time diagnostics. +Detailed prompt engineering, with promptfoo (version 0.121.17) and GEPA (version 0.1.4) used to +optimize prompts for desired +outputs, supported output control; LLM-as-judge evaluations and scripted inclusion checks +assessed outputs. Prompt optimization and AI-based evaluation were not treated as independent +proof of correctness. These checks supplemented, rather than replaced, human review. +Skye Lane Goetz made all software architecture decisions and manually reviewed and audited +all changes, including AI-assisted outputs. The authors retain full responsibility for the +accuracy, originality, licensing, and ethical and legal compliance of the submitted software +and manuscript. # Acknowledgements -TODO(collaborators): funding awards and contributor acknowledgements go here. +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, OT2TR003428, 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. + +TODO(collaborators): contributor acknowledgements and sponsor-involvement statement go here. # References From df645a512e209ecb95840a015c3cab7579be4b57 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Wed, 28 Oct 2026 14:04:24 -0700 Subject: [PATCH 06/25] docs(paper): dedicated Benchmarks section, KGX-eval comparison, fullmap sizes - Move benchmark numbers into a Benchmarks section - validate-kgx vs KGX schema-driven validate; --prune repair mode - Fullmap sizes: 44.5 GB prebuilt (BABEL 22.2.0), 24.7 GB local store - Trim to stay inside the JOSS body budget --- paper/paper.md | 51 ++++++++++++++++++++++++++++---------------------- 1 file changed, 29 insertions(+), 22 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 185d557a..0b6fbfa5 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -33,7 +33,7 @@ each table's columns map to subject-predicate-object statements; Tablassert reso ("TP53", "lung cancer") to compact uniform resource identifiers (CURIEs), attaches provenance and statistical annotations, and emits KGX-compliant [@kgx] node and edge files carrying Biolink Model categories [@biolink-model]. The same recipe can be inspected, reused, and refined, so a graph -starts small and improves iteratively instead of being frozen inside a bespoke ingest script. +starts small instead of being frozen inside a bespoke ingest script. # Statement of need @@ -49,17 +49,17 @@ Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabu sources and need rigorously structured output without maintaining a transform pipeline per source. Beyond journal supplements, the same recipes apply to any relational source -- trial registries, drug approval records, or a researcher's own tables -- and each build can emit the Resource Ingest -Guide metadata that Translator submissions require. Recipes are validated with Pydantic before a -build starts, so a typo or an impossible combination of options fails immediately with an error -naming the offending field, rather than surfacing as a malformed graph hours later. +Guide metadata that Translator submissions require. Recipes are validated before a build +starts, so a typo fails immediately with an error naming the field, rather than surfacing as a +malformed graph hours later. # State of the field The prevailing practice for Translator ingests is bespoke Python per data source, with generic tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a finished knowledge graph generically, but a schema says nothing about how a spreadsheet column becomes a node. The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a network API, which makes build-time bulk lookups slow and builds themselves irreproducible without network access. -Extending those tools would not remove the cost Tablassert targets: the repeated ingest code, the -hosted-resolution dependency, and the lack of integrated mapping-quality checks. Tablassert -integrates all three into one recipe-driven pipeline, and contributing resolution or audit stages -back to Koza would not produce a single declarative YAML mapping covering both. +Extending those tools would not remove the cost Tablassert targets: repeated ingest code, the +hosted-resolution dependency, and no integrated mapping-quality checks. Tablassert integrates all +three into one recipe-driven pipeline; contributing resolution or audit stages back to Koza would +still not yield a single declarative mapping. # Software design @@ -77,15 +77,15 @@ vocabularies, onto canonical identifiers with category and taxon metadata. The s set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen over analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a parallel, memory-bounded streaming pass, and partitioned into shards that serve concurrent batch -lookups. Restricting resolution to BABEL-covered entities is deliberate: what does not map is +lookups. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 GB compressed; the redb +store used here, built from the 2026-07-22 export, occupies 24.7 GB. What does not map is reported, not guessed, and metadata arrives with the identifier. Resolution is exact-match over normalized keys rather than fuzzy search -- approximate matches surface only in the audit stage, not in the graph -- because fuzzy resolution returns plausible-but-wrong identifiers often enough to corrupt a graph silently. The build applies a fixed allowlist of the top 100 experimental taxa derived from Translator knowledge-graph node frequencies, alongside all OrganismTaxon rows and rows without taxon metadata: a deliberate trade-off that shrinks the database but drops -species-specific identifiers outside the allowlist. A prebuilt fullmap can be downloaded from -RENCI alongside building one locally. +species-specific identifiers outside the allowlist. Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species while @@ -99,10 +99,8 @@ column being resolved breaks remaining ties, so a column of gene symbols resolve names as genes. These are query shapes the hosted API does not offer: it takes a string and returns ranked identifiers, with no taxon constraint, category policy, or per-column context, and the full identifier-record workflow (string to identifier, then canonicalization for preferred name and -category) takes two HTTP calls per term where the fullmap returns all of it in one batched fetch. -Benchmarks shipped in `paper/benchmarks/` quantify this on 113 real ingest terms: local resolution -sustained 171 unique terms per second, while the hosted workflow spent a median of 0.55 seconds -per call. +category) takes two HTTP calls per term where the fullmap returns all of it in one batched fetch +(see Benchmarks). Every edge carries recipe-declared structured provenance -- source record URLs, supporting publications, and the processing context that produced it -- so an assertion can be traced back to @@ -115,14 +113,23 @@ with their scores rather than silently retained; in the DAKP production builds t the full graph -- 1,661,094 audited rows in the current release -- which in practice catches resolution errors that upstream synonym sources are slow to fix. Output correctness is checked in two passes: an optional study stage asserts structural invariants over the emitted NDJSON (no -duplicate or isolated nodes, no empty required fields, no edges demoted to bare Association), and -the `validate-kgx` command validates every node and edge against category-specific Biolink schema -classes, so the emitted graph, not just its configuration, can be verified well-formed. Tablassert +duplicate or isolated nodes, no empty required fields, no edges demoted to bare Association). The +`validate-kgx` command goes beyond KGX's schema-driven `validate` [@kgx]: every node and edge is +constructed as the Biolink class its own category names, knowledge-source identity is checked +against the Translator infores registry offline, and `--prune` rewrites the edges file in place to +its strictly-compliant subset, with the exit code gating the post-prune state. Tablassert is a Python package with a PyO3 Rust extension; the resolution engine, database build, and NDJSON -writer run in Rust while recipes and validation remain Python. The same benchmark folder contrasts -a Koza transform of the tutorial example (79 lines of YAML plus Python, no resolution) with the -32-line Tablassert recipe: on 10,000 rows Tablassert took 6.9 seconds including resolution against -the fullmap, Koza 5.4 seconds without it. +writer run in Rust while recipes and validation remain Python. + +# Benchmarks + +Raw results ship in `paper/benchmarks/`. On 113 real ingest terms, local +fullmap resolution sustained 171 unique terms per second through the build's batch path, +returning the full identifier record in one fetch; the hosted SRI service spent a median of 0.55 +seconds per call, and its two-call identifier-record workflow 0.58 seconds per term. The Koza +contrast maps the tutorial example both ways: 79 lines of YAML plus Python with no entity +resolution, versus the 32-line Tablassert recipe (10,000 rows: 6.9 seconds including resolution +against the fullmap, 5.4 seconds without it). # Research impact statement From 29103e03cda8b9b87dfded629783a8753c585dba Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Wed, 28 Oct 2026 14:08:28 -0700 Subject: [PATCH 07/25] docs(paper): correct fullmap store size to full 17-file total (92.9 GB) The 24.7 GB figure was only the primary redb file; the store is one primary plus 16 shard files, 92,881,358,848 bytes uncompressed. Benchmark README now carries the per-component breakdown. --- paper/benchmarks/name_resolution/README.md | 12 +++++++++++- paper/paper.md | 6 +++--- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/paper/benchmarks/name_resolution/README.md b/paper/benchmarks/name_resolution/README.md index 12265a88..16c2e8b8 100644 --- a/paper/benchmarks/name_resolution/README.md +++ b/paper/benchmarks/name_resolution/README.md @@ -17,7 +17,17 @@ Hosted flows measured: fullmap fetch returns per term (canonical CURIE, preferred name, category, taxon, source). -## Results (wenceslaus, 2026-10-28; tablassert 19.7.0; fullmap source 2026jul22, 24 GB) +## Results (wenceslaus, 2026-10-28; tablassert 19.7.0; fullmap source 2026jul22) + +Fullmap store, uncompressed on disk (exact bytes via `stat`): + +| Component | Bytes | GB (decimal) | +| ------------------------------------ | ------------------ | -------------- | +| `fullmap` (primary redb) | 24,741,679,104 | 24.74 | +| `fullmap.s0..s15.redb` (16 shards) | 68,139,679,744 | 68.14 | +| Total store | 92,881,358,848 | 92.88 (86.50 GiB) | + +(`downloads/` in the same directory holds 38.08 GB of BABEL source exports; not part of the database.) See `results.json` for full detail; medians of 3 local repeats, hosted at concurrency 4 with retry/backoff: diff --git a/paper/paper.md b/paper/paper.md index 0b6fbfa5..85ae90dd 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -76,9 +76,9 @@ maps normalized synonyms, including the meta-identifiers that unify the same con vocabularies, onto canonical identifiers with category and taxon metadata. The store itself is a set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen over analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a -parallel, memory-bounded streaming pass, and partitioned into shards that serve concurrent batch -lookups. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 GB compressed; the redb -store used here, built from the 2026-07-22 export, occupies 24.7 GB. What does not map is +parallel, memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 GB compressed; the redb +store used here, built from the 2026-07-22 export, occupies 92.9 GB across one primary and 16 +shard files. What does not map is reported, not guessed, and metadata arrives with the identifier. Resolution is exact-match over normalized keys rather than fuzzy search -- approximate matches surface only in the audit stage, not in the graph -- because fuzzy resolution returns plausible-but-wrong identifiers often enough From 8cd0b84b4033ae887b45ec0132190a163e7e71a5 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Wed, 28 Oct 2026 14:32:45 -0700 Subject: [PATCH 08/25] docs(paper): fold benchmarks into Software design; cite LinkML, BioCypher, OntoWeaver, Monarch JOSS's official template lists only Summary, Statement of need, State of the field, Software design, Research impact, AI usage disclosure, Acknowledgements, and References, so the standalone Benchmarks section is folded into Software design where the numbers support design claims. New citations replace the LinkML repo entry with the GigaScience paper and add BioCypher (Nat Biotechnol 2023), OntoWeaver/BioCypher tabular-to-KG mapping (FUSION 2024), and Monarch 2024 (NAR) to State of the field. Trim AI disclosure redundancy to stay inside the 1750-word budget. --- paper/paper.bib | 42 +++++++++++++++++++++++++++++++++++++----- paper/paper.md | 43 ++++++++++++++++++------------------------- 2 files changed, 55 insertions(+), 30 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 68c1b946..ee3e117c 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -109,11 +109,43 @@ @misc{nameres howpublished = {\url{https://github.com/NCATSTranslator/NameResolution}} } -@misc{linkml, - title = {{LinkML}: Linked Data Modeling Language}, - year = {2026}, - note = {Schema framework for knowledge-graph data}, - howpublished = {\url{https://github.com/linkml/linkml}} +@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{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, diff --git a/paper/paper.md b/paper/paper.md index 85ae90dd..4eabfc08 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,7 +55,7 @@ malformed graph hours later. # State of the field -The prevailing practice for Translator ingests is bespoke Python per data source, with generic tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a finished knowledge graph generically, but a schema says nothing about how a spreadsheet column becomes a node. The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a network API, which makes build-time bulk lookups slow and builds themselves irreproducible without network access. +The prevailing practice for Translator ingests is bespoke Python per data source, with generic tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a finished knowledge graph generically, but a schema says nothing about how a spreadsheet column becomes a node. BioCypher [@biocypher] standardizes biomedical graph construction, and with OntoWeaver maps tabular data into semantic graphs [@ontoweaver], but both assume a curated ontology-aligned model rather than ad hoc research tables. The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a network API, which makes build-time bulk lookups slow and builds themselves irreproducible without network access. Extending those tools would not remove the cost Tablassert targets: repeated ingest code, the hosted-resolution dependency, and no integrated mapping-quality checks. Tablassert integrates all three into one recipe-driven pipeline; contributing resolution or audit stages back to Koza would @@ -99,8 +99,10 @@ column being resolved breaks remaining ties, so a column of gene symbols resolve names as genes. These are query shapes the hosted API does not offer: it takes a string and returns ranked identifiers, with no taxon constraint, category policy, or per-column context, and the full identifier-record workflow (string to identifier, then canonicalization for preferred name and -category) takes two HTTP calls per term where the fullmap returns all of it in one batched fetch -(see Benchmarks). +category) takes two HTTP calls per term where the fullmap returns all of it in one batched +fetch. Benchmarks in `paper/benchmarks/` quantify the gap on 113 real ingest terms: local +resolution sustained 171 unique terms per second through the build's exact batch path, versus a +median of 0.55 seconds per hosted call (0.58 seconds for the two-call workflow). Every edge carries recipe-declared structured provenance -- source record URLs, supporting publications, and the processing context that produced it -- so an assertion can be traced back to @@ -119,17 +121,10 @@ constructed as the Biolink class its own category names, knowledge-source identi against the Translator infores registry offline, and `--prune` rewrites the edges file in place to its strictly-compliant subset, with the exit code gating the post-prune state. Tablassert is a Python package with a PyO3 Rust extension; the resolution engine, database build, and NDJSON -writer run in Rust while recipes and validation remain Python. - -# Benchmarks - -Raw results ship in `paper/benchmarks/`. On 113 real ingest terms, local -fullmap resolution sustained 171 unique terms per second through the build's batch path, -returning the full identifier record in one fetch; the hosted SRI service spent a median of 0.55 -seconds per call, and its two-call identifier-record workflow 0.58 seconds per term. The Koza -contrast maps the tutorial example both ways: 79 lines of YAML plus Python with no entity -resolution, versus the 32-line Tablassert recipe (10,000 rows: 6.9 seconds including resolution -against the fullmap, 5.4 seconds without it). +writer run in Rust while recipes and validation remain Python. The Koza contrast +in `paper/benchmarks/` maps the tutorial example both ways: 79 lines of YAML plus Python with no +entity resolution, against the 32-line Tablassert recipe (10,000 rows: 6.9 seconds including +resolution against the fullmap, 5.4 seconds without it). # Research impact statement @@ -150,18 +145,16 @@ built with Tablassert is in preparation. # AI usage disclosure Generative AI assisted with code generation, test writing, documentation, and drafting and -revising this manuscript, primarily through a customized pi coding agent (version 0.81.1). AI spelling and -grammar checks were used for the documentation and manuscript; Grammarly also assisted with -the manuscript. AI coding-agent use began on 27 January 2026, marked by commit `50f29e8`, -which credits Claude Sonnet 4.5; subsequent commits also credit Claude Opus 4.5. Initial -software versions were developed without AI coding agents. - -Commit co-author credits additionally record Claude Sonnet 4.5, Claude Opus 4.5, and -Claude Opus 4.6, with context-window variants grouped under the same model version. +revising this manuscript, primarily through a customized pi coding agent (version 0.81.1). AI +spelling and grammar checks were used for the documentation and manuscript; Grammarly also +assisted with the manuscript. AI coding-agent use began on 27 January 2026, marked by commit +`50f29e8`; commit co-author credits record Claude Sonnet 4.5, Claude Opus 4.5, and Claude Opus +4.6 (context-window variants grouped per version). Initial software versions were developed +without AI coding agents. + Models used included the following recorded identifiers: -`claude-opus-5`, `claude-opus-5-5`, -`claude-sonnet-5`, `claude-sonnet-5-5`, `deepseek-v4-pro`, `deepseek-v4-flash`, -`gemini-3.8-flash`, `glm-5.3`, +`claude-opus-5`, `claude-opus-5-5`, `claude-sonnet-5`, `claude-sonnet-5-5`, +`deepseek-v4-pro`, `deepseek-v4-flash`, `gemini-3.8-flash`, `glm-5.3`, `glm-5.3-flash`, `gpt-5.6-luna`, `gpt-5.6-sol-review`, `gpt-5.6-terra`, `gpt-6-astra`, `gpt-6-luna`, `gpt-6-sol`, `gpt-6.1-sol`, `kimi-k2.5`, `kimi-k2.6`, `kimi-k2.7-pro`, `kimi-k2.8-preview`, `kimi-k3`, `kimi-k3.1-pro`, From de50561efbddecb4806ecf36d88e0997b4c0d1db Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Wed, 28 Oct 2026 14:36:33 -0700 Subject: [PATCH 09/25] docs(paper): cite SPIRES in State of the field Adds Caufield et al., Bioinformatics 40:btae104 (2024), contrasted as free-text zero-shot extraction against Tablassert's structured-table sources. Compensating trims keep the body inside the JOSS word budget. --- paper/paper.bib | 10 ++++++++++ paper/paper.md | 21 ++++++++++++++++----- 2 files changed, 26 insertions(+), 5 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index ee3e117c..adb1539d 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -138,6 +138,16 @@ @inproceedings{ontoweaver 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{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}, diff --git a/paper/paper.md b/paper/paper.md index 4eabfc08..34dc859f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,8 +55,19 @@ malformed graph hours later. # State of the field -The prevailing practice for Translator ingests is bespoke Python per data source, with generic tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a finished knowledge graph generically, but a schema says nothing about how a spreadsheet column becomes a node. BioCypher [@biocypher] standardizes biomedical graph construction, and with OntoWeaver maps tabular data into semantic graphs [@ontoweaver], but both assume a curated ontology-aligned model rather than ad hoc research tables. The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a network API, which makes build-time bulk lookups slow and builds themselves irreproducible without network access. -Extending those tools would not remove the cost Tablassert targets: repeated ingest code, the +The prevailing practice for Translator ingests is bespoke Python per data source, with generic +tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but +provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] ingest +engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but +leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a +finished knowledge graph generically, but a schema says nothing about how a spreadsheet column +becomes a node. BioCypher [@biocypher] standardizes biomedical graph construction, and with +OntoWeaver maps tabular data into semantic graphs [@ontoweaver], but both assume a curated +ontology-aligned model rather than ad hoc research tables. SPIRES [@spires] extracts knowledge +from free text by zero-shot prompting, while Tablassert's sources are already structured tables. +The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a +network API, which makes bulk build-time lookups slow and irreproducible offline. +None of these removes the cost Tablassert targets: repeated ingest code, the hosted-resolution dependency, and no integrated mapping-quality checks. Tablassert integrates all three into one recipe-driven pipeline; contributing resolution or audit stages back to Koza would still not yield a single declarative mapping. @@ -101,12 +112,12 @@ ranked identifiers, with no taxon constraint, category policy, or per-column con identifier-record workflow (string to identifier, then canonicalization for preferred name and category) takes two HTTP calls per term where the fullmap returns all of it in one batched fetch. Benchmarks in `paper/benchmarks/` quantify the gap on 113 real ingest terms: local -resolution sustained 171 unique terms per second through the build's exact batch path, versus a +resolution sustained 171 unique terms per second through the build's batch path, versus a median of 0.55 seconds per hosted call (0.58 seconds for the two-call workflow). Every edge carries recipe-declared structured provenance -- source record URLs, supporting -publications, and the processing context that produced it -- so an assertion can be traced back to -the exact row and evidence that support it. Optional quality control audits each resolved name +publications, and the processing context that produced it -- so an assertion traces back to its +row and evidence. Optional quality control audits each resolved name with a four-stage cascade: exact or alias match, fuzzy string similarity, Schwartz-Hearst abbreviation expansion, then SapBERT [@sapbert] embedding similarity using a checkpoint fine-tuned on BABEL synonyms (`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`), each From 7563e15352c99566095a330163e0c30e9c39defc Mon Sep 17 00:00:00 2001 From: Skye Lane Goetz Date: Thu, 29 Oct 2026 10:41:47 -0700 Subject: [PATCH 10/25] chore: read first draft of paper and made several small corrections --- paper/paper.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 34dc859f..4e4b38a4 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -29,10 +29,10 @@ bibliography: paper.bib Tablassert turns biomedical spreadsheets (Excel, CSV, TSV) into knowledge graphs ready for NCATS Translator [@translator]. Biomedical research routinely publishes its findings as supplementary tables: association statistics, assay readouts, curated gene lists. A user declares, in YAML, how -each table's columns map to subject-predicate-object statements; Tablassert resolves free text +each table's columns map to qualified subject-predicate-object statements; Tablassert resolves free text ("TP53", "lung cancer") to compact uniform resource identifiers (CURIEs), attaches provenance and -statistical annotations, and emits KGX-compliant [@kgx] node and edge files carrying Biolink Model -categories [@biolink-model]. The same recipe can be inspected, reused, and refined, so a graph +statistical annotations, and emits KGX-compliant [@kgx] node and edge files leveraging the Biolink Model +[@biolink-model]. The same recipe can be inspected, reused, and refined, so a graph starts small instead of being frozen inside a bespoke ingest script. # Statement of need @@ -49,7 +49,7 @@ Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabu sources and need rigorously structured output without maintaining a transform pipeline per source. Beyond journal supplements, the same recipes apply to any relational source -- trial registries, drug approval records, or a researcher's own tables -- and each build can emit the Resource Ingest -Guide metadata that Translator submissions require. Recipes are validated before a build +Guide metadata required for integration with Translator. Recipes are validated before a build starts, so a typo fails immediately with an error naming the field, rather than surfacing as a malformed graph hours later. @@ -93,10 +93,11 @@ shard files. What does not map is reported, not guessed, and metadata arrives with the identifier. Resolution is exact-match over normalized keys rather than fuzzy search -- approximate matches surface only in the audit stage, not in the graph -- because fuzzy resolution returns plausible-but-wrong identifiers often enough -to corrupt a graph silently. The build applies a fixed allowlist of the top 100 experimental taxa +to corrupt a graph silently, which is inexcusable when researchers and medical professionals rely +on Translator for accurate information. The build applies a fixed allowlist of the top 100 experimental taxa derived from Translator knowledge-graph node frequencies, alongside all OrganismTaxon rows and -rows without taxon metadata: a deliberate trade-off that shrinks the database but drops -species-specific identifiers outside the allowlist. +rows without taxon metadata: a deliberate trade-off that shrinks the database and speeds up query times but excludes +some rare species-specific identifiers outside the allowlist. Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species while @@ -107,11 +108,12 @@ level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multip bonus of 1 for prioritized categories and 50 otherwise -- then deduplicated to the best tier, retaining distinct CURIEs that tie. When column context is on, category frequency across the column being resolved breaks remaining ties, so a column of gene symbols resolves ambiguous short -names as genes. These are query shapes the hosted API does not offer: it takes a string and returns +names as genes instead of proteins or a phenotypic feature with the same name. These are query +shapes the hosted API does not offer: it takes a string and returns ranked identifiers, with no taxon constraint, category policy, or per-column context, and the full identifier-record workflow (string to identifier, then canonicalization for preferred name and category) takes two HTTP calls per term where the fullmap returns all of it in one batched -fetch. Benchmarks in `paper/benchmarks/` quantify the gap on 113 real ingest terms: local +fetch. Benchmarks quantify the gap on 113 real ingest terms: local resolution sustained 171 unique terms per second through the build's batch path, versus a median of 0.55 seconds per hosted call (0.58 seconds for the two-call workflow). @@ -173,7 +175,7 @@ Models used included the following recorded identifiers: `laguna-s-2.1`, `qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and `mimo-v2.6-pro`. -A bespoke LLM-as-judge implementation reviewed code before pull requests, and CodeRabbit +A bespoke LLM-as-judge implementation reviewed code before pull requests, and CodeRabbit occasionally reviewed pull requests before Goetz's own review. Validation combined manual review and auditing with automated tests, CI/CD pipelines, custom linting rules written by Goetz, and scripted inclusion checks for required output elements. Git pre-commit hooks provided earlier @@ -183,7 +185,7 @@ optimize prompts for desired outputs, supported output control; LLM-as-judge evaluations and scripted inclusion checks assessed outputs. Prompt optimization and AI-based evaluation were not treated as independent proof of correctness. These checks supplemented, rather than replaced, human review. -Skye Lane Goetz made all software architecture decisions and manually reviewed and audited +The authors made all software architecture decisions and manually reviewed and audited all changes, including AI-assisted outputs. The authors retain full responsibility for the accuracy, originality, licensing, and ethical and legal compliance of the submitted software and manuscript. @@ -196,6 +198,6 @@ Awards OT2TR003443, OT2TR003428, and OT2TR005706). Any opinions expressed in thi 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. -TODO(collaborators): contributor acknowledgements and sponsor-involvement statement go here. +TODO(collaborators): contributor acknowledgments and sponsor-involvement statement go here. # References From c03d45287c97d010c485590332e42f21494a5a3d Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Thu, 29 Oct 2026 11:03:54 -0700 Subject: [PATCH 11/25] docs(paper): document normalization, parallelism, QC example, and graph scale --- paper/benchmarks/evidence.md | 88 ++++++++++++++++++++++++++++++++++++ paper/paper.bib | 8 ++++ paper/paper.md | 68 +++++++++++++--------------- 3 files changed, 128 insertions(+), 36 deletions(-) create mode 100644 paper/benchmarks/evidence.md 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/paper.bib b/paper/paper.bib index adb1539d..36c78342 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -5,6 +5,14 @@ % 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}, diff --git a/paper/paper.md b/paper/paper.md index 4e4b38a4..101c889c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -31,19 +31,16 @@ Translator [@translator]. Biomedical research routinely publishes its findings a tables: association statistics, assay readouts, curated gene lists. A user declares, in YAML, how each table's columns map to qualified subject-predicate-object statements; Tablassert resolves free text ("TP53", "lung cancer") to compact uniform resource identifiers (CURIEs), attaches provenance and -statistical annotations, and emits KGX-compliant [@kgx] node and edge files leveraging the Biolink Model +statistical annotations, and emits KGX-compliant [@kgx] node and edge files leveraging the Biolink Model [@biolink-model]. The same recipe can be inspected, reused, and refined, so a graph starts small instead of being frozen inside a bespoke ingest script. # Statement of need -Tablassert grew out of a recurring research task: converting one paper's supplementary spreadsheet -into a Translator knowledge graph, then another, then another. Each hand-written ingest repeated -the same structure -- read the table, map columns to Biolink statements, resolve names, attach -provenance -- until near-identical codebases outnumbered the tables they served. Tablassert -replaces that per-source code with a declarative recipe: a few lines of YAML per table describe -what each row asserts, and the package handles formatting, resolution, and validation the same way -every time. +Converting supplementary spreadsheets into Translator graphs repeatedly requires reading tables, +mapping columns to Biolink statements, resolving names, and attaching provenance. Tablassert +replaces this per-source code with declarative YAML recipes while handling formatting, resolution, +and validation consistently. Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabular biomedical sources and need rigorously structured output without maintaining a transform pipeline per source. @@ -67,14 +64,12 @@ ontology-aligned model rather than ad hoc research tables. SPIRES [@spires] extr from free text by zero-shot prompting, while Tablassert's sources are already structured tables. The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a network API, which makes bulk build-time lookups slow and irreproducible offline. -None of these removes the cost Tablassert targets: repeated ingest code, the -hosted-resolution dependency, and no integrated mapping-quality checks. Tablassert integrates all -three into one recipe-driven pipeline; contributing resolution or audit stages back to Koza would -still not yield a single declarative mapping. +Tablassert composes declarative mapping, local resolution, and mapping-quality checks into one +recipe-driven pipeline. # Software design -Tablassert's core trade-off is buy-time work for build-time ease: a large entity-resolution +Tablassert's core trade-off is up-front work for build-time ease: a large entity-resolution database is built (or downloaded) once, and every subsequent build resolves millions of terms locally in batched lookups without service calls. Recipes are compiled into TCode, an ordered sequence of (callable, arguments) operations, executed through Polars lazy frames so independent @@ -84,7 +79,10 @@ prefix of instructions reuse its computed snapshot instead of recomputing it. The embedded fullmap database is the design's foundation. Built from BABEL [@babel] exports, it maps normalized synonyms, including the meta-identifiers that unify the same concept across source -vocabularies, onto canonical identifiers with category and taxon metadata. The store itself is a +vocabularies, onto canonical identifiers with category and taxon metadata. Level-one normalization +trims whitespace and quotes, Unicode-lowercases text, Porter2-stems ASCII alphabetic tokens, +then deduplicates, sorts, and joins tokens; level two additionally removes non-word characters. +Identical normalization is applied to synonyms and query text. The store itself is a set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen over analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a parallel, memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 GB compressed; the redb @@ -92,11 +90,9 @@ store used here, built from the 2026-07-22 export, occupies 92.9 GB across one p shard files. What does not map is reported, not guessed, and metadata arrives with the identifier. Resolution is exact-match over normalized keys rather than fuzzy search -- approximate matches surface only in the audit stage, -not in the graph -- because fuzzy resolution returns plausible-but-wrong identifiers often enough -to corrupt a graph silently, which is inexcusable when researchers and medical professionals rely -on Translator for accurate information. The build applies a fixed allowlist of the top 100 experimental taxa +not in the graph -- to avoid silently accepting plausible but incorrect approximate matches. The build applies a fixed allowlist of the top 100 experimental taxa derived from Translator knowledge-graph node frequencies, alongside all OrganismTaxon rows and -rows without taxon metadata: a deliberate trade-off that shrinks the database and speeds up query times but excludes +rows without taxon metadata: a deliberate trade-off that shrinks the database and speeds up query times but excludes some rare species-specific identifiers outside the allowlist. Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own @@ -108,12 +104,13 @@ level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multip bonus of 1 for prioritized categories and 50 otherwise -- then deduplicated to the best tier, retaining distinct CURIEs that tie. When column context is on, category frequency across the column being resolved breaks remaining ties, so a column of gene symbols resolves ambiguous short -names as genes instead of proteins or a phenotypic feature with the same name. These are query +names as genes instead of proteins or a phenotypic feature with the same name. These are query shapes the hosted API does not offer: it takes a string and returns ranked identifiers, with no taxon constraint, category policy, or per-column context, and the full identifier-record workflow (string to identifier, then canonicalization for preferred name and category) takes two HTTP calls per term where the fullmap returns all of it in one batched -fetch. Benchmarks quantify the gap on 113 real ingest terms: local +fetch. Benchmarks on a dual-socket Intel Xeon Gold 6230 host (40 physical cores, 80 hardware +threads, 187 GiB RAM, SSD RAID1, Ubuntu 24.04) used 113 ingest terms: local resolution sustained 171 unique terms per second through the build's batch path, versus a median of 0.55 seconds per hosted call (0.58 seconds for the two-call workflow). @@ -124,17 +121,19 @@ with a four-stage cascade: exact or alias match, fuzzy string similarity, Schwar abbreviation expansion, then SapBERT [@sapbert] embedding similarity using a checkpoint fine-tuned on BABEL synonyms (`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`), each stage handling only what cheaper stages could not clear. Suspect mappings are dropped and logged -with their scores rather than silently retained; in the DAKP production builds this audit covers -the full graph -- 1,661,094 audited rows in the current release -- which in practice catches -resolution errors that upstream synonym sources are slow to fix. Output correctness is checked in +with their scores rather than silently retained. A DAKP FAERS audit (2026-10-22) 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]. Output correctness is checked in two passes: an optional study stage asserts structural invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required fields, no edges demoted to bare Association). The `validate-kgx` command goes beyond KGX's schema-driven `validate` [@kgx]: every node and edge is constructed as the Biolink class its own category names, knowledge-source identity is checked against the Translator infores registry offline, and `--prune` rewrites the edges file in place to its strictly-compliant subset, with the exit code gating the post-prune state. Tablassert -is a Python package with a PyO3 Rust extension; the resolution engine, database build, and NDJSON -writer run in Rust while recipes and validation remain Python. The Koza contrast +uses PyO3 Rust for normalization, resolution, database construction, and NDJSON writing. +GIL-released Rust regions use Rayon workers for CPU-heavy normalization and sharded lookups, +enabling parallel execution even on GIL-enabled Python 3.11, without requiring free-threaded Python. +This removes per-term Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. The Koza contrast in `paper/benchmarks/` maps the tutorial example both ways: 79 lines of YAML plus Python with no entity resolution, against the 32-line Tablassert recipe (10,000 rows: 6.9 seconds including resolution against the fullmap, 5.4 seconds without it). @@ -145,7 +144,8 @@ Tablassert and its declarative precursor underlie several production knowledge g published MicrobiomeKG [@microbiomekg], a Translator-deployed graph built from supplementary tables of 40 publications, was produced with an early unnamed precursor; the deployed Microbiome and Multiomics knowledge graphs [@multiomics-kp] and their maintenance builds use present-day -Tablassert. DAKP [@dakp], the Drug Approvals Knowledge Provider (now developed in the glusman-team +Tablassert. Multiomics 3.1.3 contains 222,994 nodes and 18,206,965 edges from 2,136 PMC publications [@multiomics-kp]; +DAKP 1.26.0 contains 16,257 nodes and 135,263 edges [@drug-approvals]. DAKP [@dakp], the Drug Approvals Knowledge Provider (now developed in the glusman-team graph monorepo [@graph-monorepo]), applies Tablassert to regulatory drug-approval sources and publishes validated, versioned releases to a public dataset archive [@drug-approvals]; a public linkouts interface exposes its per-edge evidence, and integration into the Translator ingest @@ -175,16 +175,12 @@ Models used included the following recorded identifiers: `laguna-s-2.1`, `qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and `mimo-v2.6-pro`. -A bespoke LLM-as-judge implementation reviewed code before pull requests, and CodeRabbit occasionally -reviewed pull requests before Goetz's own review. Validation combined manual review and -auditing with automated tests, CI/CD pipelines, custom linting rules written by Goetz, and -scripted inclusion checks for required output elements. Git pre-commit hooks provided earlier -checks to shorten CI feedback, and language servers provided development-time diagnostics. -Detailed prompt engineering, with promptfoo (version 0.121.17) and GEPA (version 0.1.4) used to -optimize prompts for desired -outputs, supported output control; LLM-as-judge evaluations and scripted inclusion checks -assessed outputs. Prompt optimization and AI-based evaluation were not treated as independent -proof of correctness. These checks supplemented, rather than replaced, human review. +A bespoke LLM-as-judge implementation reviewed code before pull requests; CodeRabbit occasionally +reviewed pull requests before Goetz. Validation combined manual auditing, automated tests, CI/CD, +Goetz's custom linting rules, scripted inclusion checks, Git pre-commit hooks, and language-server +diagnostics. Detailed prompt engineering used promptfoo (0.121.17) and GEPA (0.1.4) to optimize +outputs, evaluated with LLM judges and inclusion checks. Prompt optimization and AI evaluation +supplemented human review without independently proving correctness. The authors made all software architecture decisions and manually reviewed and audited all changes, including AI-assisted outputs. The authors retain full responsibility for the accuracy, originality, licensing, and ethical and legal compliance of the submitted software From f8e4d71496fcec3876d8b11392f3ed7035d166b1 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Thu, 29 Oct 2026 12:11:47 -0700 Subject: [PATCH 12/25] docs(paper): apply revised wording and consistent paragraph wrapping --- paper/paper.md | 232 +++++++++++++++++++++++++------------------------ 1 file changed, 118 insertions(+), 114 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 101c889c..dbcc9e5c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -27,43 +27,45 @@ bibliography: paper.bib # Summary Tablassert turns biomedical spreadsheets (Excel, CSV, TSV) into knowledge graphs ready for NCATS -Translator [@translator]. Biomedical research routinely publishes its findings as supplementary -tables: association statistics, assay readouts, curated gene lists. A user declares, in YAML, how -each table's columns map to qualified subject-predicate-object statements; Tablassert resolves free text -("TP53", "lung cancer") to compact uniform resource identifiers (CURIEs), attaches provenance and -statistical annotations, and emits KGX-compliant [@kgx] node and edge files leveraging the Biolink Model -[@biolink-model]. The same recipe can be inspected, reused, and refined, so a graph -starts small instead of being frozen inside a bespoke ingest script. +Translator [@translator]. Biomedical research routinely publishes findings as supplementary +tables: association statistics, assay readouts, curated gene lists. A user declares, in YAML, +how each table's columns map to qualified subject-predicate-object statements; Tablassert +resolves free text ("TP53", "lung cancer") to compact uniform resource identifiers (CURIEs), +attaches provenance and statistical annotations, and emits KGX-compliant [@kgx] node and edge +files leveraging the Biolink Model [@biolink-model]. You can inspect, reuse, and refine the same +recipe, so a graph starts small instead of being frozen inside a bespoke ingest script. # Statement of need -Converting supplementary spreadsheets into Translator graphs repeatedly requires reading tables, -mapping columns to Biolink statements, resolving names, and attaching provenance. Tablassert -replaces this per-source code with declarative YAML recipes while handling formatting, resolution, +Repeatedly converting supplementary spreadsheets into Translator graphs requires reading tables, +mapping columns to Biolink statements, resolving names, and attaching provenance. By replacing +this per-source code with declarative YAML recipes, Tablassert handles formatting, resolution, and validation consistently. Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabular biomedical -sources and need rigorously structured output without maintaining a transform pipeline per source. -Beyond journal supplements, the same recipes apply to any relational source -- trial registries, -drug approval records, or a researcher's own tables -- and each build can emit the Resource Ingest -Guide metadata required for integration with Translator. Recipes are validated before a build -starts, so a typo fails immediately with an error naming the field, rather than surfacing as a -malformed graph hours later. +sources and need rigorously structured output without maintaining a transform pipeline per +source. Extending beyond journal supplements, the same recipes apply to any relational source -- +trial registries, drug approval records, or a researcher's own tables -- and each build can emit +the Resource Ingest Guide metadata required for integration with Translator. Recipes are +validated before a build starts, so a typo fails immediately with an error naming the field, +rather than surfacing as a malformed graph hours later. # State of the field The prevailing practice for Translator ingests is bespoke Python per data source, with generic tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but -provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] ingest -engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, but -leaves entity resolution to separate services or mappings. LinkML [@linkml] models the schema of a -finished knowledge graph generically, but a schema says nothing about how a spreadsheet column -becomes a node. BioCypher [@biocypher] standardizes biomedical graph construction, and with -OntoWeaver maps tabular data into semantic graphs [@ontoweaver], but both assume a curated -ontology-aligned model rather than ad hoc research tables. SPIRES [@spires] extracts knowledge -from free text by zero-shot prompting, while Tablassert's sources are already structured tables. -The hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier mappings as a -network API, which makes bulk build-time lookups slow and irreproducible offline. +provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] +ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, +but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the +schema of a finished knowledge graph generically, but a schema says nothing about how a +spreadsheet column becomes a node. BioCypher [@biocypher] standardizes biomedical graph +construction, and with OntoWeaver maps tabular data into semantic graphs [@ontoweaver], but both +assume a curated ontology-aligned model rather than ad hoc research tables. SPIRES [@spires] +extracts knowledge from free text by zero-shot prompting, while Tablassert's sources are already +structured tables. Finally, the hosted SRI name resolution service [@nameres] serves BABEL +[@babel] identifier mappings as a network API, which makes bulk build-time lookups slow and +irreproducible offline. + Tablassert composes declarative mapping, local resolution, and mapping-quality checks into one recipe-driven pipeline. @@ -72,69 +74,72 @@ recipe-driven pipeline. Tablassert's core trade-off is up-front work for build-time ease: a large entity-resolution database is built (or downloaded) once, and every subsequent build resolves millions of terms locally in batched lookups without service calls. Recipes are compiled into TCode, an ordered -sequence of (callable, arguments) operations, executed through Polars lazy frames so independent -transformations fuse and materialize only at explicit boundaries: batched resolution, quality -control, and output writing. Because operations are first-class values, sections that share a -prefix of instructions reuse its computed snapshot instead of recomputing it. - -The embedded fullmap database is the design's foundation. Built from BABEL [@babel] exports, it -maps normalized synonyms, including the meta-identifiers that unify the same concept across source -vocabularies, onto canonical identifiers with category and taxon metadata. Level-one normalization -trims whitespace and quotes, Unicode-lowercases text, Porter2-stems ASCII alphabetic tokens, -then deduplicates, sorts, and joins tokens; level two additionally removes non-word characters. -Identical normalization is applied to synonyms and query text. The store itself is a -set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen over -analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a -parallel, memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 GB compressed; the redb -store used here, built from the 2026-07-22 export, occupies 92.9 GB across one primary and 16 -shard files. What does not map is -reported, not guessed, and metadata arrives with the identifier. Resolution is exact-match over -normalized keys rather than fuzzy search -- approximate matches surface only in the audit stage, -not in the graph -- to avoid silently accepting plausible but incorrect approximate matches. The build applies a fixed allowlist of the top 100 experimental taxa -derived from Translator knowledge-graph node frequencies, alongside all OrganismTaxon rows and -rows without taxon metadata: a deliberate trade-off that shrinks the database and speeds up query times but excludes -some rare species-specific identifiers outside the allowlist. +sequence of (callable, arguments) operations, executed through Polars lazy frames. As a result, +independent transformations fuse and materialize only at explicit boundaries: batched +resolution, quality control, and output writing. Because operations are first-class values, +sections that share a prefix of instructions reuse its computed snapshot instead of recomputing +it. + +The embedded fullmap database underpins the design. Built from BABEL [@babel] exports, it maps +normalized synonyms, including the meta-identifiers that unify the same concept across source +vocabularies, onto canonical identifiers with category and taxon metadata. Level-one +normalization trims whitespace and quotes, Unicode-lowercases text, Porter2-stems ASCII +alphabetic tokens, then deduplicates, sorts, and joins tokens; level two additionally removes +non-word characters. Identical normalization is applied to synonyms and query text. The store +itself is a set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen +over analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a +parallel, memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 +export is 44.5 GB compressed; the redb store used here, built from the 2026-07-22 export, +occupies 92.9 GB across one primary and 16 shard files. What does not map is reported, not +guessed, and metadata arrives with the identifier. Resolution is exact-match over normalized +keys rather than fuzzy search -- approximate matches surface only in the audit stage, not in the +graph -- to avoid silently accepting plausible but incorrect approximate matches. To keep the +database smaller and query times faster, the build applies a fixed allowlist of the top 100 +experimental taxa derived from Translator knowledge-graph node frequencies, alongside all +OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some rare +species-specific identifiers outside the allowlist. Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own -constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species while -retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also drops -categories the installed model does not recognize), and CURIE prefix and regex exclusions. -Surviving candidates are ranked by a priority score -- exact preferred-name equality ranks 1, -level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multiplied by a category -bonus of 1 for prioritized categories and 50 otherwise -- then deduplicated to the best tier, -retaining distinct CURIEs that tie. When column context is on, category frequency across the -column being resolved breaks remaining ties, so a column of gene symbols resolves ambiguous short -names as genes instead of proteins or a phenotypic feature with the same name. These are query -shapes the hosted API does not offer: it takes a string and returns -ranked identifiers, with no taxon constraint, category policy, or per-column context, and the full +constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species +while retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also +drops categories the installed model does not recognize), and CURIE prefix and regex exclusions. +A priority score ranks surviving candidates -- exact preferred-name equality ranks 1, +level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multiplied by a +category bonus of 1 for prioritized categories and 50 otherwise -- then deduplicates them to the +best tier, retaining distinct CURIEs that tie. When column context is on, category frequency +across the column being resolved breaks remaining ties, so a column of gene symbols resolves +ambiguous short names as genes instead of proteins or a phenotypic feature with the same name. +These are query shapes the hosted API does not offer: it takes a string and returns ranked +identifiers, with no taxon constraint, category policy, or per-column context, and the full identifier-record workflow (string to identifier, then canonicalization for preferred name and -category) takes two HTTP calls per term where the fullmap returns all of it in one batched -fetch. Benchmarks on a dual-socket Intel Xeon Gold 6230 host (40 physical cores, 80 hardware -threads, 187 GiB RAM, SSD RAID1, Ubuntu 24.04) used 113 ingest terms: local -resolution sustained 171 unique terms per second through the build's batch path, versus a -median of 0.55 seconds per hosted call (0.58 seconds for the two-call workflow). +category) takes two HTTP calls per term, while fullmap returns all of it in one batched fetch. +Benchmarks on a dual-socket Intel Xeon Gold 6230 host (40 physical cores, 80 hardware threads, +187 GiB RAM, SSD RAID1, Ubuntu 24.04) used 113 ingest terms: local resolution sustained 171 +unique terms per second through the build's batch path, versus a median of 0.55 seconds per +hosted call (0.58 seconds for the two-call workflow). Every edge carries recipe-declared structured provenance -- source record URLs, supporting publications, and the processing context that produced it -- so an assertion traces back to its -row and evidence. Optional quality control audits each resolved name -with a four-stage cascade: exact or alias match, fuzzy string similarity, Schwartz-Hearst -abbreviation expansion, then SapBERT [@sapbert] embedding similarity using a checkpoint fine-tuned -on BABEL synonyms (`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`), each -stage handling only what cheaper stages could not clear. Suspect mappings are dropped and logged -with their scores rather than silently retained. A DAKP FAERS audit (2026-10-22) 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]. Output correctness is checked in -two passes: an optional study stage asserts structural invariants over the emitted NDJSON (no -duplicate or isolated nodes, no empty required fields, no edges demoted to bare Association). The -`validate-kgx` command goes beyond KGX's schema-driven `validate` [@kgx]: every node and edge is -constructed as the Biolink class its own category names, knowledge-source identity is checked -against the Translator infores registry offline, and `--prune` rewrites the edges file in place to -its strictly-compliant subset, with the exit code gating the post-prune state. Tablassert -uses PyO3 Rust for normalization, resolution, database construction, and NDJSON writing. -GIL-released Rust regions use Rayon workers for CPU-heavy normalization and sharded lookups, -enabling parallel execution even on GIL-enabled Python 3.11, without requiring free-threaded Python. -This removes per-term Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. The Koza contrast -in `paper/benchmarks/` maps the tutorial example both ways: 79 lines of YAML plus Python with no +row and evidence. Optional quality control audits each resolved name with a four-stage cascade: +exact or alias match, fuzzy string similarity, Schwartz-Hearst abbreviation expansion, then +SapBERT [@sapbert] embedding similarity using a checkpoint fine-tuned on BABEL synonyms +(`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`), each stage handling only +what cheaper stages could not clear. Suspect mappings are dropped and logged with their scores +rather than silently retained. For example, a DAKP FAERS audit (2026-10-22) 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]. Output correctness is checked in two passes: an optional study stage asserts +structural invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required +fields, no edges demoted to bare Association). The `validate-kgx` command goes beyond KGX's +schema-driven `validate` [@kgx]: every node and edge is constructed as the Biolink class with +its own category names, knowledge-source identity is checked against the Translator infores +registry offline, and `--prune` rewrites the edges file in place to its strictly-compliant +subset, with the exit code gating the post-prune state. Tablassert uses PyO3 Rust for +normalization, resolution, database construction, and NDJSON writing. GIL-released Rust regions +use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel execution +even on GIL-enabled Python 3.11, without requiring free-threaded Python. This removes per-term +Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. +Contrasting Koza, the tutorial example maps both ways: 79 lines of YAML plus Python with no entity resolution, against the 32-line Tablassert recipe (10,000 rows: 6.9 seconds including resolution against the fullmap, 5.4 seconds without it). @@ -144,47 +149,46 @@ Tablassert and its declarative precursor underlie several production knowledge g published MicrobiomeKG [@microbiomekg], a Translator-deployed graph built from supplementary tables of 40 publications, was produced with an early unnamed precursor; the deployed Microbiome and Multiomics knowledge graphs [@multiomics-kp] and their maintenance builds use present-day -Tablassert. Multiomics 3.1.3 contains 222,994 nodes and 18,206,965 edges from 2,136 PMC publications [@multiomics-kp]; -DAKP 1.26.0 contains 16,257 nodes and 135,263 edges [@drug-approvals]. DAKP [@dakp], the Drug Approvals Knowledge Provider (now developed in the glusman-team -graph monorepo [@graph-monorepo]), applies Tablassert to regulatory drug-approval sources and -publishes validated, versioned releases to a public dataset archive [@drug-approvals]; a public -linkouts interface exposes its per-edge evidence, and integration into the Translator ingest -infrastructure is under review [@ingests], beginning with a Tablassert passthrough for the -Multiomics graph [@ingests-mokg]. -KRAKEN [@kraken], Phenome Health's unified knowledge graph, integrates the Microbiome, Multiomics, -and Drug Approvals graphs as domain-specific sources. A rare-disease multiomics knowledge graph -built with Tablassert is in preparation. +Tablassert. Multiomics 3.1.3 contains 222,994 nodes and 18,206,965 edges from 2,136 PMC +publications [@multiomics-kp]; DAKP 1.26.0 contains 16,257 nodes and 135,263 edges +[@drug-approvals]. DAKP [@dakp], the Drug Approvals Knowledge Provider (now developed in the +glusman-team graph monorepo [@graph-monorepo]), applies Tablassert to regulatory drug-approval +sources and publishes validated, versioned releases to a public dataset archive +[@drug-approvals]; a public linkouts interface exposes its per-edge evidence, and integration +into the Translator ingest infrastructure is under review [@ingests], beginning with a +Tablassert passthrough for the Multiomics graph [@ingests-mokg]. KRAKEN [@kraken], Phenome +Health's unified knowledge graph, integrates the Microbiome, Multiomics, and Drug Approvals +graphs as domain-specific sources. A rare-disease multiomics knowledge graph built with +Tablassert is in preparation. # AI usage disclosure Generative AI assisted with code generation, test writing, documentation, and drafting and -revising this manuscript, primarily through a customized pi coding agent (version 0.81.1). AI +revising this manuscript, primarily through a customized Pi coding agent (version 0.81.1). AI spelling and grammar checks were used for the documentation and manuscript; Grammarly also assisted with the manuscript. AI coding-agent use began on 27 January 2026, marked by commit `50f29e8`; commit co-author credits record Claude Sonnet 4.5, Claude Opus 4.5, and Claude Opus 4.6 (context-window variants grouped per version). Initial software versions were developed without AI coding agents. -Models used included the following recorded identifiers: -`claude-opus-5`, `claude-opus-5-5`, `claude-sonnet-5`, `claude-sonnet-5-5`, -`deepseek-v4-pro`, `deepseek-v4-flash`, `gemini-3.8-flash`, `glm-5.3`, -`glm-5.3-flash`, `gpt-5.6-luna`, `gpt-5.6-sol-review`, `gpt-5.6-terra`, `gpt-6-astra`, -`gpt-6-luna`, `gpt-6-sol`, `gpt-6.1-sol`, `kimi-k2.5`, `kimi-k2.6`, `kimi-k2.7-pro`, -`kimi-k2.8-preview`, `kimi-k3`, `kimi-k3.1-pro`, -`muse-spark-1.3-contributor-free`, `nemotron-3-ultra-550b-a55b:free`, -`laguna-s-2.1`, `qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and -`mimo-v2.6-pro`. - -A bespoke LLM-as-judge implementation reviewed code before pull requests; CodeRabbit occasionally -reviewed pull requests before Goetz. Validation combined manual auditing, automated tests, CI/CD, -Goetz's custom linting rules, scripted inclusion checks, Git pre-commit hooks, and language-server -diagnostics. Detailed prompt engineering used promptfoo (0.121.17) and GEPA (0.1.4) to optimize -outputs, evaluated with LLM judges and inclusion checks. Prompt optimization and AI evaluation -supplemented human review without independently proving correctness. -The authors made all software architecture decisions and manually reviewed and audited -all changes, including AI-assisted outputs. The authors retain full responsibility for the -accuracy, originality, licensing, and ethical and legal compliance of the submitted software -and manuscript. +Models used included the following recorded identifiers: `claude-opus-5`, `claude-opus-5-5`, +`claude-sonnet-5`, `claude-sonnet-5-5`, `deepseek-v4-pro`, `deepseek-v4-flash`, +`gemini-3.8-flash`, `glm-5.3`, `glm-5.3-flash`, `gpt-5.6-luna`, `gpt-5.6-sol-review`, +`gpt-5.6-terra`, `gpt-6-astra`, `gpt-6-luna`, `gpt-6-sol`, `gpt-6.1-sol`, `kimi-k2.5`, +`kimi-k2.6`, `kimi-k2.7-pro`, `kimi-k2.8-preview`, `kimi-k3`, `kimi-k3.1-pro`, +`muse-spark-1.3-contributor-free`, `nemotron-3-ultra-550b-a55b:free`, `laguna-s-2.1`, +`qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and `mimo-v2.6-pro`. + +A bespoke LLM-as-judge implementation reviewed code before pull requests; CodeRabbit +occasionally reviewed pull requests before Goetz. Validation combined manual auditing, automated +tests, CI/CD, Goetz's custom linting rules, scripted inclusion checks, Git pre-commit hooks, and +language-server diagnostics. Detailed prompt engineering used promptfoo (0.121.17) and GEPA +(0.1.4) to optimize outputs, evaluated with LLM judges and inclusion checks. Prompt optimization +and AI evaluation supplemented human review without independently proving correctness. The +authors made all software architecture decisions and manually reviewed and audited all changes, +including AI-assisted outputs. The authors retain full responsibility for the accuracy, +originality, licensing, and ethical and legal compliance of the submitted software and +manuscript. # Acknowledgements From 2f4574078d1557d1ba06a5a3b4649242e481cf02 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Thu, 29 Oct 2026 12:18:07 -0700 Subject: [PATCH 13/25] docs(paper): draft author contributions and simplify AI disclosure --- paper/paper.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index dbcc9e5c..de0db3c9 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -165,13 +165,11 @@ Tablassert is in preparation. Generative AI assisted with code generation, test writing, documentation, and drafting and revising this manuscript, primarily through a customized Pi coding agent (version 0.81.1). AI -spelling and grammar checks were used for the documentation and manuscript; Grammarly also -assisted with the manuscript. AI coding-agent use began on 27 January 2026, marked by commit -`50f29e8`; commit co-author credits record Claude Sonnet 4.5, Claude Opus 4.5, and Claude Opus -4.6 (context-window variants grouped per version). Initial software versions were developed -without AI coding agents. +spelling and grammar checks covered documentation and manuscript, including Grammarly for the +manuscript. Coding-agent use began on 27 January 2026 (`50f29e8`); co-author credits record +Claude Sonnet 4.5 and Opus 4.5/4.6 (context variants grouped). Initial versions used no coding agents. -Models used included the following recorded identifiers: `claude-opus-5`, `claude-opus-5-5`, +Models used included `claude-opus-5`, `claude-opus-5-5`, `claude-sonnet-5`, `claude-sonnet-5-5`, `deepseek-v4-pro`, `deepseek-v4-flash`, `gemini-3.8-flash`, `glm-5.3`, `glm-5.3-flash`, `gpt-5.6-luna`, `gpt-5.6-sol-review`, `gpt-5.6-terra`, `gpt-6-astra`, `gpt-6-luna`, `gpt-6-sol`, `gpt-6.1-sol`, `kimi-k2.5`, @@ -190,6 +188,11 @@ including AI-assisted outputs. The authors retain full responsibility for the ac originality, licensing, and ethical and legal compliance of the submitted software and manuscript. +# Author contributions + +Skye Lane Goetz developed the software, led design, and performed analyses. Jared C. Roach and +Gwênlyn Glusman helped design the software and analyses and provided detailed project feedback. All authors will write, revise, and proofread. + # Acknowledgements This work was supported by the National Science Foundation (award 2150265) and by the National @@ -198,6 +201,6 @@ Awards OT2TR003443, OT2TR003428, and OT2TR005706). Any opinions expressed in thi 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. -TODO(collaborators): contributor acknowledgments and sponsor-involvement statement go here. +TODO: confirm contributions; disclose sponsor involvement. # References From e3eb9bbb4de7c0ee68cc5f2c182c117d12156f46 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Thu, 29 Oct 2026 12:18:43 -0700 Subject: [PATCH 14/25] docs(paper): remove confirmed-contributions TODO --- paper/paper.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index de0db3c9..ebfa425b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -201,6 +201,4 @@ Awards OT2TR003443, OT2TR003428, and OT2TR005706). Any opinions expressed in thi 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. -TODO: confirm contributions; disclose sponsor involvement. - # References From 7ae23e9cb886a20dc8ac9d1df2e24441b43f3291 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Thu, 29 Oct 2026 12:22:06 -0700 Subject: [PATCH 15/25] docs(paper): clarify funder role and consortium authorship consultation --- paper/paper.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index ebfa425b..81729cfb 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,18 +37,15 @@ recipe, so a graph starts small instead of being frozen inside a bespoke ingest # Statement of need -Repeatedly converting supplementary spreadsheets into Translator graphs requires reading tables, -mapping columns to Biolink statements, resolving names, and attaching provenance. By replacing -this per-source code with declarative YAML recipes, Tablassert handles formatting, resolution, -and validation consistently. +Tablassert replaces repetitive spreadsheet-ingest code with declarative YAML recipes for +consistent formatting, resolution, and validation. Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabular biomedical sources and need rigorously structured output without maintaining a transform pipeline per source. Extending beyond journal supplements, the same recipes apply to any relational source -- trial registries, drug approval records, or a researcher's own tables -- and each build can emit the Resource Ingest Guide metadata required for integration with Translator. Recipes are -validated before a build starts, so a typo fails immediately with an error naming the field, -rather than surfacing as a malformed graph hours later. +validated before a build starts, so configuration errors fail early with field-specific messages. # State of the field @@ -201,4 +198,8 @@ Awards OT2TR003443, OT2TR003428, and OT2TR005706). Any opinions expressed in thi 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. +The funders provided financial support but had no role in software design, analysis, or +manuscript preparation. Consortium authorship requirements were clarified through committee +consultation. + # References From 8734dbb2597d4248e05ae48a4502125688ea3a98 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Thu, 29 Oct 2026 14:33:06 -0700 Subject: [PATCH 16/25] docs(paper): verify benchmark coverage and scope comparative claims --- paper/benchmarks/README.md | 13 +- paper/benchmarks/koza_comparison/README.md | 130 +- paper/benchmarks/koza_comparison/count_loc.py | 70 + paper/benchmarks/koza_comparison/loc.json | 11 + paper/benchmarks/koza_comparison/make_10k.py | 40 + .../koza_comparison/measure_koza.py | 167 + .../koza_comparison/test_bench_koza.py | 99 + paper/benchmarks/koza_comparison/timing.json | 128 +- .../koza_comparison/tutorial-data-10k.csv | 20002 ++++++++-------- .../tutorial-koza-resolved.yaml | 29 + .../koza_comparison/tutorial-koza.yaml | 2 +- .../koza_comparison/tutorial-table.yaml | 5 +- .../tutorial-transform-resolved.py | 107 + .../koza_comparison/tutorial-transform.py | 4 +- paper/benchmarks/name_resolution/README.md | 110 +- .../name_resolution/bench_name_res.py | 531 +- .../name_resolution/local_cold_run0.json | 1879 ++ .../name_resolution/local_cold_run1.json | 1879 ++ .../name_resolution/local_cold_run2.json | 1879 ++ .../name_resolution/local_runner.py | 158 + .../name_resolution/local_warm.json | 1883 ++ .../results-2026-10-28-v1.json | 3744 +++ paper/benchmarks/name_resolution/results.json | 19703 ++++++++++++--- .../name_resolution/test_bench_name_res.py | 296 + paper/paper.md | 124 +- 25 files changed, 39473 insertions(+), 13520 deletions(-) create mode 100644 paper/benchmarks/koza_comparison/count_loc.py create mode 100644 paper/benchmarks/koza_comparison/loc.json create mode 100644 paper/benchmarks/koza_comparison/make_10k.py create mode 100644 paper/benchmarks/koza_comparison/measure_koza.py create mode 100644 paper/benchmarks/koza_comparison/test_bench_koza.py create mode 100644 paper/benchmarks/koza_comparison/tutorial-koza-resolved.yaml create mode 100644 paper/benchmarks/koza_comparison/tutorial-transform-resolved.py create mode 100644 paper/benchmarks/name_resolution/local_cold_run0.json create mode 100644 paper/benchmarks/name_resolution/local_cold_run1.json create mode 100644 paper/benchmarks/name_resolution/local_cold_run2.json create mode 100644 paper/benchmarks/name_resolution/local_runner.py create mode 100644 paper/benchmarks/name_resolution/local_warm.json create mode 100644 paper/benchmarks/name_resolution/results-2026-10-28-v1.json create mode 100644 paper/benchmarks/name_resolution/test_bench_name_res.py diff --git a/paper/benchmarks/README.md b/paper/benchmarks/README.md index ddae33a3..5fe21c89 100644 --- a/paper/benchmarks/README.md +++ b/paper/benchmarks/README.md @@ -3,11 +3,14 @@ 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 SRI - name resolution service, on real ingest vocabulary. -- `koza_comparison/`: the Tablassert tutorial recipe versus an equivalent Koza - transform of the same table -- lines of code, runtime, and what each tool does and - does not do with the data. +- `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/`: Tablassert local resolution, Koza mapping only, and Koza + with hosted NameResolution plus NodeNormalization over the same table. The + executable harness records cold and warm runtime, output counts, resolution + coverage, and the policies that make the resulting graphs non-equivalent. All runs were executed on the `wenceslaus` build host (80 cores, 187 GB RAM, Ubuntu Linux); per-run environment details (versions, dates, repeat counts) are diff --git a/paper/benchmarks/koza_comparison/README.md b/paper/benchmarks/koza_comparison/README.md index 1739abe9..187db3c4 100644 --- a/paper/benchmarks/koza_comparison/README.md +++ b/paper/benchmarks/koza_comparison/README.md @@ -1,37 +1,93 @@ -# Koza comparison: tutorial example as Tablassert recipe vs Koza transform - -Same input table (`tutorial-data.csv`, 4 rows; `tutorial-data-10k.csv` is the same -rows replicated to 10,000), same mapping task: each row becomes one -`associated_with` edge plus its two nodes, with p-value and sample-size -annotations and citation provenance. - -- Tablassert side: `tutorial-table.yaml` (the recipe) + `tutorial-graph.yaml` - (graph wrapper pointing at the fullmap). Recipe lines: 32. -- Koza side: `tutorial-koza.yaml` (reader/writer config) + `tutorial-transform.py` - (the transform). Combined lines: 29 + 50 = 79. - -## Results (wenceslaus, 2026-10-28; tablassert 19.7.0, koza 2.7.0, Python 3.12) - -10,000-row input, 3 runs each, wall clock in seconds (medians in bold): - -| Tool | Run 1 | Run 2 | Run 3 | Median | Output | -| ----------- | ------- | ------- | ------- | -------- | ---------------------------------------------- | -| Tablassert | 7.21 | 6.85 | 6.94 | 6.94 | KGX NDJSON, resolved CURIEs, categories, taxon-filtered, provenance, RIG | -| Koza | 5.47 | 5.41 | 5.48 | 5.47 | KGX TSV, raw cell text as node ids, no resolution | - -`timing.json` holds the raw measurements. For the 10k timing runs both configs -were pointed at `tutorial-data-10k.csv` (the `local:`/`files:` entries), otherwise -unmodified. - -## What the comparison shows - -- Comparable wall time at this scale, but Tablassert is doing strictly more work: - every node is resolved against the 24 GB fullmap (taxon-filtered, category-ranked) - while Koza emits the raw strings as identifiers. -- The Koza equivalent costs about 2.5x the lines because the mapping logic lives in - Python code, not configuration. -- Koza has no entity resolution stage: getting from "TP53" and "lung cancer" to - CURIEs requires either pre-mapped inputs, hand-built maps, or calling an external - resolver per row -- none of which Koza provides. -- Edge properties (knowledge source, publications) are hardcoded in the transform - Python; in Tablassert they are declared provenance fields validated with the recipe. +# Koza comparison: mapping and resolution workloads + +The same deterministic 10,000-row CSV (`tutorial-data-10k.csv`, generated by +`make_10k.py`) supports three explicitly separate workloads: + +1. `tablassert_*`: `tutorial-graph.yaml` and `tutorial-table.yaml` build the graph + with local fullmap resolution, category prioritization, deduplicated KGX NDJSON + nodes, provenance, and a RIG. +2. `koza_mapping_only_*`: `tutorial-koza.yaml` and `tutorial-transform.py` map each + row to KGX TSV but emit raw labels as node IDs. This isolates mapping cost; it is + not a resolved-graph comparison. +3. `koza_hosted_resolved_*`: `tutorial-koza-resolved.yaml` and + `tutorial-transform-resolved.py` deduplicate labels, call hosted NameResolution + (four concurrent lookups, at most five candidates, `only_taxa=NCBITaxon:9606`), + then call NodeNormalization in batches of 100 CURIEs. The top-ranked candidate + supplies the canonical ID, preferred name, and categories. Empty or incomplete + results fail the run. The HTTP implementation is imported from the sibling + `name_resolution/bench_name_res.py` harness so both benchmarks use the same API + workflow. + +Koza 2.7.0 provides mapping tables but no built-in label resolver. Its +`@koza.transform()` whole-dataset hook supports this custom integration. Hosted +resolution therefore demonstrates additional code and network cost, not a missing +capability that cannot be implemented. + +## Measuring + +Run on the build host with both installed CLIs: + +```bash +python make_10k.py +python measure_koza.py --repeats 3 \ + --tablassert /users/sgoetz/Code/dakp/.venv/bin/tablassert \ + --koza ./koza-venv/bin/koza --out timing.json +``` + +Every measurement includes a fresh CLI process. The first repeat of each workload +is labeled cold; immediate repeats are labeled warm. Output directories are removed +before every run. OS, database, and remote-service caches are never cleared, so +"cold" does not mean cold OS or service caches. Cold is one first-pass measurement; warm is three subsequent passes. They are +reported separately and are not pooled. + +Each run verifies edge and node counts. Hosted runs also record unique terms, +complete canonical records, request counts, and resolution time in +`koza-hosted-resolution.json`, copied into the corresponding `timing.json` run. +Versions, platform, CPU count, and RAM are recorded. + +## Output and policy differences + +The workloads all perform tabular mapping, but their graphs are not equivalent: + +- Tablassert emits 12,500 edges from 10,000 input rows because `pancreatic cancer` + retains two equally ranked canonical CURIEs in the installed fullmap. It emits + nine deduplicated nodes. +- Resolved Koza emits only the top hosted candidate, hence 10,000 edges. Its TSV + writer emits two node records per input row (20,000 node rows), including repeats. +- Mapping-only Koza also emits 10,000 edges and 20,000 repeated node rows, but IDs + are unresolved input labels. + +Consequently the timings compare implementation and infrastructure costs for these +workloads, not throughput of identical graphs. Hosted candidate ranking, taxon +handling, and tie behavior differ from local fullmap ranking. A small synthetic +input with eight unique labels measures per-workflow overhead; it does not +represent resolution throughput on a large vocabulary. + +## Configuration trade-offs + +Tablassert's basic table recipe has 29 counted lines and its separate graph wrapper +24; Koza's mapping-only YAML and Python transform have 25 and 31. Blank lines, +comments, and Python docstrings are excluded by `count_loc.py`. The resolved Koza +configuration and integration module have 25 and 86 counted lines, excluding the +shared HTTP client imported from the name-resolution benchmark. These counts +illustrate where logic lives; they are not a general complexity metric. + +Tablassert's advantage here is integrated, offline, batched resolution with +column-aware policy and graph-level outputs. Its costs are the 92.9 GB fullmap and +building or obtaining that versioned resource. The hosted Koza arm avoids that +local store and can use current service content, but needs imperative integration +code and inherits network latency, availability, rate limits, and remote-version +drift. Mapping-only Koza is cheaper because it does less work, not because it is a +faster equivalent resolver. + +## Current hosted execution status + +The hosted arm is blocked as of 2026-10-29: NameResolution DNS lookups timed out, +and an independent Cloudflare DNS query returned `SERVFAIL` with +`No Reachable Authority` at `renci.org`. Live Koza probes failed before emitting +records. `timing.json` records this blocker explicitly; no hosted Koza timing is +claimed. To regenerate the available arms while recording the failure, use +`--skip-hosted-reason 'description of verified network failure'`. Omit that flag +when the service recovers to run all three workloads. + +Offline tests: `pytest test_bench_koza.py`. diff --git a/paper/benchmarks/koza_comparison/count_loc.py b/paper/benchmarks/koza_comparison/count_loc.py new file mode 100644 index 00000000..b533946a --- /dev/null +++ b/paper/benchmarks/koza_comparison/count_loc.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +"""Line-of-code counter for the koza comparison: no blanks, no docstrings. + +Python files: docstrings are excluded via the AST (module, class, and function +docstrings), blank lines are never counted, and comment-only lines are not code. +YAML files have no docstrings; blank lines are excluded. + +The Tablassert graph wrapper (tutorial-graph.yaml) is always reported as its own +line item, separate from the recipe, so the two configuration layers are visible +instead of silently summed. +""" + +from __future__ import annotations + +import argparse +import ast +import json +from pathlib import Path + + +def count_python(path: Path) -> int: + """Count non-blank, non-docstring, non-comment lines of 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)) + count = 0 + for lineno, line in enumerate(source.splitlines(), start=1): + stripped = line.strip() + if not stripped or stripped.startswith("#"): + continue + if any(start <= lineno <= end for start, end in docstring_spans): + continue + count += 1 + return count + + +def count_yaml(path: Path) -> int: + """Count non-blank lines of a YAML file (comments excluded).""" + return sum(1 for line in path.read_text(encoding="utf-8").splitlines() if line.strip() and not line.strip().startswith("#")) + + +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() + + counts = { + "tablassert_table_yaml": count_yaml(args.dir / "tutorial-table.yaml"), + "tablassert_graph_wrapper_yaml": count_yaml(args.dir / "tutorial-graph.yaml"), + "koza_yaml": count_yaml(args.dir / "tutorial-koza.yaml"), + "koza_transform_py": count_python(args.dir / "tutorial-transform.py"), + "koza_resolved_yaml": count_yaml(args.dir / "tutorial-koza-resolved.yaml"), + "koza_resolved_transform_py": count_python(args.dir / "tutorial-transform-resolved.py"), + } + counts["tablassert_recipe_total"] = counts["tablassert_table_yaml"] + counts["tablassert_graph_wrapper_yaml"] + counts["koza_total"] = counts["koza_yaml"] + counts["koza_transform_py"] + counts["koza_resolved_total"] = counts["koza_resolved_yaml"] + counts["koza_resolved_transform_py"] + print(json.dumps(counts, indent=2)) + if args.out: + args.out.write_text(json.dumps(counts, indent=2)) + + +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..8e8dbd0f --- /dev/null +++ b/paper/benchmarks/koza_comparison/loc.json @@ -0,0 +1,11 @@ +{ + "tablassert_table_yaml": 29, + "tablassert_graph_wrapper_yaml": 24, + "koza_yaml": 25, + "koza_transform_py": 31, + "koza_resolved_yaml": 25, + "koza_resolved_transform_py": 86, + "tablassert_recipe_total": 53, + "koza_total": 56, + "koza_resolved_total": 111 +} \ No newline at end of file diff --git a/paper/benchmarks/koza_comparison/make_10k.py b/paper/benchmarks/koza_comparison/make_10k.py new file mode 100644 index 00000000..8da78be4 --- /dev/null +++ b/paper/benchmarks/koza_comparison/make_10k.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Deterministic 10,000-row generator shared by both koza-comparison runners. + +Replicates tutorial-data.csv to exactly 10,000 data rows (plus header), preserving +row order, so Tablassert and Koza measure the identical input. Same output bytes +on every run (no RNG, no timestamps). +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +TARGET_ROWS = 10_000 + + +def generate(source: Path, out: Path) -> int: + lines = source.read_text(encoding="utf-8").splitlines() + header, rows = lines[0], [line for line in lines[1:] if line.strip()] + if not rows: + msg = f"{source} has no data rows" + raise ValueError(msg) + data = [header] + while len(data) - 1 < TARGET_ROWS: + data.extend(rows[: min(TARGET_ROWS - (len(data) - 1), len(rows))]) + out.write_text("\n".join(data) + "\n", encoding="utf-8") + return len(data) - 1 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--source", type=Path, default=Path(__file__).with_name("tutorial-data.csv")) + parser.add_argument("--out", type=Path, default=Path(__file__).with_name("tutorial-data-10k.csv")) + args = parser.parse_args() + rows = generate(args.source, args.out) + print(f"wrote {args.out} with {rows} data rows") + + +if __name__ == "__main__": + main() diff --git a/paper/benchmarks/koza_comparison/measure_koza.py b/paper/benchmarks/koza_comparison/measure_koza.py new file mode 100644 index 00000000..42957bf6 --- /dev/null +++ b/paper/benchmarks/koza_comparison/measure_koza.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +"""Measure Tablassert and Koza tutorial workloads with verified outputs. + +Three workloads are reported separately: Tablassert with local fullmap resolution, +Koza mapping without resolution, and Koza with hosted NameResolution followed by +NodeNormalization. Every timing includes a fresh CLI process. Cold runs have no +preceding repeat of that workload; warm runs immediately follow a prior repeat. +Outputs are removed before every run. OS, database, and remote-service caches are +never cleared, so cold does not mean a cold OS or service cache. + +Usage on the build host: + + python make_10k.py + python measure_koza.py --repeats 3 --koza ./koza-venv/bin/koza --out timing.json +""" + +from __future__ import annotations + +import argparse +import csv +import json +import os +import platform +import shutil +import statistics +import subprocess +import time +from collections.abc import Callable +from pathlib import Path +from typing import cast + +HERE = Path(__file__).parent + + +def hardware() -> dict[str, object]: + info: dict[str, object] = {"platform": platform.platform(), "python": platform.python_version(), "cpu_count": os.cpu_count()} + meminfo = Path("/proc/meminfo") + if meminfo.exists(): + for line in meminfo.read_text().splitlines(): + if line.startswith("MemTotal:"): + info["mem_total_gib"] = round(int(line.split()[1]) / 1024 / 1024, 1) + break + return info + + +def clean_outputs(paths: list[Path]) -> None: + for path in paths: + if path.is_dir(): + shutil.rmtree(path) + else: + path.unlink(missing_ok=True) + + +def output_rows(out_dir: Path, pattern: str) -> tuple[int, int]: + files = list(out_dir.glob(pattern)) + return len(files), sum(sum(1 for _ in path.open()) for path in files) + + +def run_tablassert(graph_yaml: Path, out_dir: Path, tablassert_bin: str) -> tuple[float, dict[str, object]]: + start = time.perf_counter() + proc = subprocess.run([tablassert_bin, "build-kg", str(graph_yaml)], capture_output=True, text=True, check=False) + elapsed = time.perf_counter() - start + if proc.returncode != 0: + raise RuntimeError(f"tablassert build-kg exited {proc.returncode}: {proc.stderr[-800:]}") + edge_files, edge_rows = output_rows(out_dir, "*.edges.ndjson") + node_files, node_rows = output_rows(out_dir, "*.nodes.ndjson") + if (edge_files, node_files, edge_rows, node_rows) != (1, 1, 12_500, 9): + raise RuntimeError(f"unexpected Tablassert output: edges={edge_rows}, nodes={node_rows}") + return elapsed, {"edge_rows": edge_rows, "node_rows": node_rows} + + +def run_koza(koza_yaml: Path, out_dir: Path, koza_bin: str, resolved: bool = False) -> tuple[float, dict[str, object]]: + start = time.perf_counter() + proc = subprocess.run([koza_bin, "transform", str(koza_yaml), "-o", str(out_dir)], capture_output=True, text=True, check=False) + elapsed = time.perf_counter() - start + if proc.returncode != 0: + raise RuntimeError(f"koza exited {proc.returncode}: {proc.stderr[-1200:]}") + edge_files, edge_lines = output_rows(out_dir, "*_edges.tsv") + node_files, node_lines = output_rows(out_dir, "*_nodes.tsv") + edge_rows, node_rows = edge_lines - 1, node_lines - 1 + if (edge_files, node_files, edge_rows, node_rows) != (1, 1, 10_000, 20_000): + raise RuntimeError(f"unexpected Koza output: edges={edge_rows}, nodes={node_rows}") + check: dict[str, object] = {"edge_rows": edge_rows, "node_rows": node_rows} + if resolved: + report_path = HERE / "koza-hosted-resolution.json" + report = json.loads(report_path.read_text()) + if report["complete_records"] != report["unique_terms"]: + raise RuntimeError(f"incomplete hosted resolution: {report}") + check["hosted_resolution"] = report + for path in out_dir.glob("*_nodes.tsv"): + with path.open() as stream: + for node in csv.DictReader(stream, delimiter="\t"): + if not all(node.get(field) for field in ("id", "name", "category")) or ":" not in node["id"]: + raise RuntimeError(f"incomplete canonical Koza node: {node}") + return elapsed, check + + +def measure(mode: str, runner: Callable[[], tuple[float, dict[str, object]]], repeats: int, outputs: list[Path]) -> dict[str, object]: + walls: list[float] = [] + checks: list[dict[str, object]] = [] + for index in range(repeats): + clean_outputs(outputs) + elapsed, check = runner() + walls.append(elapsed) + checks.append({"run": index + 1, "wall_s": elapsed, **check}) + return {"mode": mode, "repeats": repeats, "runs": checks, "wall_s": walls, "median_s": statistics.median(walls)} + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repeats", type=int, default=3) + parser.add_argument("--out", type=Path, default=HERE / "timing.json") + parser.add_argument("--koza-yaml", type=Path, default=HERE / "tutorial-koza.yaml") + parser.add_argument("--koza-resolved-yaml", type=Path, default=HERE / "tutorial-koza-resolved.yaml") + parser.add_argument("--graph-yaml", type=Path, default=HERE / "tutorial-graph.yaml") + parser.add_argument("--workdir", type=Path, default=HERE / "_runs") + parser.add_argument("--tablassert", default="/users/sgoetz/Code/dakp/.venv/bin/tablassert") + parser.add_argument("--koza", default="koza") + parser.add_argument("--skip-hosted-reason", help="record an explicit blocked hosted arm without running it") + args = parser.parse_args() + + os.chdir(HERE) + tab_out = HERE / "tutorial-output" + koza_out = args.workdir / "koza-out" + resolved_out = args.workdir / "koza-resolved-out" + report = HERE / "koza-hosted-resolution.json" + args.workdir.mkdir(parents=True, exist_ok=True) + + result: dict[str, object] = { + "date_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "versions": { + "tablassert": subprocess.check_output([args.tablassert, "--version"], text=True).strip(), + "koza": subprocess.check_output([args.koza, "--version"], text=True).strip(), + "python": platform.python_version(), + }, + "hardware": hardware(), + "input_rows": sum(1 for _ in (HERE / "tutorial-data-10k.csv").open()) - 1, + "cold_definition": "fresh CLI process, first repeat of the workload, outputs removed; OS/database/service caches not cleared", + "warm_definition": "fresh CLI process immediately following a repeat, outputs removed; OS/database/service caches not cleared", + "output_equivalence": ( + "Tablassert uses local fullmap ranking and emits 12,500 edges because one input label has a two-CURIE tie. " + "Resolved Koza emits the top hosted candidate and 10,000 edges. Mapping-only Koza emits raw labels. " + "The workloads all perform tabular mapping, but the graphs and matching policies are not equivalent." + ), + } + workloads = { + "tablassert": (lambda: run_tablassert(args.graph_yaml, tab_out, args.tablassert), [tab_out]), + "koza_mapping_only": (lambda: run_koza(args.koza_yaml, koza_out, args.koza), [koza_out]), + "koza_hosted_resolved": (lambda: run_koza(args.koza_resolved_yaml, resolved_out, args.koza, resolved=True), [resolved_out, report]), + } + for label, (runner, outputs) in workloads.items(): + if label == "koza_hosted_resolved" and args.skip_hosted_reason: + result[label] = {"status": "blocked", "reason": args.skip_hosted_reason} + continue + result[f"{label}_cold"] = measure("cold", runner, 1, outputs) + result[f"{label}_warm"] = measure("warm", runner, args.repeats, outputs) + + args.out.write_text(json.dumps(result, indent=2)) + for section in result: + if section.endswith(("_cold", "_warm")): + data = cast("dict[str, object]", result[section]) + print(f"{section}: median {cast('float', data['median_s']):.3f}s") + print(f"wrote {args.out}") + + +if __name__ == "__main__": + main() 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..952e73d3 --- /dev/null +++ b/paper/benchmarks/koza_comparison/test_bench_koza.py @@ -0,0 +1,99 @@ +"""Offline tests for the koza comparison harness (LOC counting + 10k generator).""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +sys.path.insert(0, str(Path(__file__).parent)) + +from count_loc import count_python, count_yaml +from make_10k import generate + + +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", + ) + # code lines: import os, def f():, return 1, x = 2 -> 4 + 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_generate_deterministic_10k(tmp_path: Path) -> None: + source = tmp_path / "base.csv" + source.write_text("a,b\n1,2\n3,4\n", encoding="utf-8") + out1, out2 = tmp_path / "o1.csv", tmp_path / "o2.csv" + assert generate(source, out1) == 10_000 + assert generate(source, out2) == 10_000 + assert out1.read_bytes() == out2.read_bytes() + lines = out1.read_text().splitlines() + assert lines[0] == "a,b" + assert len(lines) == 10_001 + # order preserved: row 1 of output is row 1 of source + assert lines[1] == "1,2" + assert lines[-1] == "3,4" + + +def test_generate_rejects_empty_source(tmp_path: Path) -> None: + source = tmp_path / "empty.csv" + source.write_text("a,b\n", encoding="utf-8") + try: + generate(source, tmp_path / "o.csv") + raised = False + except ValueError: + raised = True + assert raised + + +def load_resolved_transform(monkeypatch: pytest.MonkeyPatch): + decorators = SimpleNamespace(transform=lambda: lambda function: function) + monkeypatch.setitem(sys.modules, "koza", decorators) + path = Path(__file__).with_name("tutorial-transform-resolved.py") + spec = importlib.util.spec_from_file_location("tutorial_transform_resolved", path) + assert spec is not None + assert spec.loader is not None + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return module + + +def test_koza_hosted_resolution_uses_top_canonical_hit(monkeypatch: pytest.MonkeyPatch) -> None: + module = load_resolved_transform(monkeypatch) + + def fake_lookup(term: str, limit: int, budget: object, only_taxa: str) -> dict[str, object]: + return {"term": term, "http_status": 200, "error": None, "curies": [f"X:{term}"], "latency_s": 0.1} + + def fake_normalize(curies: list[str], budget: object) -> dict[str, object]: + normalized = {curie: {"id": {"identifier": curie, "label": curie}, "type": ["biolink:NamedThing"]} for curie in curies} + return {"normalized": normalized, "http_calls": 1, "error": None} + + monkeypatch.setattr(module, "lookup_one", fake_lookup) + monkeypatch.setattr(module, "normalize_batch", fake_normalize) + records = [{"gene_symbol": "TP53", "disease_name": "lung cancer"}] + resolved, report = module.resolve_labels(records) + assert resolved["TP53"]["id"] == "X:TP53" + assert report["complete_records"] == 2 + assert report["http_calls"] == {"lookup": 2, "get_normalized_nodes": 1} + + +def test_koza_hosted_resolution_rejects_empty_lookup(monkeypatch: pytest.MonkeyPatch) -> None: + module = load_resolved_transform(monkeypatch) + monkeypatch.setattr( + module, "lookup_one", lambda term, limit, budget, only_taxa: {"term": term, "http_status": 200, "error": None, "curies": [], "latency_s": 0.1} + ) + monkeypatch.setattr(module, "normalize_batch", lambda curies, budget: {"normalized": {}, "http_calls": 0, "error": None}) + with pytest.raises(RuntimeError, match="no candidates"): + module.resolve_labels([{"gene_symbol": "unknown", "disease_name": "unknown"}]) diff --git a/paper/benchmarks/koza_comparison/timing.json b/paper/benchmarks/koza_comparison/timing.json index 7cfa29f6..87cfab3f 100644 --- a/paper/benchmarks/koza_comparison/timing.json +++ b/paper/benchmarks/koza_comparison/timing.json @@ -1,18 +1,114 @@ { - "tablassert_10k": { - "wall_s": [ - 7.210189267992973, - 6.851314269006252, - 6.9370478158816695 - ], - "median": 6.9370478158816695 - }, - "koza_10k": { - "wall_s": [ - 5.4703435860574245, - 5.4094292228110135, - 5.477345589082688 - ], - "median": 5.4703435860574245 - } + "date_utc": "2026-10-29T21:28:55Z", + "versions": { + "tablassert": "tablassert 19.7.0", + "koza": "Koza version: 2.7.0", + "python": "3.12.3" + }, + "hardware": { + "platform": "Linux-6.8.0-142-generic-x86_64-with-glibc2.39", + "python": "3.12.3", + "cpu_count": 80, + "mem_total_gib": 187.5 + }, + "input_rows": 10000, + "cold_definition": "fresh CLI process, first repeat of the workload, outputs removed; OS/database/service caches not cleared", + "warm_definition": "fresh CLI process immediately following a repeat, outputs removed; OS/database/service caches not cleared", + "output_equivalence": "Tablassert uses local fullmap ranking and emits 12,500 edges because one input label has a two-CURIE tie. Resolved Koza emits the top hosted candidate and 10,000 edges. Mapping-only Koza emits raw labels. The workloads all perform tabular mapping, but the graphs and matching policies are not equivalent.", + "tablassert_cold": { + "mode": "cold", + "repeats": 1, + "runs": [ + { + "run": 1, + "wall_s": 6.719237510114908, + "edge_rows": 12500, + "node_rows": 9 + } + ], + "wall_s": [ + 6.719237510114908 + ], + "median_s": 6.719237510114908 + }, + "tablassert_warm": { + "mode": "warm", + "repeats": 3, + "runs": [ + { + "run": 1, + "wall_s": 6.726083653979003, + "edge_rows": 12500, + "node_rows": 9 + }, + { + "run": 2, + "wall_s": 6.727087490726262, + "edge_rows": 12500, + "node_rows": 9 + }, + { + "run": 3, + "wall_s": 6.749565453734249, + "edge_rows": 12500, + "node_rows": 9 + } + ], + "wall_s": [ + 6.726083653979003, + 6.727087490726262, + 6.749565453734249 + ], + "median_s": 6.727087490726262 + }, + "koza_mapping_only_cold": { + "mode": "cold", + "repeats": 1, + "runs": [ + { + "run": 1, + "wall_s": 5.381368623115122, + "edge_rows": 10000, + "node_rows": 20000 + } + ], + "wall_s": [ + 5.381368623115122 + ], + "median_s": 5.381368623115122 + }, + "koza_mapping_only_warm": { + "mode": "warm", + "repeats": 3, + "runs": [ + { + "run": 1, + "wall_s": 5.368373390752822, + "edge_rows": 10000, + "node_rows": 20000 + }, + { + "run": 2, + "wall_s": 5.381817974150181, + "edge_rows": 10000, + "node_rows": 20000 + }, + { + "run": 3, + "wall_s": 5.381799325812608, + "edge_rows": 10000, + "node_rows": 20000 + } + ], + "wall_s": [ + 5.368373390752822, + 5.381817974150181, + 5.381799325812608 + ], + "median_s": 5.381799325812608 + }, + "koza_hosted_resolved": { + "status": "blocked", + "reason": "2026-10-29: NameResolution DNS lookup times out; independent Cloudflare query returns SERVFAIL with No Reachable Authority at renci.org. Hosted Koza probes failed before producing records." + } } \ No newline at end of file diff --git a/paper/benchmarks/koza_comparison/tutorial-data-10k.csv b/paper/benchmarks/koza_comparison/tutorial-data-10k.csv index 43208dfc..c7dff09d 100644 --- a/paper/benchmarks/koza_comparison/tutorial-data-10k.csv +++ b/paper/benchmarks/koza_comparison/tutorial-data-10k.csv @@ -1,10001 +1,10001 @@ -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 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung 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-KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic 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+KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +TP53,lung cancer,0.001,450 +BRCA1,breast cancer,0.0001,1200 +EGFR,colorectal cancer,0.005,680 +KRAS,pancreatic cancer,0.002,320 +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-koza-resolved.yaml b/paper/benchmarks/koza_comparison/tutorial-koza-resolved.yaml new file mode 100644 index 00000000..f52229c0 --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-koza-resolved.yaml @@ -0,0 +1,29 @@ +name: tutorial-koza-resolved + +reader: + files: + - ./tutorial-data-10k.csv + format: csv + delimiter: "," + +transform: + code: ./tutorial-transform-resolved.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 with hosted resolution) + description: Same mapping with hosted NameResolution and NodeNormalization. diff --git a/paper/benchmarks/koza_comparison/tutorial-koza.yaml b/paper/benchmarks/koza_comparison/tutorial-koza.yaml index bf3c8b3f..8cb939d0 100644 --- a/paper/benchmarks/koza_comparison/tutorial-koza.yaml +++ b/paper/benchmarks/koza_comparison/tutorial-koza.yaml @@ -2,7 +2,7 @@ name: tutorial-koza reader: files: - - ./tutorial-data.csv + - ./tutorial-data-10k.csv format: csv delimiter: "," diff --git a/paper/benchmarks/koza_comparison/tutorial-table.yaml b/paper/benchmarks/koza_comparison/tutorial-table.yaml index d6de616f..de785905 100644 --- a/paper/benchmarks/koza_comparison/tutorial-table.yaml +++ b/paper/benchmarks/koza_comparison/tutorial-table.yaml @@ -1,12 +1,9 @@ template: source: kind: text - local: ./tutorial-data.csv + local: ./tutorial-data-10k.csv url: - https://example.com/data.csv - row_slice: - - 1 - - auto delimiter: "," statement: subject: diff --git a/paper/benchmarks/koza_comparison/tutorial-transform-resolved.py b/paper/benchmarks/koza_comparison/tutorial-transform-resolved.py new file mode 100644 index 00000000..12153dad --- /dev/null +++ b/paper/benchmarks/koza_comparison/tutorial-transform-resolved.py @@ -0,0 +1,107 @@ +"""Koza 2.7.0 transform with hosted label resolution and normalization. + +Uses the same NameResolution then NodeNormalization client as the standalone +hosted benchmark. Raw labels require both endpoints: NodeNormalization accepts +CURIEs, not labels. Only the top-ranked candidate is emitted. Unresolved terms, +request failures, and incomplete canonical records fail the run. +""" + +import json +import sys +import time +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass, field +from pathlib import Path + +import koza + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "name_resolution")) +from bench_name_res import RequestBudget, canonical_record, group_normalized_by_requested, lookup_one, normalize_batch + +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_labels(records: list[dict[str, str]]) -> tuple[dict[str, dict[str, object]], dict[str, object]]: + terms = sorted({record[column] for record in records for column in ("gene_symbol", "disease_name")}) + started = time.perf_counter() + budget = RequestBudget(len(terms) + 20) + only_taxa = "NCBITaxon:9606" + with ThreadPoolExecutor(max_workers=4) as pool: + lookups = list(pool.map(lambda term: lookup_one(term, 5, budget, only_taxa=only_taxa), terms)) + lookup_attempts = len(terms) + 20 - budget.remaining + normalization_attempts_start = budget.remaining + curies = sorted({curie for result in lookups for curie in result["curies"]}) + normalization = normalize_batch(curies, budget) + if normalization["error"]: + raise RuntimeError(normalization["error"]) + grouped = group_normalized_by_requested(curies, normalization["normalized"]) + resolved: dict[str, dict[str, object]] = {} + for result in lookups: + if result["error"] or not result["curies"]: + raise RuntimeError(f"hosted lookup failed for {result['term']!r}: {result['error'] or 'no candidates'}") + record: dict[str, object] | None = canonical_record(grouped.get(result["curies"][0])) + if record is None: + raise RuntimeError(f"incomplete top-ranked canonical record for {result['term']!r}") + resolved[result["term"]] = record + report: dict[str, object] = { + "input_rows": len(records), + "unique_terms": len(terms), + "complete_records": len(resolved), + "candidate_curies": len(curies), + "http_calls": {"lookup": len(lookups), "get_normalized_nodes": normalization["http_calls"]}, + "http_attempts_including_retries": {"lookup": lookup_attempts, "get_normalized_nodes": normalization_attempts_start - budget.remaining}, + "resolution_wall_s": time.perf_counter() - started, + "lookup_limit": 5, + "lookup_concurrency": 4, + "only_taxa": only_taxa, + "selection": "top-ranked NameResolution candidate; no ties emitted", + } + return resolved, report + + +def categories(record: dict[str, object]) -> list[str]: + values = record["categories"] + if not isinstance(values, list): + raise TypeError("canonical categories must be a list") + return [str(value) for value in values] + + +@koza.transform() +def transform_rows(koza, data): + records = list(data) + resolved, report = resolve_labels(records) + Path("koza-hosted-resolution.json").write_text(json.dumps(report, indent=2)) + for record in records: + subject = resolved[record["gene_symbol"]] + obj = resolved[record["disease_name"]] + koza.write( + NamedThing(id=str(subject["id"]), name=str(subject["preferred_name"]), category=categories(subject)), + NamedThing(id=str(obj["id"]), name=str(obj["preferred_name"]), category=categories(obj)), + Association( + subject=str(subject["id"]), + predicate=ASSOCIATION, + object=str(obj["id"]), + 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/koza_comparison/tutorial-transform.py b/paper/benchmarks/koza_comparison/tutorial-transform.py index a5245fcb..df9df2af 100644 --- a/paper/benchmarks/koza_comparison/tutorial-transform.py +++ b/paper/benchmarks/koza_comparison/tutorial-transform.py @@ -1,8 +1,8 @@ """Koza 2.7.0 transform equivalent of the Tablassert tutorial example. Emits one associated_with edge per row plus its two nodes. Entity resolution is -NOT included: Koza provides no resolver, so identifiers must already be CURIEs -or be mapped by hand (see README for what this comparison measures). +NOT included in this mapping-only arm. The companion resolved transform adds +hosted NameResolution and NodeNormalization (see README). """ from dataclasses import dataclass, field diff --git a/paper/benchmarks/name_resolution/README.md b/paper/benchmarks/name_resolution/README.md index 16c2e8b8..7e5ed65b 100644 --- a/paper/benchmarks/name_resolution/README.md +++ b/paper/benchmarks/name_resolution/README.md @@ -1,65 +1,77 @@ -# Name resolution: local fullmap vs hosted SRI service +# Name resolution: local fullmap vs hosted SRI NameResolution + NodeNormalization -Compares Tablassert's embedded fullmap database (batched local lookups through -`tablassert.fullmap.resolve_batch`, the exact path a build runs) against the hosted -Translator SRI name resolution service () on -the same 113 terms. Terms are drawn from the DAKP production audit's most frequent -free-text subject/object pairs plus common ingest vocabulary; the exact list ships -in `terms.txt`. +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: -Hosted flows measured: +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. -- Single-hop: `GET /lookup?string=` -- what a build would call per term to get - candidate CURIEs. -- Multi-hop identifier-record workflow: `GET /lookup` for the string, then - `POST /synonyms` on the top CURIE to fetch the canonical identifier's preferred - name and categories. This is the two-request flow needed to obtain what one - fullmap fetch returns per term (canonical CURIE, preferred name, category, taxon, - source). +## What each run records -## Results (wenceslaus, 2026-10-28; tablassert 19.7.0; fullmap source 2026jul22) +- 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. -Fullmap store, uncompressed on disk (exact bytes via `stat`): +## Documented mismatches (explicit, not silently papered over) -| Component | Bytes | GB (decimal) | -| ------------------------------------ | ------------------ | -------------- | -| `fullmap` (primary redb) | 24,741,679,104 | 24.74 | -| `fullmap.s0..s15.redb` (16 shards) | 68,139,679,744 | 68.14 | -| Total store | 92,881,358,848 | 92.88 (86.50 GiB) | +- 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. -(`downloads/` in the same directory holds 38.08 GB of BABEL source exports; not part of the database.) +## Cold vs warm, repeats -See `results.json` for full detail; medians of 3 local repeats, hosted at -concurrency 4 with retry/backoff: +- 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. -| Metric | Local fullmap | Hosted (single-hop) | Hosted (multi-hop) | -| ------------------------------------- | -------------------------- | ----------------------------- | ----------------------------- | -| Throughput | 171 unique terms/s | 113 terms in 19.9 s (~5.7/s at concurrency 4) | 113 terms in 17.4 s (~6.5/s at concurrency 4) | -| Median latency per term | ~5 ms | 0.553 s | 0.576 s | -| Requests per term for full record | 0 (one batched fetch) | 2 | 2 | -| Coverage (terms with >= 1 hit) | 136 resolved rows from 113 terms (ties multi-resolve; misses dropped) | 113/113 | 113/113 (108 canonical records) | -| Offline reproducibility | yes | no | no | - -Notes: - -- `resolve_batch` deduplicates terms before the redb fetch, so throughput is - reported over unique terms; replicating rows (1130-row run, `terms_10x.txt`) - added no measurable lookup cost. -- The hosted service occasionally rate-limits bursts; the harness retries with - backoff, and hosted runs use modest concurrency (<= 6). -- Qualitative difference (not timed): the hosted API returns ranked hits with no - taxon constraint, category policy, prefix/regex exclusion, or per-column context; - the fullmap applies all of these per node column during the same batched fetch. - Worked example: the hosted top hit for "TP53" is the pig ortholog - (NCBITaxon:9823); a Tablassert subject column with `taxon: 9606` filters that - hit by construction. +`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 --repeats 3 + --python /path/to/python-with-tablassert --out results.json \ + --local-repeats 3 --hosted-concurrency 4 --hosted-max-requests 300 --limit 5 ``` -Requires a tablassert installation importable by `--python` and a built (or -downloaded) fullmap directory. +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 index 6893fd3c..853977bb 100644 --- a/paper/benchmarks/name_resolution/bench_name_res.py +++ b/paper/benchmarks/name_resolution/bench_name_res.py @@ -1,23 +1,51 @@ -"""Benchmark: local fullmap resolution (tablassert quick-map) vs hosted SRI name resolution. +"""Benchmark: local fullmap resolution vs hosted SRI NameResolution + NodeNormalization. -Compares Tablassert's embedded fullmap database against the Translator SRI hosted -name resolution service (https://name-resolution-sri.renci.org) on the same term -list. Terms are drawn from the DAKP production audit's most frequent free-text -subject/object pairs, so they are real ingest vocabulary, not synthetic. +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 \\ - --tablassert /path/to/tablassert --out results.json --repeats 3 \\ - --hosted-concurrency 6 --limit 5 + 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. See README.md for the recorded run environment. +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 @@ -25,189 +53,420 @@ 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 -HOSTED_BASE = "https://name-resolution-sri.renci.org" -HOSTED_URL = f"{HOSTED_BASE}/lookup" +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" -def read_terms(path: Path) -> list[str]: - terms: list[str] = [] - for line in path.read_text().splitlines(): - term = line.strip() - if term and not term.startswith("#"): - terms.append(term) - return terms +# ---------------------------------------------------------------- requests -def bench_local(python: str, fullmap: str, terms: list[str], repeats: int) -> dict: - """Time tablassert.fullmap.resolve_batch -- the exact resolution path a build runs. +class LookupRecord(TypedDict): + """Outcome of one stage-1 /lookup call.""" - Runs level_one/level_two normalization then resolve_batch over a DataFrame - holding every term, mirroring lib.build_graph_pipeline's op chain, and - collects the result. One process per repeat. - """ - runner = Path(__file__).with_name("_local_runner.py") - terms_file = Path(__file__).with_name("_terms.json.tmp") - terms_file.write_text(json.dumps(terms)) - code = """\nimport json, sys, time\nimport polars as pl\nfrom pathlib import Path\nfrom tablassert.fullmap import ResolveSpec, resolve_batch\nfrom tablassert.nlp import level_one, level_two\nterms = json.load(open(sys.argv[1]))\nrepeats = int(sys.argv[2])\ndb = Path(sys.argv[3])\nruns, resolved = [], []\nfor _ in range(repeats):\n lf = pl.LazyFrame({"term": terms})\n lf = level_two(level_one(lf, "term"), "term")\n spec = ResolveSpec(col="term", taxon="9606")\n start = time.perf_counter()\n out = resolve_batch(lf, [spec], db=db, log=False, column_context=True).collect()\n runs.append(time.perf_counter() - start)\n resolved.append(out.height)\nprint(json.dumps({"runs": runs, "resolved": resolved, "n_terms": len(terms), "columns": out.columns}))\n""" - runner.write_text(code) - argv = [python, str(runner), str(terms_file), str(repeats), fullmap] - start = time.perf_counter() - proc = subprocess.run(argv, capture_output=True, text=True, env={"PYTHONIOENCODING": "utf-8"}, check=False) - total = time.perf_counter() - start - if proc.returncode != 0: - msg = f"local runner exited {proc.returncode}: {proc.stderr[-800:]}" - raise RuntimeError(msg) - payload = json.loads(proc.stdout.strip().splitlines()[-1]) - runs = payload["runs"] - n_unique = len(set(terms)) - return { - "n_terms": payload["n_terms"], - "n_unique_terms": n_unique, - "repeats": repeats, - "resolve_wall_s": runs, - "resolve_wall_s_median": statistics.median(runs), - "unique_terms_per_s_median": n_unique / statistics.median(runs), - "per_unique_term_ms_median": statistics.median(runs) / n_unique * 1000, - "resolved_rows": payload["resolved"], - "process_total_s_incl_startup": total, - "note": "resolve_batch dedups terms before the redb fetch; throughput is over unique terms", - } + 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(req: urllib.request.Request, timeout: float) -> object: - """Open a request with two retries (exponential backoff) on transient errors.""" +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(3): + for attempt in range(attempts): + if budget is not None: + budget.spend() try: - return urllib.request.urlopen(req, timeout=timeout) + 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 (429, 500, 502, 503, 504) and attempt < 2: + if exc.code in RETRYABLE and attempt < attempts - 1: time.sleep(2.0 * (attempt + 1)) continue raise - except urllib.error.URLError as exc: + except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: last = exc - if attempt < 2: + if attempt < attempts - 1: time.sleep(2.0 * (attempt + 1)) continue raise raise last if last else RuntimeError("unreachable") -def hosted_lookup(term: str, limit: int, timeout: float = 30.0, canonicalize: bool = False) -> dict: - """One term through the hosted flow. With canonicalize, runs the full identifier-record - workflow: /lookup for the string, then /synonyms on the top curie to fetch the - canonical identifier's preferred name and categories (two HTTP calls). +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. """ - url = f"{HOSTED_BASE}/lookup?string={urllib.parse.quote(term)}&limit={limit}" + 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() - record: dict = {"term": term, "status": None, "latency_s": None, "curies": [], "canonical": None} try: - req = urllib.request.Request(url, headers={"User-Agent": "tablassert-benchmark/1.0"}) - with _fetch(req, timeout) as resp: - body = json.loads(resp.read().decode("utf-8")) - record["status"] = resp.status - record["curies"] = [hit.get("curie") for hit in body[:limit] if hit.get("curie")] - if canonicalize and record["curies"]: - payload = json.dumps({"preferred_curies": [record["curies"][0]]}).encode("utf-8") - req = urllib.request.Request( - f"{HOSTED_BASE}/synonyms", data=payload, headers={"User-Agent": "tablassert-benchmark/1.0", "Content-Type": "application/json"} - ) - with _fetch(req, timeout) as resp: - syn = json.loads(resp.read().decode("utf-8")) - entry = syn.get(record["curies"][0], {}) - record["canonical"] = { - "curie": entry.get("curie"), - "preferred_name": entry.get("preferred_name"), - "types": entry.get("types", [])[:3], - } - except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: - record["status"] = f"error: {type(exc).__name__}" + 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 bench_hosted(terms: list[str], concurrency: int, limit: int, canonicalize: bool = False) -> dict: - results: list[dict] = [] +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(hosted_lookup, term, limit, 30.0, canonicalize): term for term in terms} - for future in as_completed(futures): - results.append(future.result()) - wall = time.perf_counter() - wall_start - ok = [r for r in results if r["status"] == 200] - lat = [r["latency_s"] for r in ok] - got_canonical = sum(1 for r in ok if r.get("canonical")) + 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 { - "n_terms": len(terms), + "repeats": 1, "concurrency": concurrency, - "canonicalize": canonicalize, - "wall_s": wall, - "ok": len(ok), - "errors": len(results) - len(ok), - "coverage": len(ok) / len(results), - "canonical_records": got_canonical, - "latency_s": { - "median": statistics.median(lat) if lat else None, - "p95": sorted(lat)[int(len(lat) * 0.95) - 1] if lat else None, - "mean": statistics.mean(lat) if lat else None, + "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), }, - "terms_per_s_effective": len(ok) / wall if wall else None, - "records": results, + "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": ( - "per-term /lookup + /synonyms, the full identifier-record workflow" - if canonicalize - else f"per-term HTTP GET, concurrency {concurrency}; a build would need one request per term" + "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", 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("--repeats", type=int, default=3) - parser.add_argument("--hosted-concurrency", type=int, default=6) + 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() - terms = read_terms(args.terms) - version = subprocess.run( - [args.python, "-c", 'from importlib.metadata import version; print(version("tablassert"))'], capture_output=True, text=True, check=True - ).stdout.strip() + 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 = { + result: dict[str, object] = { "date_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), - "tablassert_version": version, - "fullmap": args.fullmap, - "hosted_endpoint": HOSTED_URL, - "local": bench_local(args.python, args.fullmap, terms, args.repeats), + "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) - result["hosted_multihop"] = bench_hosted(terms, args.hosted_concurrency, args.limit, canonicalize=True) + 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)) - local = result["local"] - print(f"local: {local['unique_terms_per_s_median']:.0f} unique terms/s (median of {local['repeats']} runs)") - if "hosted" in result: - hosted = result["hosted"] - med = hosted["latency_s"]["median"] - med_s = f"{med:.3f}" if med is not None else "n/a" - print(f"hosted: {hosted['ok']}/{hosted['n_terms']} ok, median latency {med_s}s/term") - multi = result.get("hosted_multihop") - if multi: - mmed = multi["latency_s"]["median"] - mmed_s = f"{mmed:.3f}" if mmed is not None else "n/a" - print(f"hosted multihop: {multi['ok']}/{multi['n_terms']} ok, median {mmed_s}s/term, canonical {multi['canonical_records']}") - print(f"wrote {args.out}") + 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__": 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 + }, + { + "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": "NCBIGene:1956", + "preferred_name": "EGFR", + "category": "biolink:Gene", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "ELIGARD", + "canonical_id": "UMLS:C1170247", + "preferred_name": "Eligard", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Eubacterium rectale", + "canonical_id": "UMLS:C0317478", + "preferred_name": "Agathobacter rectalis", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Faecalibacterium prausnitzii", + "canonical_id": "NCBITaxon:853", + "preferred_name": "Faecalibacterium prausnitzii", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "GILENYA", + "canonical_id": "UMLS:C2938762", + "preferred_name": "Gilenya", + "category": "biolink:ChemicalEntity", + "taxon": null + }, + { + "input_term": "Heavy menstrual bleeding", + "canonical_id": "HP:0000132", + "preferred_name": "Menorrhagia", + "category": "biolink:PhenotypicFeature", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Helicobacter pylori", + "canonical_id": "NCBITaxon:210", + "preferred_name": "Helicobacter pylori", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Helicobacter pylori", + "canonical_id": "NCBITaxon:210", + "preferred_name": "Helicobacter pylori", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Helicobacter pylori", + "canonical_id": "NCBITaxon:210", + "preferred_name": "Helicobacter pylori", + "category": "biolink:OrganismTaxon", + "taxon": null + }, + { + "input_term": "Hypermagnesemia", + "canonical_id": "HP:0002918", + "preferred_name": "Hypermagnesemia", + "category": "biolink:PhenotypicFeature", + "taxon": "NCBITaxon:9606" + }, + { + "input_term": "Hypermagnesemia", + "canonical_id": "NCIT:C37969", + "preferred_name": "Hypermagnesemia", + "category": "biolink:PhenotypicFeature", + "taxon": null + }, + { + "input_term": 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+ }, + { + "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": 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b/paper/benchmarks/name_resolution/results.json @@ -1,3744 +1,17025 @@ { - "date_utc": "2026-10-28T20:46:13Z", + "date_utc": "2026-10-29T20:17:22Z", "tablassert_version": "19.7.0", + "python_version": "3.12.3", + "platform": "Linux-6.8.0-142-generic-x86_64-with-glibc2.39", "fullmap": "/local_raid1/sgoetz/DBSTORE/FULLMAP/fullmap", - "hosted_endpoint": "https://name-resolution-sri.renci.org/lookup", + "unique_terms_deterministic": true, + "terms": [ + "ADIPOQ", + "AMPYRA", + "APOE", + "ARELTO", + "AUBAGIO", + "AVANDIA", + "Acne", + "Alistipes", + "Alzheimer disease", + "Atrial fibrillation", + "BRCA1", + "BRCA2", + "Bifidobacterium", + "Breast cancer", + "C-reactive protein", + "CHANTIX", + "CIIMZA", + "CIMZIA", + "CONTRACEPTION", + "COVID-19 treatment", + "Chronic pain", + "Colorectal cancer", + "Contrast dye", + "Crohn's disease", + "DIALUID", + "DIANA LOW CALCIUM PERITONEAL DIALYSIS SOLUTION WITH DEXTROSE", + "DIANEAL LOW CALCIUM PERITONEAL DIALYSIS SOLUTION WITH DEXTROSE", + "Deep vein thrombosis", + "Diabetes mellitus type 2", + "Dialysis solution", + "EGFR", + "ELIGARD", + "Eubacterium rectale", + "Faecalibacterium prausnitzii", + "GILENYA", + "Heavy menstrual bleeding", + "Helicobacter pylori", + "Hypermagnesemia", + "IL6", + "KRAS", + "LEP", + "Lactobacillus", + "MIRENA", + "Multiple sclerosis", + "NUPLAZID", + "Narcolepsy", + "OTEZLA", + "OXYCONTIN", + "PAXLOVID", + "PROACTIV MD ADAPALENE ACNE TREATMENT", + "PROACTIV MD DAILY OIL CONTROL SPF 30", + "PROACTIV MD DEEP CLEANSING FACE WASH", + "Parkinson's disease psychosis", + "Peritoneal dialysis", + "Prostate cancer", + "Psoriasis", + "RINVOQ", + "Rheumatoid arthritis", + "Rivaroxaban", + "SIMPONI", + "Schizophrenia", + "Smoking cessation", + "Staphylococcus aureus", + "TECEFIDERA", + "TNF", + "TP53", + "Thrombosis prophylaxis", + "Ulcerative colitis", + "XARELTO", + "XARELTO XR", + "XELJANZ", + "XYREM", + "ZYPREXA", + "antibiotics", + "aspirin", + "atororvastatin", + "attention deficit hyperactivity 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"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." + } + }, + "hosted": { + "concurrency": 4, + "lookup_limit": 5, + "wall_s": 50.22906791837886, + "normalization_wall_s": 42.168682085350156, + "http_calls": { + "lookup": 104, + "get_normalized_nodes": 5 + }, + "coverage": { + "http_ok": 104, + "nonempty_candidates": 99, + "complete_record": 99, + "complete_record_human_pref": 99, + "n_terms": 104 + }, + "latency_s": { + "median": 0.2940373537130654, + "mean": 0.30969754319029075 + }, + "terms_per_s_effective": 2.07051423229668, + "normalization_error": null, + "records": [ { - "term": "XARELTO XR", - "status": 200, - "latency_s": 0.5682737822644413, - "curies": [ - "CHEBI:207316", - "CHEBI:209098", - "PUBCHEM.COMPOUND:9932718", - "UNII:748C75726M", - "CHEBI:216666" + "term": "ADIPOQ", + "http_status": 200, + "error": null, + "latency_s": 0.47558964928612113, + "candidate_curies": [ + "NCBIGene:9370", + "NCBIGene:100874095", + "UniProtKB:Q6TCH7-4", + "UniProtKB:Q6TCH7-1", + 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"biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": [ + "NCBITaxon:9606" + ] + } + } ], "canonical": { - "curie": "UMLS:C0031139", - "preferred_name": "Peritoneal Dialysis", - "types": [ - "Procedure", - "ActivityAndBehavior", - "NamedThing" + "id": "NCBIGene:9370", + "preferred_name": "ADIPOQ", + "categories": [ + "biolink:Gene", + "biolink:GeneOrGeneProduct", + "biolink:GeneOrGeneProductOrGeneFamily", + "biolink:GenomicEntity", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:PhysicalEssence", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:PhysicalEssenceOrOccurrent", + "biolink:MacromolecularMachineMixin", + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrProteinOrPolypeptide" + ], + "taxa": [ + "NCBITaxon:9606" + ] + }, + "human_pref": { + "id": "NCBIGene:9370", + "preferred_name": "ADIPOQ", + "categories": [ + "biolink:Gene", + "biolink:GeneOrGeneProduct", + "biolink:GeneOrGeneProductOrGeneFamily", + "biolink:GenomicEntity", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:PhysicalEssence", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:PhysicalEssenceOrOccurrent", + "biolink:MacromolecularMachineMixin", + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrProteinOrPolypeptide" + ], + "taxa": [ + "NCBITaxon:9606" ] } }, { - "term": "XELJANZ", - "status": 200, - "latency_s": 0.5759973279200494, - "curies": [ - "CHEBI:71197", - "CHEBI:71200", - "UMLS:C4238144" + "term": "AMPYRA", + "http_status": 200, + "error": null, + 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"biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:PhysicalEssence", + "biolink:OntologyClass", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:PhysicalEssenceOrOccurrent", + "biolink:MacromolecularMachineMixin", + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrProteinOrPolypeptide" + ], + "taxa": [ + "NCBITaxon:9606" + ] + } + }, + { + "curie": "UMLS:C0003595", + "canonical": { + "id": "UMLS:C0003595", + "preferred_name": "Apolipoprotein E", + "categories": [ + "biolink:Protein", + "biolink:GeneProductMixin", + "biolink:Polypeptide", + "biolink:ChemicalEntityOrGeneOrGeneProduct", + "biolink:ChemicalEntityOrProteinOrPolypeptide", + "biolink:BiologicalEntity", + "biolink:ThingWithTaxon", + "biolink:NamedThing", + "biolink:GeneOrGeneProduct", + "biolink:MacromolecularMachineMixin" + ], + "taxa": null + } + }, + { + "curie": "PR:000004155", + "canonical": { + "id": "PR:000004155", + 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"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 + } } ], - "note": "per-term /lookup + /synonyms, the full identifier-record workflow" - } -} \ No newline at end of file + "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/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/paper.md b/paper/paper.md index 81729cfb..53cf5b83 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -45,23 +45,24 @@ sources and need rigorously structured output without maintaining a transform pi source. Extending beyond journal supplements, the same recipes apply to any relational source -- trial registries, drug approval records, or a researcher's own tables -- and each build can emit the Resource Ingest Guide metadata required for integration with Translator. Recipes are -validated before a build starts, so configuration errors fail early with field-specific messages. +validated before a build starts, so configuration errors fail early with field-specific +messages. # State of the field -The prevailing practice for Translator ingests is bespoke Python per data source, with generic -tools at each layer that none of them composes. KGX [@kgx] exchanges and validates graphs but -provides no column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] -ingest engine, pairs YAML configuration with semi-declarative Python transforms and outputs KGX, -but leaves entity resolution to separate services or mappings. LinkML [@linkml] models the -schema of a finished knowledge graph generically, but a schema says nothing about how a -spreadsheet column becomes a node. BioCypher [@biocypher] standardizes biomedical graph -construction, and with OntoWeaver maps tabular data into semantic graphs [@ontoweaver], but both -assume a curated ontology-aligned model rather than ad hoc research tables. SPIRES [@spires] -extracts knowledge from free text by zero-shot prompting, while Tablassert's sources are already -structured tables. Finally, the hosted SRI name resolution service [@nameres] serves BABEL -[@babel] identifier mappings as a network API, which makes bulk build-time lookups slow and -irreproducible offline. +Translator ingests commonly use source-specific Python with tools for exchange, schema modeling, +and transformation. KGX [@kgx] exchanges and validates graphs but provides no +column-to-statement mapping. Koza [@koza], the Monarch Initiative's [@monarch] ingest engine, +pairs YAML configuration with semi-declarative Python transforms and outputs KGX, with entity +resolution supplied through mappings or additional code. LinkML [@linkml] provides schema +modeling and validation rather than Tablassert's integrated resolution pipeline. BioCypher +[@biocypher] standardizes biomedical graph construction; OntoWeaver [@ontoweaver] provides +declarative tabular mappings for BioCypher. These are related approaches, not inherently +unsuitable for research tables. SPIRES [@spires] extracts knowledge from free text by zero-shot +prompting, while Tablassert's sources are already structured tables. Finally, the hosted SRI +name resolution service [@nameres] serves BABEL [@babel] identifier resources through a network +API. Hosted builds depend on service availability; reproducing historical results requires +preserving responses or pinning the underlying resources. Tablassert composes declarative mapping, local resolution, and mapping-quality checks into one recipe-driven pipeline. @@ -71,11 +72,9 @@ recipe-driven pipeline. Tablassert's core trade-off is up-front work for build-time ease: a large entity-resolution database is built (or downloaded) once, and every subsequent build resolves millions of terms locally in batched lookups without service calls. Recipes are compiled into TCode, an ordered -sequence of (callable, arguments) operations, executed through Polars lazy frames. As a result, -independent transformations fuse and materialize only at explicit boundaries: batched -resolution, quality control, and output writing. Because operations are first-class values, -sections that share a prefix of instructions reuse its computed snapshot instead of recomputing -it. +sequence of (callable, arguments) operations, executed through Polars lazy frames. Independent +transformations fuse until batched resolution, quality control, or output writing requires +materialization. Sections sharing an instruction prefix reuse its computed snapshot. The embedded fullmap database underpins the design. Built from BABEL [@babel] exports, it maps normalized synonyms, including the meta-identifiers that unify the same concept across source @@ -104,41 +103,48 @@ A priority score ranks surviving candidates -- exact preferred-name equality ran level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multiplied by a category bonus of 1 for prioritized categories and 50 otherwise -- then deduplicates them to the best tier, retaining distinct CURIEs that tie. When column context is on, category frequency -across the column being resolved breaks remaining ties, so a column of gene symbols resolves -ambiguous short names as genes instead of proteins or a phenotypic feature with the same name. -These are query shapes the hosted API does not offer: it takes a string and returns ranked -identifiers, with no taxon constraint, category policy, or per-column context, and the full -identifier-record workflow (string to identifier, then canonicalization for preferred name and -category) takes two HTTP calls per term, while fullmap returns all of it in one batched fetch. -Benchmarks on a dual-socket Intel Xeon Gold 6230 host (40 physical cores, 80 hardware threads, -187 GiB RAM, SSD RAID1, Ubuntu 24.04) used 113 ingest terms: local resolution sustained 171 -unique terms per second through the build's batch path, versus a median of 0.55 seconds per -hosted call (0.58 seconds for the two-call workflow). - -Every edge carries recipe-declared structured provenance -- source record URLs, supporting -publications, and the processing context that produced it -- so an assertion traces back to its -row and evidence. Optional quality control audits each resolved name with a four-stage cascade: -exact or alias match, fuzzy string similarity, Schwartz-Hearst abbreviation expansion, then -SapBERT [@sapbert] embedding similarity using a checkpoint fine-tuned on BABEL synonyms +across the column being resolved helps break remaining ties; this is a heuristic, not a +guarantee of semantic correctness. Hosted NameResolution also supports bulk lookup, taxon and +Biolink-type filters, and CURIE prefix inclusion/exclusion. Fullmap additionally integrates +per-column category-ranking policies, regex exclusions, and column-context ranking. Hosted +candidates can be canonicalized through batched Node Normalization requests; two network stages +need not mean two requests per term. On a dual-socket Intel Xeon Gold 6230 host (40 physical +cores, 80 hardware threads, 187 GiB RAM, SSD RAID1, Ubuntu 24.04), 113 hand-selected ingest +entries comprised 104 unique terms. Three fresh-process local batch passes took a median 0.886 +seconds, excluding interpreter startup; three in-process passes took a median 0.516 seconds. OS +caches were not cleared. Local resolution returned complete identifier, name, and category +fields for 93 terms. One hosted sweep returned complete top-ranked canonical records for 99 +terms in 50.23 seconds: 104 concurrent-limited lookups plus five batched normalization requests, +with no request errors. Matching policies and resource versions differ; this convenience sample +measures neither accuracy nor general service performance. + +Edges carry recipe-declared provenance, including source URLs and publications when provided; +traceability depends on the source and configuration. Optional quality control audits each +resolved name with a four-stage cascade: exact or alias match, fuzzy string similarity, +Schwartz-Hearst abbreviation expansion, then SapBERT [@sapbert] embedding similarity using a +checkpoint fine-tuned on BABEL synonyms (`RenaissanceComputing/SapBERT-PubMedBERT-BabelSynonyms-v2025-01-23`), each stage handling only what cheaper stages could not clear. Suspect mappings are dropped and logged with their scores rather than silently retained. For example, a DAKP FAERS audit (2026-10-22) 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]. Output correctness is checked in two passes: an optional study stage asserts -structural invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required -fields, no edges demoted to bare Association). The `validate-kgx` command goes beyond KGX's -schema-driven `validate` [@kgx]: every node and edge is constructed as the Biolink class with -its own category names, knowledge-source identity is checked against the Translator infores -registry offline, and `--prune` rewrites the edges file in place to its strictly-compliant -subset, with the exit code gating the post-prune state. Tablassert uses PyO3 Rust for -normalization, resolution, database construction, and NDJSON writing. GIL-released Rust regions -use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel execution -even on GIL-enabled Python 3.11, without requiring free-threaded Python. This removes per-term -Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. -Contrasting Koza, the tutorial example maps both ways: 79 lines of YAML plus Python with no -entity resolution, against the 32-line Tablassert recipe (10,000 rows: 6.9 seconds including -resolution against the fullmap, 5.4 seconds without it). +[@prothrombin]. This is one rejection, not measured QC precision or recall. Structural +validation is separate from scientific correctness: an optional study stage asserts structural +invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required fields, no +edges demoted to bare Association). Like KGX's schema-driven `validate` [@kgx], `validate-kgx` +checks graph records; it constructs records using category-specific Biolink classes, +knowledge-source identity is checked against the Translator infores registry offline, and +`--prune` removes edges failing its checks in place, with the exit code gating the post-prune +state. Passing these checks does not establish biological validity. Tablassert uses PyO3 Rust +for normalization, resolution, database construction, and NDJSON writing. GIL-released Rust +regions use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel +execution even on GIL-enabled Python 3.11, without requiring free-threaded Python. This removes +per-term Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. +The tutorial uses 29 recipe lines plus a 24-line graph wrapper; Koza's mapping-only version uses +25 YAML and 31 Python lines, excluding comments and docstrings. Koza supports custom hosted +resolution, avoiding local storage but adding code and service dependencies. Tablassert +integrates resolution but requires the fullmap. Its retained ties differ from the hosted +transform's top-hit policy, precluding identical-graph runtime claims. # Research impact statement @@ -164,15 +170,16 @@ Generative AI assisted with code generation, test writing, documentation, and dr revising this manuscript, primarily through a customized Pi coding agent (version 0.81.1). AI spelling and grammar checks covered documentation and manuscript, including Grammarly for the manuscript. Coding-agent use began on 27 January 2026 (`50f29e8`); co-author credits record -Claude Sonnet 4.5 and Opus 4.5/4.6 (context variants grouped). Initial versions used no coding agents. +Claude Sonnet 4.5 and Opus 4.5/4.6 (context variants grouped). Initial versions used no coding +agents. -Models used included `claude-opus-5`, `claude-opus-5-5`, -`claude-sonnet-5`, `claude-sonnet-5-5`, `deepseek-v4-pro`, `deepseek-v4-flash`, -`gemini-3.8-flash`, `glm-5.3`, `glm-5.3-flash`, `gpt-5.6-luna`, `gpt-5.6-sol-review`, -`gpt-5.6-terra`, `gpt-6-astra`, `gpt-6-luna`, `gpt-6-sol`, `gpt-6.1-sol`, `kimi-k2.5`, -`kimi-k2.6`, `kimi-k2.7-pro`, `kimi-k2.8-preview`, `kimi-k3`, `kimi-k3.1-pro`, -`muse-spark-1.3-contributor-free`, `nemotron-3-ultra-550b-a55b:free`, `laguna-s-2.1`, -`qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and `mimo-v2.6-pro`. +Models used included `claude-opus-5`, `claude-opus-5-5`, `claude-sonnet-5`, `claude-sonnet-5-5`, +`deepseek-v4-pro`, `deepseek-v4-flash`, `gemini-3.8-flash`, `glm-5.3`, `glm-5.3-flash`, +`gpt-5.6-luna`, `gpt-5.6-sol-review`, `gpt-5.6-terra`, `gpt-6-astra`, `gpt-6-luna`, `gpt-6-sol`, +`gpt-6.1-sol`, `kimi-k2.5`, `kimi-k2.6`, `kimi-k2.7-pro`, `kimi-k2.8-preview`, `kimi-k3`, +`kimi-k3.1-pro`, `muse-spark-1.3-contributor-free`, `nemotron-3-ultra-550b-a55b:free`, +`laguna-s-2.1`, `qwen3.8-flash`, `qwen3.8-max`, `qwen3.8-max-preview`, `mimo-v2.5`, and +`mimo-v2.6-pro`. A bespoke LLM-as-judge implementation reviewed code before pull requests; CodeRabbit occasionally reviewed pull requests before Goetz. Validation combined manual auditing, automated @@ -188,7 +195,8 @@ manuscript. # Author contributions Skye Lane Goetz developed the software, led design, and performed analyses. Jared C. Roach and -Gwênlyn Glusman helped design the software and analyses and provided detailed project feedback. All authors will write, revise, and proofread. +Gwênlyn Glusman helped design the software and analyses and provided detailed project feedback. +All authors will write, revise, and proofread. # Acknowledgements From fe7c579dc191df2d7510f3331f0fc87760b7e6eb Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 12:18:36 -0700 Subject: [PATCH 17/25] docs(paper): replace Koza timing with configuration-size comparison --- paper/benchmarks/README.md | 13 +- paper/benchmarks/koza_comparison/README.md | 101 +- paper/benchmarks/koza_comparison/count_loc.py | 76 +- paper/benchmarks/koza_comparison/loc.json | 35 +- paper/benchmarks/koza_comparison/make_10k.py | 40 - .../koza_comparison/measure_koza.py | 167 - .../koza_comparison/test_bench_koza.py | 80 +- paper/benchmarks/koza_comparison/timing.json | 114 - .../koza_comparison/tutorial-data-10k.csv | 10001 ---------------- .../tutorial-koza-resolved.yaml | 29 - .../koza_comparison/tutorial-koza.yaml | 2 +- .../koza_comparison/tutorial-table.yaml | 5 +- .../tutorial-transform-resolved.py | 107 - .../koza_comparison/tutorial-transform.py | 4 +- paper/paper.md | 10 +- 15 files changed, 113 insertions(+), 10671 deletions(-) delete mode 100644 paper/benchmarks/koza_comparison/make_10k.py delete mode 100644 paper/benchmarks/koza_comparison/measure_koza.py delete mode 100644 paper/benchmarks/koza_comparison/timing.json delete mode 100644 paper/benchmarks/koza_comparison/tutorial-data-10k.csv delete mode 100644 paper/benchmarks/koza_comparison/tutorial-koza-resolved.yaml delete mode 100644 paper/benchmarks/koza_comparison/tutorial-transform-resolved.py diff --git a/paper/benchmarks/README.md b/paper/benchmarks/README.md index 5fe21c89..1d58186e 100644 --- a/paper/benchmarks/README.md +++ b/paper/benchmarks/README.md @@ -7,12 +7,11 @@ scripts, inputs, and recorded results with the environment they were measured in 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/`: Tablassert local resolution, Koza mapping only, and Koza - with hosted NameResolution plus NodeNormalization over the same table. The - executable harness records cold and warm runtime, output counts, resolution - coverage, and the policies that make the resulting graphs non-equivalent. +- `koza_comparison/`: line and non-whitespace-character counts for the + Tablassert tutorial recipe and a mapping-only Koza transform. No runtime is + benchmarked. -All runs were executed on the `wenceslaus` build host (80 cores, 187 GB RAM, -Ubuntu Linux); per-run environment details (versions, dates, repeat counts) are -recorded inside each `results.json`. Numbers in the manuscript come from these +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/koza_comparison/README.md b/paper/benchmarks/koza_comparison/README.md index 187db3c4..dd42198e 100644 --- a/paper/benchmarks/koza_comparison/README.md +++ b/paper/benchmarks/koza_comparison/README.md @@ -1,93 +1,28 @@ -# Koza comparison: mapping and resolution workloads +# Koza comparison: configuration size and verbosity -The same deterministic 10,000-row CSV (`tutorial-data-10k.csv`, generated by -`make_10k.py`) supports three explicitly separate workloads: +This directory compares the size of two mappings for the same four-row tutorial +CSV (`tutorial-data.csv`). It does not benchmark runtime. -1. `tablassert_*`: `tutorial-graph.yaml` and `tutorial-table.yaml` build the graph - with local fullmap resolution, category prioritization, deduplicated KGX NDJSON - nodes, provenance, and a RIG. -2. `koza_mapping_only_*`: `tutorial-koza.yaml` and `tutorial-transform.py` map each - row to KGX TSV but emit raw labels as node IDs. This isolates mapping cost; it is - not a resolved-graph comparison. -3. `koza_hosted_resolved_*`: `tutorial-koza-resolved.yaml` and - `tutorial-transform-resolved.py` deduplicate labels, call hosted NameResolution - (four concurrent lookups, at most five candidates, `only_taxa=NCBITaxon:9606`), - then call NodeNormalization in batches of 100 CURIEs. The top-ranked candidate - supplies the canonical ID, preferred name, and categories. Empty or incomplete - results fail the run. The HTTP implementation is imported from the sibling - `name_resolution/bench_name_res.py` harness so both benchmarks use the same API - workflow. +- Tablassert: `tutorial-table.yaml` maps columns to statements, provenance, and + annotations. `tutorial-graph.yaml` is the separate graph wrapper with fullmap + and RIG metadata. +- Koza: `tutorial-koza.yaml` configures input and output, and + `tutorial-transform.py` implements row-to-node and row-to-edge mapping. -Koza 2.7.0 provides mapping tables but no built-in label resolver. Its -`@koza.transform()` whole-dataset hook supports this custom integration. Hosted -resolution therefore demonstrates additional code and network cost, not a missing -capability that cannot be implemented. +The Koza example emits raw labels as node identifiers. It does not implement entity +resolution, whereas the Tablassert recipe uses fullmap resolution. The comparison +therefore shows where mapping logic is expressed and how much text each example +requires; it is not an equivalent end-to-end workflow or general complexity metric. -## Measuring - -Run on the build host with both installed CLIs: +## Counting ```bash -python make_10k.py -python measure_koza.py --repeats 3 \ - --tablassert /users/sgoetz/Code/dakp/.venv/bin/tablassert \ - --koza ./koza-venv/bin/koza --out timing.json +python count_loc.py --out loc.json ``` -Every measurement includes a fresh CLI process. The first repeat of each workload -is labeled cold; immediate repeats are labeled warm. Output directories are removed -before every run. OS, database, and remote-service caches are never cleared, so -"cold" does not mean cold OS or service caches. Cold is one first-pass measurement; warm is three subsequent passes. They are -reported separately and are not pooled. - -Each run verifies edge and node counts. Hosted runs also record unique terms, -complete canonical records, request counts, and resolution time in -`koza-hosted-resolution.json`, copied into the corresponding `timing.json` run. -Versions, platform, CPU count, and RAM are recorded. - -## Output and policy differences - -The workloads all perform tabular mapping, but their graphs are not equivalent: - -- Tablassert emits 12,500 edges from 10,000 input rows because `pancreatic cancer` - retains two equally ranked canonical CURIEs in the installed fullmap. It emits - nine deduplicated nodes. -- Resolved Koza emits only the top hosted candidate, hence 10,000 edges. Its TSV - writer emits two node records per input row (20,000 node rows), including repeats. -- Mapping-only Koza also emits 10,000 edges and 20,000 repeated node rows, but IDs - are unresolved input labels. - -Consequently the timings compare implementation and infrastructure costs for these -workloads, not throughput of identical graphs. Hosted candidate ranking, taxon -handling, and tie behavior differ from local fullmap ranking. A small synthetic -input with eight unique labels measures per-workflow overhead; it does not -represent resolution throughput on a large vocabulary. - -## Configuration trade-offs - -Tablassert's basic table recipe has 29 counted lines and its separate graph wrapper -24; Koza's mapping-only YAML and Python transform have 25 and 31. Blank lines, -comments, and Python docstrings are excluded by `count_loc.py`. The resolved Koza -configuration and integration module have 25 and 86 counted lines, excluding the -shared HTTP client imported from the name-resolution benchmark. These counts -illustrate where logic lives; they are not a general complexity metric. - -Tablassert's advantage here is integrated, offline, batched resolution with -column-aware policy and graph-level outputs. Its costs are the 92.9 GB fullmap and -building or obtaining that versioned resource. The hosted Koza arm avoids that -local store and can use current service content, but needs imperative integration -code and inherits network latency, availability, rate limits, and remote-version -drift. Mapping-only Koza is cheaper because it does less work, not because it is a -faster equivalent resolver. - -## Current hosted execution status - -The hosted arm is blocked as of 2026-10-29: NameResolution DNS lookups timed out, -and an independent Cloudflare DNS query returned `SERVFAIL` with -`No Reachable Authority` at `renci.org`. Live Koza probes failed before emitting -records. `timing.json` records this blocker explicitly; no hosted Koza timing is -claimed. To regenerate the available arms while recording the failure, use -`--skip-hosted-reason 'description of verified network failure'`. Omit that flag -when the service recovers to run all three workloads. +`count_loc.py` excludes blank lines, comment-only lines, and Python docstrings. +Verbosity is the number of non-whitespace characters on the counted lines, so +indentation style does not affect it. The Tablassert table recipe and graph wrapper +are reported separately and as a total. Offline tests: `pytest test_bench_koza.py`. diff --git a/paper/benchmarks/koza_comparison/count_loc.py b/paper/benchmarks/koza_comparison/count_loc.py index b533946a..608bc209 100644 --- a/paper/benchmarks/koza_comparison/count_loc.py +++ b/paper/benchmarks/koza_comparison/count_loc.py @@ -1,13 +1,10 @@ #!/usr/bin/env python3 -"""Line-of-code counter for the koza comparison: no blanks, no docstrings. +"""Count configuration and code size for the Tablassert and Koza tutorial mappings. -Python files: docstrings are excluded via the AST (module, class, and function -docstrings), blank lines are never counted, and comment-only lines are not code. -YAML files have no docstrings; blank lines are excluded. - -The Tablassert graph wrapper (tutorial-graph.yaml) is always reported as its own -line item, separate from the recipe, so the two configuration layers are visible -instead of silently summed. +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 @@ -18,8 +15,8 @@ from pathlib import Path -def count_python(path: Path) -> int: - """Count non-blank, non-docstring, non-comment lines of a Python file.""" +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]] = [] @@ -28,20 +25,40 @@ def count_python(path: Path) -> int: 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)) - count = 0 + lines: list[str] = [] for lineno, line in enumerate(source.splitlines(), start=1): stripped = line.strip() - if not stripped or stripped.startswith("#"): + if not stripped or stripped.startswith("#") or any(start <= lineno <= end for start, end in docstring_spans): continue - if any(start <= lineno <= end for start, end in docstring_spans): - continue - count += 1 - return count + 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: - """Count non-blank lines of a YAML file (comments excluded).""" - return sum(1 for line in path.read_text(encoding="utf-8").splitlines() if line.strip() and not line.strip().startswith("#")) + 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: @@ -50,20 +67,19 @@ def main() -> None: parser.add_argument("--out", type=Path, help="optional JSON output path") args = parser.parse_args() - counts = { - "tablassert_table_yaml": count_yaml(args.dir / "tutorial-table.yaml"), - "tablassert_graph_wrapper_yaml": count_yaml(args.dir / "tutorial-graph.yaml"), - "koza_yaml": count_yaml(args.dir / "tutorial-koza.yaml"), - "koza_transform_py": count_python(args.dir / "tutorial-transform.py"), - "koza_resolved_yaml": count_yaml(args.dir / "tutorial-koza-resolved.yaml"), - "koza_resolved_transform_py": count_python(args.dir / "tutorial-transform-resolved.py"), + 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["tablassert_recipe_total"] = counts["tablassert_table_yaml"] + counts["tablassert_graph_wrapper_yaml"] - counts["koza_total"] = counts["koza_yaml"] + counts["koza_transform_py"] - counts["koza_resolved_total"] = counts["koza_resolved_yaml"] + counts["koza_resolved_transform_py"] - print(json.dumps(counts, indent=2)) + 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(json.dumps(counts, indent=2)) + args.out.write_text(text, encoding="utf-8") if __name__ == "__main__": diff --git a/paper/benchmarks/koza_comparison/loc.json b/paper/benchmarks/koza_comparison/loc.json index 8e8dbd0f..3bdae348 100644 --- a/paper/benchmarks/koza_comparison/loc.json +++ b/paper/benchmarks/koza_comparison/loc.json @@ -1,11 +1,26 @@ { - "tablassert_table_yaml": 29, - "tablassert_graph_wrapper_yaml": 24, - "koza_yaml": 25, - "koza_transform_py": 31, - "koza_resolved_yaml": 25, - "koza_resolved_transform_py": 86, - "tablassert_recipe_total": 53, - "koza_total": 56, - "koza_resolved_total": 111 -} \ No newline at end of file + "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": 31, + "non_whitespace_characters": 883 + }, + "tablassert_total": { + "lines": 56, + "non_whitespace_characters": 1152 + }, + "koza_total": { + "lines": 56, + "non_whitespace_characters": 1275 + } +} diff --git a/paper/benchmarks/koza_comparison/make_10k.py b/paper/benchmarks/koza_comparison/make_10k.py deleted file mode 100644 index 8da78be4..00000000 --- a/paper/benchmarks/koza_comparison/make_10k.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python3 -"""Deterministic 10,000-row generator shared by both koza-comparison runners. - -Replicates tutorial-data.csv to exactly 10,000 data rows (plus header), preserving -row order, so Tablassert and Koza measure the identical input. Same output bytes -on every run (no RNG, no timestamps). -""" - -from __future__ import annotations - -import argparse -from pathlib import Path - -TARGET_ROWS = 10_000 - - -def generate(source: Path, out: Path) -> int: - lines = source.read_text(encoding="utf-8").splitlines() - header, rows = lines[0], [line for line in lines[1:] if line.strip()] - if not rows: - msg = f"{source} has no data rows" - raise ValueError(msg) - data = [header] - while len(data) - 1 < TARGET_ROWS: - data.extend(rows[: min(TARGET_ROWS - (len(data) - 1), len(rows))]) - out.write_text("\n".join(data) + "\n", encoding="utf-8") - return len(data) - 1 - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--source", type=Path, default=Path(__file__).with_name("tutorial-data.csv")) - parser.add_argument("--out", type=Path, default=Path(__file__).with_name("tutorial-data-10k.csv")) - args = parser.parse_args() - rows = generate(args.source, args.out) - print(f"wrote {args.out} with {rows} data rows") - - -if __name__ == "__main__": - main() diff --git a/paper/benchmarks/koza_comparison/measure_koza.py b/paper/benchmarks/koza_comparison/measure_koza.py deleted file mode 100644 index 42957bf6..00000000 --- a/paper/benchmarks/koza_comparison/measure_koza.py +++ /dev/null @@ -1,167 +0,0 @@ -#!/usr/bin/env python3 -"""Measure Tablassert and Koza tutorial workloads with verified outputs. - -Three workloads are reported separately: Tablassert with local fullmap resolution, -Koza mapping without resolution, and Koza with hosted NameResolution followed by -NodeNormalization. Every timing includes a fresh CLI process. Cold runs have no -preceding repeat of that workload; warm runs immediately follow a prior repeat. -Outputs are removed before every run. OS, database, and remote-service caches are -never cleared, so cold does not mean a cold OS or service cache. - -Usage on the build host: - - python make_10k.py - python measure_koza.py --repeats 3 --koza ./koza-venv/bin/koza --out timing.json -""" - -from __future__ import annotations - -import argparse -import csv -import json -import os -import platform -import shutil -import statistics -import subprocess -import time -from collections.abc import Callable -from pathlib import Path -from typing import cast - -HERE = Path(__file__).parent - - -def hardware() -> dict[str, object]: - info: dict[str, object] = {"platform": platform.platform(), "python": platform.python_version(), "cpu_count": os.cpu_count()} - meminfo = Path("/proc/meminfo") - if meminfo.exists(): - for line in meminfo.read_text().splitlines(): - if line.startswith("MemTotal:"): - info["mem_total_gib"] = round(int(line.split()[1]) / 1024 / 1024, 1) - break - return info - - -def clean_outputs(paths: list[Path]) -> None: - for path in paths: - if path.is_dir(): - shutil.rmtree(path) - else: - path.unlink(missing_ok=True) - - -def output_rows(out_dir: Path, pattern: str) -> tuple[int, int]: - files = list(out_dir.glob(pattern)) - return len(files), sum(sum(1 for _ in path.open()) for path in files) - - -def run_tablassert(graph_yaml: Path, out_dir: Path, tablassert_bin: str) -> tuple[float, dict[str, object]]: - start = time.perf_counter() - proc = subprocess.run([tablassert_bin, "build-kg", str(graph_yaml)], capture_output=True, text=True, check=False) - elapsed = time.perf_counter() - start - if proc.returncode != 0: - raise RuntimeError(f"tablassert build-kg exited {proc.returncode}: {proc.stderr[-800:]}") - edge_files, edge_rows = output_rows(out_dir, "*.edges.ndjson") - node_files, node_rows = output_rows(out_dir, "*.nodes.ndjson") - if (edge_files, node_files, edge_rows, node_rows) != (1, 1, 12_500, 9): - raise RuntimeError(f"unexpected Tablassert output: edges={edge_rows}, nodes={node_rows}") - return elapsed, {"edge_rows": edge_rows, "node_rows": node_rows} - - -def run_koza(koza_yaml: Path, out_dir: Path, koza_bin: str, resolved: bool = False) -> tuple[float, dict[str, object]]: - start = time.perf_counter() - proc = subprocess.run([koza_bin, "transform", str(koza_yaml), "-o", str(out_dir)], capture_output=True, text=True, check=False) - elapsed = time.perf_counter() - start - if proc.returncode != 0: - raise RuntimeError(f"koza exited {proc.returncode}: {proc.stderr[-1200:]}") - edge_files, edge_lines = output_rows(out_dir, "*_edges.tsv") - node_files, node_lines = output_rows(out_dir, "*_nodes.tsv") - edge_rows, node_rows = edge_lines - 1, node_lines - 1 - if (edge_files, node_files, edge_rows, node_rows) != (1, 1, 10_000, 20_000): - raise RuntimeError(f"unexpected Koza output: edges={edge_rows}, nodes={node_rows}") - check: dict[str, object] = {"edge_rows": edge_rows, "node_rows": node_rows} - if resolved: - report_path = HERE / "koza-hosted-resolution.json" - report = json.loads(report_path.read_text()) - if report["complete_records"] != report["unique_terms"]: - raise RuntimeError(f"incomplete hosted resolution: {report}") - check["hosted_resolution"] = report - for path in out_dir.glob("*_nodes.tsv"): - with path.open() as stream: - for node in csv.DictReader(stream, delimiter="\t"): - if not all(node.get(field) for field in ("id", "name", "category")) or ":" not in node["id"]: - raise RuntimeError(f"incomplete canonical Koza node: {node}") - return elapsed, check - - -def measure(mode: str, runner: Callable[[], tuple[float, dict[str, object]]], repeats: int, outputs: list[Path]) -> dict[str, object]: - walls: list[float] = [] - checks: list[dict[str, object]] = [] - for index in range(repeats): - clean_outputs(outputs) - elapsed, check = runner() - walls.append(elapsed) - checks.append({"run": index + 1, "wall_s": elapsed, **check}) - return {"mode": mode, "repeats": repeats, "runs": checks, "wall_s": walls, "median_s": statistics.median(walls)} - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--repeats", type=int, default=3) - parser.add_argument("--out", type=Path, default=HERE / "timing.json") - parser.add_argument("--koza-yaml", type=Path, default=HERE / "tutorial-koza.yaml") - parser.add_argument("--koza-resolved-yaml", type=Path, default=HERE / "tutorial-koza-resolved.yaml") - parser.add_argument("--graph-yaml", type=Path, default=HERE / "tutorial-graph.yaml") - parser.add_argument("--workdir", type=Path, default=HERE / "_runs") - parser.add_argument("--tablassert", default="/users/sgoetz/Code/dakp/.venv/bin/tablassert") - parser.add_argument("--koza", default="koza") - parser.add_argument("--skip-hosted-reason", help="record an explicit blocked hosted arm without running it") - args = parser.parse_args() - - os.chdir(HERE) - tab_out = HERE / "tutorial-output" - koza_out = args.workdir / "koza-out" - resolved_out = args.workdir / "koza-resolved-out" - report = HERE / "koza-hosted-resolution.json" - args.workdir.mkdir(parents=True, exist_ok=True) - - result: dict[str, object] = { - "date_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), - "versions": { - "tablassert": subprocess.check_output([args.tablassert, "--version"], text=True).strip(), - "koza": subprocess.check_output([args.koza, "--version"], text=True).strip(), - "python": platform.python_version(), - }, - "hardware": hardware(), - "input_rows": sum(1 for _ in (HERE / "tutorial-data-10k.csv").open()) - 1, - "cold_definition": "fresh CLI process, first repeat of the workload, outputs removed; OS/database/service caches not cleared", - "warm_definition": "fresh CLI process immediately following a repeat, outputs removed; OS/database/service caches not cleared", - "output_equivalence": ( - "Tablassert uses local fullmap ranking and emits 12,500 edges because one input label has a two-CURIE tie. " - "Resolved Koza emits the top hosted candidate and 10,000 edges. Mapping-only Koza emits raw labels. " - "The workloads all perform tabular mapping, but the graphs and matching policies are not equivalent." - ), - } - workloads = { - "tablassert": (lambda: run_tablassert(args.graph_yaml, tab_out, args.tablassert), [tab_out]), - "koza_mapping_only": (lambda: run_koza(args.koza_yaml, koza_out, args.koza), [koza_out]), - "koza_hosted_resolved": (lambda: run_koza(args.koza_resolved_yaml, resolved_out, args.koza, resolved=True), [resolved_out, report]), - } - for label, (runner, outputs) in workloads.items(): - if label == "koza_hosted_resolved" and args.skip_hosted_reason: - result[label] = {"status": "blocked", "reason": args.skip_hosted_reason} - continue - result[f"{label}_cold"] = measure("cold", runner, 1, outputs) - result[f"{label}_warm"] = measure("warm", runner, args.repeats, outputs) - - args.out.write_text(json.dumps(result, indent=2)) - for section in result: - if section.endswith(("_cold", "_warm")): - data = cast("dict[str, object]", result[section]) - print(f"{section}: median {cast('float', data['median_s']):.3f}s") - print(f"wrote {args.out}") - - -if __name__ == "__main__": - main() diff --git a/paper/benchmarks/koza_comparison/test_bench_koza.py b/paper/benchmarks/koza_comparison/test_bench_koza.py index 952e73d3..3510dc85 100644 --- a/paper/benchmarks/koza_comparison/test_bench_koza.py +++ b/paper/benchmarks/koza_comparison/test_bench_koza.py @@ -1,18 +1,13 @@ -"""Offline tests for the koza comparison harness (LOC counting + 10k generator).""" +"""Offline tests for the Koza configuration-size comparison.""" from __future__ import annotations -import importlib.util import sys from pathlib import Path -from types import SimpleNamespace - -import pytest sys.path.insert(0, str(Path(__file__).parent)) -from count_loc import count_python, count_yaml -from make_10k import generate +from count_loc import count_python, count_yaml, measure def test_count_python_excludes_docstrings_blanks_comments(tmp_path: Path) -> None: @@ -21,7 +16,6 @@ def test_count_python_excludes_docstrings_blanks_comments(tmp_path: Path) -> Non '"""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", ) - # code lines: import os, def f():, return 1, x = 2 -> 4 assert count_python(source) == 4 @@ -31,69 +25,7 @@ def test_count_yaml_excludes_blanks_and_comments(tmp_path: Path) -> None: assert count_yaml(data) == 3 -def test_generate_deterministic_10k(tmp_path: Path) -> None: - source = tmp_path / "base.csv" - source.write_text("a,b\n1,2\n3,4\n", encoding="utf-8") - out1, out2 = tmp_path / "o1.csv", tmp_path / "o2.csv" - assert generate(source, out1) == 10_000 - assert generate(source, out2) == 10_000 - assert out1.read_bytes() == out2.read_bytes() - lines = out1.read_text().splitlines() - assert lines[0] == "a,b" - assert len(lines) == 10_001 - # order preserved: row 1 of output is row 1 of source - assert lines[1] == "1,2" - assert lines[-1] == "3,4" - - -def test_generate_rejects_empty_source(tmp_path: Path) -> None: - source = tmp_path / "empty.csv" - source.write_text("a,b\n", encoding="utf-8") - try: - generate(source, tmp_path / "o.csv") - raised = False - except ValueError: - raised = True - assert raised - - -def load_resolved_transform(monkeypatch: pytest.MonkeyPatch): - decorators = SimpleNamespace(transform=lambda: lambda function: function) - monkeypatch.setitem(sys.modules, "koza", decorators) - path = Path(__file__).with_name("tutorial-transform-resolved.py") - spec = importlib.util.spec_from_file_location("tutorial_transform_resolved", path) - assert spec is not None - assert spec.loader is not None - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -def test_koza_hosted_resolution_uses_top_canonical_hit(monkeypatch: pytest.MonkeyPatch) -> None: - module = load_resolved_transform(monkeypatch) - - def fake_lookup(term: str, limit: int, budget: object, only_taxa: str) -> dict[str, object]: - return {"term": term, "http_status": 200, "error": None, "curies": [f"X:{term}"], "latency_s": 0.1} - - def fake_normalize(curies: list[str], budget: object) -> dict[str, object]: - normalized = {curie: {"id": {"identifier": curie, "label": curie}, "type": ["biolink:NamedThing"]} for curie in curies} - return {"normalized": normalized, "http_calls": 1, "error": None} - - monkeypatch.setattr(module, "lookup_one", fake_lookup) - monkeypatch.setattr(module, "normalize_batch", fake_normalize) - records = [{"gene_symbol": "TP53", "disease_name": "lung cancer"}] - resolved, report = module.resolve_labels(records) - assert resolved["TP53"]["id"] == "X:TP53" - assert report["complete_records"] == 2 - assert report["http_calls"] == {"lookup": 2, "get_normalized_nodes": 1} - - -def test_koza_hosted_resolution_rejects_empty_lookup(monkeypatch: pytest.MonkeyPatch) -> None: - module = load_resolved_transform(monkeypatch) - monkeypatch.setattr( - module, "lookup_one", lambda term, limit, budget, only_taxa: {"term": term, "http_status": 200, "error": None, "curies": [], "latency_s": 0.1} - ) - monkeypatch.setattr(module, "normalize_batch", lambda curies, budget: {"normalized": {}, "http_calls": 0, "error": None}) - with pytest.raises(RuntimeError, match="no candidates"): - module.resolve_labels([{"gene_symbol": "unknown", "disease_name": "unknown"}]) +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} diff --git a/paper/benchmarks/koza_comparison/timing.json b/paper/benchmarks/koza_comparison/timing.json deleted file mode 100644 index 87cfab3f..00000000 --- a/paper/benchmarks/koza_comparison/timing.json +++ /dev/null @@ -1,114 +0,0 @@ -{ - "date_utc": "2026-10-29T21:28:55Z", - "versions": { - "tablassert": "tablassert 19.7.0", - "koza": "Koza version: 2.7.0", - "python": "3.12.3" - }, - "hardware": { - "platform": "Linux-6.8.0-142-generic-x86_64-with-glibc2.39", - "python": "3.12.3", - "cpu_count": 80, - "mem_total_gib": 187.5 - }, - "input_rows": 10000, - "cold_definition": "fresh CLI process, first repeat of the workload, outputs removed; OS/database/service caches not cleared", - "warm_definition": "fresh CLI process immediately following a repeat, outputs removed; OS/database/service caches not cleared", - "output_equivalence": "Tablassert uses local fullmap ranking and emits 12,500 edges because one input label has a two-CURIE tie. Resolved Koza emits the top hosted candidate and 10,000 edges. Mapping-only Koza emits raw labels. The workloads all perform tabular mapping, but the graphs and matching policies are not equivalent.", - "tablassert_cold": { - "mode": "cold", - "repeats": 1, - "runs": [ - { - "run": 1, - "wall_s": 6.719237510114908, - "edge_rows": 12500, - "node_rows": 9 - } - ], - "wall_s": [ - 6.719237510114908 - ], - "median_s": 6.719237510114908 - }, - "tablassert_warm": { - "mode": "warm", - "repeats": 3, - "runs": [ - { - "run": 1, - "wall_s": 6.726083653979003, - "edge_rows": 12500, - "node_rows": 9 - }, - { - "run": 2, - "wall_s": 6.727087490726262, - "edge_rows": 12500, - "node_rows": 9 - }, - { - "run": 3, - "wall_s": 6.749565453734249, - "edge_rows": 12500, - "node_rows": 9 - } - ], - "wall_s": [ - 6.726083653979003, - 6.727087490726262, - 6.749565453734249 - ], - "median_s": 6.727087490726262 - }, - "koza_mapping_only_cold": { - "mode": "cold", - "repeats": 1, - "runs": [ - { - "run": 1, - "wall_s": 5.381368623115122, - "edge_rows": 10000, - "node_rows": 20000 - } - ], - "wall_s": [ - 5.381368623115122 - ], - "median_s": 5.381368623115122 - }, - "koza_mapping_only_warm": { - "mode": "warm", - "repeats": 3, - "runs": [ - { - "run": 1, - "wall_s": 5.368373390752822, - "edge_rows": 10000, - "node_rows": 20000 - }, - { - "run": 2, - "wall_s": 5.381817974150181, - "edge_rows": 10000, - "node_rows": 20000 - }, - { - "run": 3, - "wall_s": 5.381799325812608, - "edge_rows": 10000, - "node_rows": 20000 - } - ], - "wall_s": [ - 5.368373390752822, - 5.381817974150181, - 5.381799325812608 - ], - "median_s": 5.381799325812608 - }, - "koza_hosted_resolved": { - "status": "blocked", - "reason": "2026-10-29: NameResolution DNS lookup times out; independent Cloudflare query returns SERVFAIL with No Reachable Authority at renci.org. Hosted Koza probes failed before producing records." - } -} \ No newline at end of file diff --git a/paper/benchmarks/koza_comparison/tutorial-data-10k.csv b/paper/benchmarks/koza_comparison/tutorial-data-10k.csv deleted file mode 100644 index c7dff09d..00000000 --- a/paper/benchmarks/koza_comparison/tutorial-data-10k.csv +++ /dev/null @@ -1,10001 +0,0 @@ -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 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 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-EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic 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cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -TP53,lung cancer,0.001,450 -BRCA1,breast cancer,0.0001,1200 -EGFR,colorectal cancer,0.005,680 -KRAS,pancreatic cancer,0.002,320 -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-koza-resolved.yaml b/paper/benchmarks/koza_comparison/tutorial-koza-resolved.yaml deleted file mode 100644 index f52229c0..00000000 --- a/paper/benchmarks/koza_comparison/tutorial-koza-resolved.yaml +++ /dev/null @@ -1,29 +0,0 @@ -name: tutorial-koza-resolved - -reader: - files: - - ./tutorial-data-10k.csv - format: csv - delimiter: "," - -transform: - code: ./tutorial-transform-resolved.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 with hosted resolution) - description: Same mapping with hosted NameResolution and NodeNormalization. diff --git a/paper/benchmarks/koza_comparison/tutorial-koza.yaml b/paper/benchmarks/koza_comparison/tutorial-koza.yaml index 8cb939d0..bf3c8b3f 100644 --- a/paper/benchmarks/koza_comparison/tutorial-koza.yaml +++ b/paper/benchmarks/koza_comparison/tutorial-koza.yaml @@ -2,7 +2,7 @@ name: tutorial-koza reader: files: - - ./tutorial-data-10k.csv + - ./tutorial-data.csv format: csv delimiter: "," diff --git a/paper/benchmarks/koza_comparison/tutorial-table.yaml b/paper/benchmarks/koza_comparison/tutorial-table.yaml index de785905..d6de616f 100644 --- a/paper/benchmarks/koza_comparison/tutorial-table.yaml +++ b/paper/benchmarks/koza_comparison/tutorial-table.yaml @@ -1,9 +1,12 @@ template: source: kind: text - local: ./tutorial-data-10k.csv + local: ./tutorial-data.csv url: - https://example.com/data.csv + row_slice: + - 1 + - auto delimiter: "," statement: subject: diff --git a/paper/benchmarks/koza_comparison/tutorial-transform-resolved.py b/paper/benchmarks/koza_comparison/tutorial-transform-resolved.py deleted file mode 100644 index 12153dad..00000000 --- a/paper/benchmarks/koza_comparison/tutorial-transform-resolved.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Koza 2.7.0 transform with hosted label resolution and normalization. - -Uses the same NameResolution then NodeNormalization client as the standalone -hosted benchmark. Raw labels require both endpoints: NodeNormalization accepts -CURIEs, not labels. Only the top-ranked candidate is emitted. Unresolved terms, -request failures, and incomplete canonical records fail the run. -""" - -import json -import sys -import time -from concurrent.futures import ThreadPoolExecutor -from dataclasses import dataclass, field -from pathlib import Path - -import koza - -sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "name_resolution")) -from bench_name_res import RequestBudget, canonical_record, group_normalized_by_requested, lookup_one, normalize_batch - -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_labels(records: list[dict[str, str]]) -> tuple[dict[str, dict[str, object]], dict[str, object]]: - terms = sorted({record[column] for record in records for column in ("gene_symbol", "disease_name")}) - started = time.perf_counter() - budget = RequestBudget(len(terms) + 20) - only_taxa = "NCBITaxon:9606" - with ThreadPoolExecutor(max_workers=4) as pool: - lookups = list(pool.map(lambda term: lookup_one(term, 5, budget, only_taxa=only_taxa), terms)) - lookup_attempts = len(terms) + 20 - budget.remaining - normalization_attempts_start = budget.remaining - curies = sorted({curie for result in lookups for curie in result["curies"]}) - normalization = normalize_batch(curies, budget) - if normalization["error"]: - raise RuntimeError(normalization["error"]) - grouped = group_normalized_by_requested(curies, normalization["normalized"]) - resolved: dict[str, dict[str, object]] = {} - for result in lookups: - if result["error"] or not result["curies"]: - raise RuntimeError(f"hosted lookup failed for {result['term']!r}: {result['error'] or 'no candidates'}") - record: dict[str, object] | None = canonical_record(grouped.get(result["curies"][0])) - if record is None: - raise RuntimeError(f"incomplete top-ranked canonical record for {result['term']!r}") - resolved[result["term"]] = record - report: dict[str, object] = { - "input_rows": len(records), - "unique_terms": len(terms), - "complete_records": len(resolved), - "candidate_curies": len(curies), - "http_calls": {"lookup": len(lookups), "get_normalized_nodes": normalization["http_calls"]}, - "http_attempts_including_retries": {"lookup": lookup_attempts, "get_normalized_nodes": normalization_attempts_start - budget.remaining}, - "resolution_wall_s": time.perf_counter() - started, - "lookup_limit": 5, - "lookup_concurrency": 4, - "only_taxa": only_taxa, - "selection": "top-ranked NameResolution candidate; no ties emitted", - } - return resolved, report - - -def categories(record: dict[str, object]) -> list[str]: - values = record["categories"] - if not isinstance(values, list): - raise TypeError("canonical categories must be a list") - return [str(value) for value in values] - - -@koza.transform() -def transform_rows(koza, data): - records = list(data) - resolved, report = resolve_labels(records) - Path("koza-hosted-resolution.json").write_text(json.dumps(report, indent=2)) - for record in records: - subject = resolved[record["gene_symbol"]] - obj = resolved[record["disease_name"]] - koza.write( - NamedThing(id=str(subject["id"]), name=str(subject["preferred_name"]), category=categories(subject)), - NamedThing(id=str(obj["id"]), name=str(obj["preferred_name"]), category=categories(obj)), - Association( - subject=str(subject["id"]), - predicate=ASSOCIATION, - object=str(obj["id"]), - 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/koza_comparison/tutorial-transform.py b/paper/benchmarks/koza_comparison/tutorial-transform.py index df9df2af..bd0037ff 100644 --- a/paper/benchmarks/koza_comparison/tutorial-transform.py +++ b/paper/benchmarks/koza_comparison/tutorial-transform.py @@ -1,8 +1,8 @@ """Koza 2.7.0 transform equivalent of the Tablassert tutorial example. Emits one associated_with edge per row plus its two nodes. Entity resolution is -NOT included in this mapping-only arm. The companion resolved transform adds -hosted NameResolution and NodeNormalization (see README). +NOT included in this mapping-only example. +Koza mapping tables or custom code would be required. """ from dataclasses import dataclass, field diff --git a/paper/paper.md b/paper/paper.md index 53cf5b83..e7b4bfce 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -140,11 +140,11 @@ for normalization, resolution, database construction, and NDJSON writing. GIL-re regions use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel execution even on GIL-enabled Python 3.11, without requiring free-threaded Python. This removes per-term Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. -The tutorial uses 29 recipe lines plus a 24-line graph wrapper; Koza's mapping-only version uses -25 YAML and 31 Python lines, excluding comments and docstrings. Koza supports custom hosted -resolution, avoiding local storage but adding code and service dependencies. Tablassert -integrates resolution but requires the fullmap. Its retained ties differ from the hosted -transform's top-hit policy, precluding identical-graph runtime claims. +In the tutorial, Tablassert uses a 32-line table recipe and a 24-line graph wrapper (1,152 +non-whitespace characters). A mapping-only Koza example uses 25 YAML and 31 Python lines (1,275 +characters), excluding blank lines, comments, and docstrings. Line counts tie; Tablassert also +configures resolution declaratively. The Koza example omits resolution, +so this compares configuration size, not equivalent workflows. # Research impact statement From f14eb351ae27650e6a2405bd4399e486e7d78aa7 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 12:29:59 -0700 Subject: [PATCH 18/25] docs(paper): compare Koza using identical Tablassert resolution --- paper/benchmarks/README.md | 3 +- paper/benchmarks/koza_comparison/README.md | 67 +++++++++++++++---- .../koza_comparison/compare_outputs.py | 66 ++++++++++++++++++ paper/benchmarks/koza_comparison/loc.json | 8 +-- .../koza_comparison/output-comparison.json | 62 +++++++++++++++++ .../koza_comparison/test_bench_koza.py | 29 ++++++++ .../koza_comparison/tutorial-transform.py | 54 ++++++++++----- paper/paper.md | 10 +-- 8 files changed, 258 insertions(+), 41 deletions(-) create mode 100644 paper/benchmarks/koza_comparison/compare_outputs.py create mode 100644 paper/benchmarks/koza_comparison/output-comparison.json diff --git a/paper/benchmarks/README.md b/paper/benchmarks/README.md index 1d58186e..0db302a6 100644 --- a/paper/benchmarks/README.md +++ b/paper/benchmarks/README.md @@ -8,7 +8,8 @@ scripts, inputs, and recorded results with the environment they were measured in `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 mapping-only Koza transform. No runtime is + 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 diff --git a/paper/benchmarks/koza_comparison/README.md b/paper/benchmarks/koza_comparison/README.md index dd42198e..368c1e11 100644 --- a/paper/benchmarks/koza_comparison/README.md +++ b/paper/benchmarks/koza_comparison/README.md @@ -1,18 +1,19 @@ # Koza comparison: configuration size and verbosity -This directory compares the size of two mappings for the same four-row tutorial -CSV (`tutorial-data.csv`). It does not benchmark runtime. +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` is the separate graph wrapper with fullmap - and RIG metadata. -- Koza: `tutorial-koza.yaml` configures input and output, and - `tutorial-transform.py` implements row-to-node and row-to-edge mapping. + 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 emits raw labels as node identifiers. It does not implement entity -resolution, whereas the Tablassert recipe uses fullmap resolution. The comparison -therefore shows where mapping logic is expressed and how much text each example -requires; it is not an equivalent end-to-end workflow or general complexity metric. +The Koza example intentionally depends on Tablassert for identical resolution; +this is not an independent resolver comparison or a benchmark of Koza alone. ## Counting @@ -21,8 +22,48 @@ python count_loc.py --out loc.json ``` `count_loc.py` excludes blank lines, comment-only lines, and Python docstrings. -Verbosity is the number of non-whitespace characters on the counted lines, so -indentation style does not affect it. The Tablassert table recipe and graph wrapper -are reported separately and as a total. +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/loc.json b/paper/benchmarks/koza_comparison/loc.json index 3bdae348..6be1700f 100644 --- a/paper/benchmarks/koza_comparison/loc.json +++ b/paper/benchmarks/koza_comparison/loc.json @@ -12,15 +12,15 @@ "non_whitespace_characters": 392 }, "koza_transform_py": { - "lines": 31, - "non_whitespace_characters": 883 + "lines": 47, + "non_whitespace_characters": 1589 }, "tablassert_total": { "lines": 56, "non_whitespace_characters": 1152 }, "koza_total": { - "lines": 56, - "non_whitespace_characters": 1275 + "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 index 3510dc85..9ac72c12 100644 --- a/paper/benchmarks/koza_comparison/test_bench_koza.py +++ b/paper/benchmarks/koza_comparison/test_bench_koza.py @@ -2,11 +2,15 @@ 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 @@ -29,3 +33,28 @@ 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-transform.py b/paper/benchmarks/koza_comparison/tutorial-transform.py index bd0037ff..e4f4ca94 100644 --- a/paper/benchmarks/koza_comparison/tutorial-transform.py +++ b/paper/benchmarks/koza_comparison/tutorial-transform.py @@ -1,14 +1,18 @@ -"""Koza 2.7.0 transform equivalent of the Tablassert tutorial example. +"""Koza 2.7.0 transform for the Tablassert tutorial table. -Emits one associated_with edge per row plus its two nodes. Entity resolution is -NOT included in this mapping-only example. -Koza mapping tables or custom code would be required. +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" @@ -30,17 +34,31 @@ class Association: sample_size: int | None = None -@koza.transform_record() -def transform_row(koza, record): - subject = NamedThing(id=record["gene_symbol"], name=record["gene_symbol"], category=["biolink:Gene"]) - obj = NamedThing(id=record["disease_name"], name=record["disease_name"], category=["biolink:Disease"]) - edge = Association( - subject=record["gene_symbol"], - predicate=ASSOCIATION, - object=record["disease_name"], - primary_knowledge_source="infores:tutorial-kg", - publications=["PMID:12345678"], - p_value=float(record["p_value"]), - sample_size=int(record["sample_size"]), - ) - koza.write(subject, obj, edge) +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/paper.md b/paper/paper.md index e7b4bfce..b2c4a191 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -140,11 +140,11 @@ for normalization, resolution, database construction, and NDJSON writing. GIL-re regions use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel execution even on GIL-enabled Python 3.11, without requiring free-threaded Python. This removes per-term Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. -In the tutorial, Tablassert uses a 32-line table recipe and a 24-line graph wrapper (1,152 -non-whitespace characters). A mapping-only Koza example uses 25 YAML and 31 Python lines (1,275 -characters), excluding blank lines, comments, and docstrings. Line counts tie; Tablassert also -configures resolution declaratively. The Koza example omits resolution, -so this compares configuration size, not equivalent workflows. +The tutorial uses 56 Tablassert YAML lines versus 72 Koza YAML/Python lines (1,152 versus 1,981 +non-whitespace characters), excluding blanks, comments, and docstrings. Koza calls Tablassert's +API with identical resolution settings: both produce five edge pairs and nine matching nodes. +Tablassert additionally emits a Resource Ingest Guide, deduplicated nodes, structured provenance, +and supporting-study metadata. This compares verbosity, not runtime or capability. # Research impact statement From 6e8b8becf2029f271bfe5f27c68adb8ba5786284 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 12:43:27 -0700 Subject: [PATCH 19/25] docs(paper): clarify benchmark comparisons and supporting citations --- paper/paper.bib | 15 ++++++ paper/paper.md | 138 ++++++++++++++++++++++++++---------------------- 2 files changed, 90 insertions(+), 63 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 36c78342..7542813d 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -117,6 +117,21 @@ @misc{nameres 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}, diff --git a/paper/paper.md b/paper/paper.md index b2c4a191..c2a44e73 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -57,8 +57,7 @@ pairs YAML configuration with semi-declarative Python transforms and outputs KGX resolution supplied through mappings or additional code. LinkML [@linkml] provides schema modeling and validation rather than Tablassert's integrated resolution pipeline. BioCypher [@biocypher] standardizes biomedical graph construction; OntoWeaver [@ontoweaver] provides -declarative tabular mappings for BioCypher. These are related approaches, not inherently -unsuitable for research tables. SPIRES [@spires] extracts knowledge from free text by zero-shot +declarative tabular mappings for BioCypher. These are related approaches for research tables. SPIRES [@spires] extracts knowledge from free text by zero-shot prompting, while Tablassert's sources are already structured tables. Finally, the hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier resources through a network API. Hosted builds depend on service availability; reproducing historical results requires @@ -70,31 +69,31 @@ recipe-driven pipeline. # Software design Tablassert's core trade-off is up-front work for build-time ease: a large entity-resolution -database is built (or downloaded) once, and every subsequent build resolves millions of terms -locally in batched lookups without service calls. Recipes are compiled 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 sharing an instruction prefix reuse its computed snapshot. +database is built or downloaded once, then subsequent builds resolve terms locally in batches +without service calls. Recipes are compiled 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 +sharing an instruction prefix reuse its computed snapshot. The embedded fullmap database underpins the design. Built from BABEL [@babel] exports, it maps -normalized synonyms, including the meta-identifiers that unify the same concept across source -vocabularies, onto canonical identifiers with category and taxon metadata. Level-one -normalization trims whitespace and quotes, Unicode-lowercases text, Porter2-stems ASCII -alphabetic tokens, then deduplicates, sorts, and joins tokens; level two additionally removes -non-word characters. Identical normalization is applied to synonyms and query text. The store -itself is a set of redb [@redb] key-value files -- an embedded, crash-safe Rust engine chosen -over analytical stores because resolution is point-lookup-shaped, not query-shaped -- built in a -parallel, memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 -export is 44.5 GB compressed; the redb store used here, built from the 2026-07-22 export, -occupies 92.9 GB across one primary and 16 shard files. What does not map is reported, not -guessed, and metadata arrives with the identifier. Resolution is exact-match over normalized -keys rather than fuzzy search -- approximate matches surface only in the audit stage, not in the -graph -- to avoid silently accepting plausible but incorrect approximate matches. To keep the -database smaller and query times faster, the build applies a fixed allowlist of the top 100 -experimental taxa derived from Translator knowledge-graph node frequencies, alongside all +normalized synonyms and cross-vocabulary meta-identifiers onto canonical identifiers with +category and taxon metadata. Level-one normalization trims whitespace and quotes, +Unicode-lowercases text, Porter2-stems ASCII alphabetic tokens, then deduplicates, sorts, and +joins tokens; level two additionally removes non-word characters. + +Identical normalization is applied to synonyms and query text. The store uses redb [@redb], an +embedded, crash-safe Rust key-value engine suited to point lookups, built in a parallel, +memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 +GB compressed; the redb store used here, built from the 2026-07-22 export, occupies 92.9 GB +across one primary and 16 shard files. Resolution is exact-match over normalized keys rather +than fuzzy search; approximate matches surface only in the audit stage, not in the graph. To +keep the database smaller and query times faster, the build applies a fixed allowlist of the top +100 experimental taxa derived from Translator knowledge-graph node frequencies, alongside all OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some rare species-specific identifiers outside the allowlist. +Resolution constraints + Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species while retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also @@ -104,50 +103,64 @@ level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multip category bonus of 1 for prioritized categories and 50 otherwise -- then deduplicates them to the best tier, retaining distinct CURIEs that tie. When column context is on, category frequency across the column being resolved helps break remaining ties; this is a heuristic, not a -guarantee of semantic correctness. Hosted NameResolution also supports bulk lookup, taxon and -Biolink-type filters, and CURIE prefix inclusion/exclusion. Fullmap additionally integrates -per-column category-ranking policies, regex exclusions, and column-context ranking. Hosted -candidates can be canonicalized through batched Node Normalization requests; two network stages -need not mean two requests per term. On a dual-socket Intel Xeon Gold 6230 host (40 physical -cores, 80 hardware threads, 187 GiB RAM, SSD RAID1, Ubuntu 24.04), 113 hand-selected ingest -entries comprised 104 unique terms. Three fresh-process local batch passes took a median 0.886 -seconds, excluding interpreter startup; three in-process passes took a median 0.516 seconds. OS -caches were not cleared. Local resolution returned complete identifier, name, and category -fields for 93 terms. One hosted sweep returned complete top-ranked canonical records for 99 -terms in 50.23 seconds: 104 concurrent-limited lookups plus five batched normalization requests, -with no request errors. Matching policies and resource versions differ; this convenience sample -measures neither accuracy nor general service performance. +guarantee of semantic correctness. + +Hosted NameResolution [@nameres], a separate SRI service, supports bulk lookup, taxon and +Biolink-type filters, and CURIE prefix inclusion/exclusion. Candidates from that separate +service can be canonicalized through SRI's separate Node Normalization service [@nodenorm] using +batched requests; two network stages need not mean two requests per term. Edges carry recipe-declared provenance, including source URLs and publications when provided; traceability depends on the source and configuration. Optional quality control audits each resolved name with a four-stage cascade: exact or alias match, fuzzy string similarity, -Schwartz-Hearst abbreviation expansion, then SapBERT [@sapbert] embedding similarity using a -checkpoint fine-tuned on BABEL synonyms +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 handling only what cheaper stages could not clear. Suspect mappings are dropped and logged with their scores -rather than silently retained. For example, a DAKP FAERS audit (2026-10-22) 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]. This is one rejection, not measured QC precision or recall. Structural -validation is separate from scientific correctness: an optional study stage asserts structural -invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required fields, no -edges demoted to bare Association). Like KGX's schema-driven `validate` [@kgx], `validate-kgx` -checks graph records; it constructs records using category-specific Biolink classes, -knowledge-source identity is checked against the Translator infores registry offline, and -`--prune` removes edges failing its checks in place, with the exit code gating the post-prune -state. Passing these checks does not establish biological validity. Tablassert uses PyO3 Rust -for normalization, resolution, database construction, and NDJSON writing. GIL-released Rust -regions use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel -execution even on GIL-enabled Python 3.11, without requiring free-threaded Python. This removes -per-term Python callbacks and serial lookup bottlenecks; recipes and validation remain Python. -The tutorial uses 56 Tablassert YAML lines versus 72 Koza YAML/Python lines (1,152 versus 1,981 -non-whitespace characters), excluding blanks, comments, and docstrings. Koza calls Tablassert's -API with identical resolution settings: both produce five edge pairs and nine matching nodes. -Tablassert additionally emits a Resource Ingest Guide, deduplicated nodes, structured provenance, -and supporting-study metadata. This compares verbosity, not runtime or capability. +rather than silently retained. + +For example, a DAKP FAERS audit (2026-10-22) 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]. This is one +rejection, not measured QC precision or recall. + +Structural validation is separate from scientific correctness: an optional study stage asserts +structural invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required +fields, no edges demoted to bare Association). Like KGX's schema-driven `validate` [@kgx], +`validate-kgx` checks graph records; it constructs records using category-specific Biolink +classes, knowledge-source identity is checked against the Translator infores registry offline, +and `--prune` removes edges failing its checks in place, with the exit code gating the +post-prune state. Passing these checks does not establish biological validity. + +Tablassert uses PyO3 Rust for normalization, resolution, database construction, and NDJSON +writing. GIL-released Rust regions use Rayon workers for CPU-heavy normalization and sharded +lookups, enabling parallel execution even on GIL-enabled Python 3.11, without requiring +free-threaded Python. +recipes and validation use Python. + +The tutorial uses 56 Tablassert YAML lines versus 72 Koza [@koza] YAML/Python lines (1,152 +versus 1,981 non-whitespace characters), excluding blanks, comments, and docstrings. Koza calls +Tablassert's API with identical resolution settings: both produce five edge pairs and nine +matching nodes. Tablassert additionally emits a Resource Ingest Guide, deduplicated nodes, +structured provenance, and supporting-study metadata. This compares verbosity, not runtime or +capability. # Research impact statement +Benchmarks clarify deployment: local fullmap needs substantial storage but no service calls; +the separate hosted services avoid local storage but add network and availability dependencies. On a dual-socket Intel Xeon Gold 6230 host (40 +physical cores, 80 hardware threads, 187 GiB RAM, SSD RAID1, Ubuntu 24.04), 113 hand-selected +ingest entries comprised 104 unique terms. Three fresh-process local batch passes took a median of +0.886 seconds, excluding interpreter startup; three in-process passes took a median 0.516 +seconds. OS caches were not cleared. Tablassert's local resolution returned complete identifier, +name, and category fields for 93 terms. The alternative hosted workflow used SRI NameResolution +[@nameres] for candidate lookup followed by the separate SRI Node Normalization service +[@nodenorm] for canonical identifiers, preferred names, and categories. One sweep returned +complete top-ranked canonical records for 99 terms in 50.23 seconds: 104 lookups with four +concurrent requests plus five batched normalization requests, with no request errors. Matching +policies and resource versions differ; this convenience sample measures neither accuracy nor +general service performance. + Tablassert and its declarative precursor underlie several production knowledge graphs. The published MicrobiomeKG [@microbiomekg], a Translator-deployed graph built from supplementary tables of 40 publications, was produced with an early unnamed precursor; the deployed Microbiome @@ -168,8 +181,8 @@ Tablassert is in preparation. Generative AI assisted with code generation, test writing, documentation, and drafting and revising this manuscript, primarily through a customized Pi coding agent (version 0.81.1). AI -spelling and grammar checks covered documentation and manuscript, including Grammarly for the -manuscript. Coding-agent use began on 27 January 2026 (`50f29e8`); co-author credits record +spelling and grammar checks covered documentation and the manuscript, including Grammarly for +the manuscript. Coding-agent use began on 27 January 2026 (`50f29e8`); co-author credits record Claude Sonnet 4.5 and Opus 4.5/4.6 (context variants grouped). Initial versions used no coding agents. @@ -196,7 +209,7 @@ manuscript. Skye Lane Goetz developed the software, led design, and performed analyses. Jared C. Roach and Gwênlyn Glusman helped design the software and analyses and provided detailed project feedback. -All authors will write, revise, and proofread. +All authors wrote, revised, and proofread the manuscript. # Acknowledgements @@ -206,8 +219,7 @@ Awards OT2TR003443, OT2TR003428, and OT2TR005706). Any opinions expressed in thi 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. -The funders provided financial support but had no role in software design, analysis, or -manuscript preparation. Consortium authorship requirements were clarified through committee -consultation. +NCATS provided financial support but had no role in software design, analysis, or manuscript +preparation. Consortium authorship requirements were clarified through committee consultation. # References From 55ffc8cbaf1c54f7e81c3cd6c0c82bc81d22200c Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 13:07:43 -0700 Subject: [PATCH 20/25] docs(paper): clarify declarative validation and comparisons --- paper/paper.md | 118 +++++++++++++++++++++++++------------------------ 1 file changed, 60 insertions(+), 58 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index c2a44e73..3c0cb52f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'Tablassert: Declarative Construction of NCATS Translator Knowledge Graphs from Biomedical Tables' +title: 'Tablassert: Declarative Extraction of NCATS Translator Complicate Knowledge Graphs from Biomedical Tables' tags: - knowledge graph - KGX @@ -44,9 +44,7 @@ Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabu sources and need rigorously structured output without maintaining a transform pipeline per source. Extending beyond journal supplements, the same recipes apply to any relational source -- trial registries, drug approval records, or a researcher's own tables -- and each build can emit -the Resource Ingest Guide metadata required for integration with Translator. Recipes are -validated before a build starts, so configuration errors fail early with field-specific -messages. +the Resource Ingest Guide metadata required for integration with Translator. # State of the field @@ -57,11 +55,11 @@ pairs YAML configuration with semi-declarative Python transforms and outputs KGX resolution supplied through mappings or additional code. LinkML [@linkml] provides schema modeling and validation rather than Tablassert's integrated resolution pipeline. BioCypher [@biocypher] standardizes biomedical graph construction; OntoWeaver [@ontoweaver] provides -declarative tabular mappings for BioCypher. These are related approaches for research tables. SPIRES [@spires] extracts knowledge from free text by zero-shot -prompting, while Tablassert's sources are already structured tables. Finally, the hosted SRI -name resolution service [@nameres] serves BABEL [@babel] identifier resources through a network -API. Hosted builds depend on service availability; reproducing historical results requires -preserving responses or pinning the underlying resources. +declarative tabular mappings for BioCypher. sPIRES [@spires] extracts knowledge from free text +by zero-shot prompting, while Tablassert's sources are already structured tables. Finally, the +hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier resources through +a network API. Hosted builds depend on service availability; reproducing historical results +requires preserving responses or pinning the underlying resources. Tablassert composes declarative mapping, local resolution, and mapping-quality checks into one recipe-driven pipeline. @@ -75,6 +73,13 @@ arguments) operations, executed through Polars lazy frames. Independent transfor until batched resolution, quality control, or output writing requires materialization. Sections sharing an instruction prefix reuse its computed snapshot. +Pydantic models validate every recipe before execution with field-specific errors. Accepted YAML +satisfies the configuration schema, and successful builds produce correctly formatted KGX; this +guarantees structure, not biological correctness. YAML requires fewer changes than +source-specific code, helping nontechnical contributors and AI coding agents author graphs. This +trades granular control and source-specific optimizations for time savings and broader +participation. + The embedded fullmap database underpins the design. Built from BABEL [@babel] exports, it maps normalized synonyms and cross-vocabulary meta-identifiers onto canonical identifiers with category and taxon metadata. Level-one normalization trims whitespace and quotes, @@ -85,12 +90,12 @@ Identical normalization is applied to synonyms and query text. The store uses re embedded, crash-safe Rust key-value engine suited to point lookups, built in a parallel, memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 GB compressed; the redb store used here, built from the 2026-07-22 export, occupies 92.9 GB -across one primary and 16 shard files. Resolution is exact-match over normalized keys rather -than fuzzy search; approximate matches surface only in the audit stage, not in the graph. To -keep the database smaller and query times faster, the build applies a fixed allowlist of the top -100 experimental taxa derived from Translator knowledge-graph node frequencies, alongside all -OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some rare -species-specific identifiers outside the allowlist. +across one primary and 16 shard database files. Resolution is exact-match over normalized keys +rather than fuzzy search; approximate matches surface only in the audit stage, not in the graph. +To keep the database smaller and query times faster, the build applies a fixed allowlist of the +top 100 experimental taxa derived from Translator knowledge-graph node frequencies, alongside +all OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some +rare species-specific identifiers outside the allowlist. Resolution constraints @@ -105,11 +110,6 @@ best tier, retaining distinct CURIEs that tie. When column context is on, catego across the column being resolved helps break remaining ties; this is a heuristic, not a guarantee of semantic correctness. -Hosted NameResolution [@nameres], a separate SRI service, supports bulk lookup, taxon and -Biolink-type filters, and CURIE prefix inclusion/exclusion. Candidates from that separate -service can be canonicalized through SRI's separate Node Normalization service [@nodenorm] using -batched requests; two network stages need not mean two requests per term. - Edges carry recipe-declared provenance, including source URLs and publications when provided; traceability depends on the source and configuration. Optional quality control audits each resolved name with a four-stage cascade: exact or alias match, fuzzy string similarity, @@ -135,47 +135,49 @@ post-prune state. Passing these checks does not establish biological validity. Tablassert uses PyO3 Rust for normalization, resolution, database construction, and NDJSON writing. GIL-released Rust regions use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel execution even on GIL-enabled Python 3.11, without requiring -free-threaded Python. -recipes and validation use Python. +free-threaded Python. recipes and validation use Python. + +## Comparisons + +Hosted NameResolution [@nameres], a separate SRI service, supports bulk lookup, taxon and +Biolink-type filters, and CURIE prefix inclusion/exclusion. Candidates from that separate +service can be canonicalized through SRI's separate Node Normalization service [@nodenorm] using +batched requests; two network stages need not mean two requests per term. -The tutorial uses 56 Tablassert YAML lines versus 72 Koza [@koza] YAML/Python lines (1,152 -versus 1,981 non-whitespace characters), excluding blanks, comments, and docstrings. Koza calls -Tablassert's API with identical resolution settings: both produce five edge pairs and nine -matching nodes. Tablassert additionally emits a Resource Ingest Guide, deduplicated nodes, -structured provenance, and supporting-study metadata. This compares verbosity, not runtime or -capability. +Benchmarks clarify deployment: local fullmap needs substantial storage but no service calls; the +separate hosted services avoid local storage but add network and availability dependencies. On a +dual-socket Intel Xeon Gold 6230 host (40 physical cores, 80 hardware threads, 187 GiB RAM, SSD +RAID1, Ubuntu 24.04), 113 hand-selected ingest entries comprised 104 unique terms. Three +fresh-process local batch passes took a median of 0.886 seconds, excluding interpreter startup; +three in-process passes took a median 0.516 seconds. OS caches were not cleared. Tablassert's +local resolution returned complete identifier, name, and category fields for 93 terms. The +alternative hosted workflow used SRI NameResolution [@nameres] for candidate lookup followed by +the separate SRI Node Normalization service [@nodenorm] for canonical identifiers, preferred +names, and categories. One sweep returned complete top-ranked canonical records for 99 terms in +50.23 seconds: 104 lookups with four concurrent requests plus five batched normalization +requests, with no request errors. Matching policies and resource versions differ; this +convenience sample measures neither accuracy nor general service performance. + +The Tablassert tutorial uses 56 Tablassert YAML lines of code (LOC) versus 72 Koza [@koza] +YAML/Python LOC (1,152 versus 1,981 non-whitespace characters), excluding blanks, comments, and +docstrings. Koza calls Tablassert's API with identical resolution settings: both produce five +edge pairs and nine matching nodes. Tablassert additionally emits a Resource Ingest Guide, +deduplicated nodes, structured provenance, and supporting-study metadata. This compares +verbosity, not runtime or capability. # Research impact statement -Benchmarks clarify deployment: local fullmap needs substantial storage but no service calls; -the separate hosted services avoid local storage but add network and availability dependencies. On a dual-socket Intel Xeon Gold 6230 host (40 -physical cores, 80 hardware threads, 187 GiB RAM, SSD RAID1, Ubuntu 24.04), 113 hand-selected -ingest entries comprised 104 unique terms. Three fresh-process local batch passes took a median of -0.886 seconds, excluding interpreter startup; three in-process passes took a median 0.516 -seconds. OS caches were not cleared. Tablassert's local resolution returned complete identifier, -name, and category fields for 93 terms. The alternative hosted workflow used SRI NameResolution -[@nameres] for candidate lookup followed by the separate SRI Node Normalization service -[@nodenorm] for canonical identifiers, preferred names, and categories. One sweep returned -complete top-ranked canonical records for 99 terms in 50.23 seconds: 104 lookups with four -concurrent requests plus five batched normalization requests, with no request errors. Matching -policies and resource versions differ; this convenience sample measures neither accuracy nor -general service performance. - -Tablassert and its declarative precursor underlie several production knowledge graphs. The -published MicrobiomeKG [@microbiomekg], a Translator-deployed graph built from supplementary -tables of 40 publications, was produced with an early unnamed precursor; the deployed Microbiome -and Multiomics knowledge graphs [@multiomics-kp] and their maintenance builds use present-day -Tablassert. Multiomics 3.1.3 contains 222,994 nodes and 18,206,965 edges from 2,136 PMC -publications [@multiomics-kp]; DAKP 1.26.0 contains 16,257 nodes and 135,263 edges -[@drug-approvals]. DAKP [@dakp], the Drug Approvals Knowledge Provider (now developed in the -glusman-team graph monorepo [@graph-monorepo]), applies Tablassert to regulatory drug-approval -sources and publishes validated, versioned releases to a public dataset archive -[@drug-approvals]; a public linkouts interface exposes its per-edge evidence, and integration -into the Translator ingest infrastructure is under review [@ingests], beginning with a -Tablassert passthrough for the Multiomics graph [@ingests-mokg]. KRAKEN [@kraken], Phenome -Health's unified knowledge graph, integrates the Microbiome, Multiomics, and Drug Approvals -graphs as domain-specific sources. A rare-disease multiomics knowledge graph built with -Tablassert is in preparation. +Tablassert and its declarative precursor underlie several production knowledge graphs. +MicrobiomeKG [@microbiomekg] used an early precursor; deployed Microbiome and Multiomics graphs +[@multiomics-kp] and their maintenance builds use Tablassert. Multiomics 3.1.3 contains 222,994 +nodes and 18,206,965 edges from 2,136 PMC publications [@multiomics-kp]; DAKP 1.26.0 contains +16,257 nodes and 135,263 edges [@drug-approvals]. The Drug Approvals Knowledge Provider (DAKP) +[@dakp], developed in the glusman-team monorepo [@graph-monorepo], publishes validated, +versioned drug-approval graphs [@drug-approvals] and per-edge evidence linkouts. Translator +ingest integration is under review [@ingests], starting with a Multiomics passthrough +[@ingests-mokg]. KRAKEN [@kraken], Phenome Health's unified knowledge graph, integrates the +Microbiome, Multiomics, and Drug Approvals graphs as domain-specific sources. A rare-disease +multiomics knowledge graph built with Tablassert is in preparation. # AI usage disclosure @@ -207,7 +209,7 @@ manuscript. # Author contributions -Skye Lane Goetz developed the software, led design, and performed analyses. Jared C. Roach and +Skye L. Goetz developed the software, led design, and performed analyses. Jared C. Roach and Gwênlyn Glusman helped design the software and analyses and provided detailed project feedback. All authors wrote, revised, and proofread the manuscript. From 6d60849fbeb624c381dcf27cc5208424673030fd Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 13:09:56 -0700 Subject: [PATCH 21/25] docs(paper): frame two central design trade-offs --- paper/paper.md | 33 +++++++++++++++++---------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 3c0cb52f..d3b8ed8b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -66,19 +66,22 @@ recipe-driven pipeline. # Software design -Tablassert's core trade-off is up-front work for build-time ease: a large entity-resolution -database is built or downloaded once, then subsequent builds resolve terms locally in batches -without service calls. Recipes are compiled 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 -sharing an instruction prefix reuse its computed snapshot. - -Pydantic models validate every recipe before execution with field-specific errors. Accepted YAML -satisfies the configuration schema, and successful builds produce correctly formatted KGX; this -guarantees structure, not biological correctness. YAML requires fewer changes than -source-specific code, helping nontechnical contributors and AI coding agents author graphs. This -trades granular control and source-specific optimizations for time savings and broader -participation. +Tablassert has two main trade-offs: + +1. **The fullmap database.** Building or downloading the entity-resolution database requires + up-front storage; subsequent builds resolve terms locally in batches without service + calls. +2. **Declarative YAML configuration.** Pydantic models validate recipes before execution + with field-specific errors. Accepted YAML satisfies its schema, and successful + builds produce correctly formatted KGX; this guarantees structure, not biological correctness. + YAML requires fewer changes than source-specific code, helping nontechnical contributors and + AI coding agents author graphs. This trades granular control and source-specific optimizations + for time savings and broader participation. + +Recipes are compiled 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 sharing an instruction prefix reuse +its computed snapshot. The embedded fullmap database underpins the design. Built from BABEL [@babel] exports, it maps normalized synonyms and cross-vocabulary meta-identifiers onto canonical identifiers with @@ -97,8 +100,6 @@ top 100 experimental taxa derived from Translator knowledge-graph node frequenci all OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some rare species-specific identifiers outside the allowlist. -Resolution constraints - Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species while retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also @@ -135,7 +136,7 @@ post-prune state. Passing these checks does not establish biological validity. Tablassert uses PyO3 Rust for normalization, resolution, database construction, and NDJSON writing. GIL-released Rust regions use Rayon workers for CPU-heavy normalization and sharded lookups, enabling parallel execution even on GIL-enabled Python 3.11, without requiring -free-threaded Python. recipes and validation use Python. +free-threaded Python. Recipes and validation use Python. ## Comparisons From 9aa3bbf5a943361ecf6586e69a15b978b2e02895 Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 14:38:53 -0700 Subject: [PATCH 22/25] docs(paper): apply approved design and comparison revisions --- paper/paper.md | 129 ++++++++++++++++++++++++------------------------- 1 file changed, 62 insertions(+), 67 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index d3b8ed8b..b4cbb716 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -38,9 +38,7 @@ recipe, so a graph starts small instead of being frozen inside a bespoke ingest # Statement of need Tablassert replaces repetitive spreadsheet-ingest code with declarative YAML recipes for -consistent formatting, resolution, and validation. - -Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabular biomedical +consistent formatting, resolution, and validation. Tablassert targets knowledge-graph engineers and bioinformaticians who hold tabular biomedical sources and need rigorously structured output without maintaining a transform pipeline per source. Extending beyond journal supplements, the same recipes apply to any relational source -- trial registries, drug approval records, or a researcher's own tables -- and each build can emit @@ -61,38 +59,23 @@ hosted SRI name resolution service [@nameres] serves BABEL [@babel] identifier r a network API. Hosted builds depend on service availability; reproducing historical results requires preserving responses or pinning the underlying resources. -Tablassert composes declarative mapping, local resolution, and mapping-quality checks into one -recipe-driven pipeline. - # Software design -Tablassert has two main trade-offs: +Tablassert has three main trade-offs: fullmap storage enables local batched resolution without +service calls; declarative YAML favors accessibility and validation over procedural flexibility; +and the Maturin build system favors hot-path performance over implementation simplicity. -1. **The fullmap database.** Building or downloading the entity-resolution database requires - up-front storage; subsequent builds resolve terms locally in batches without service - calls. -2. **Declarative YAML configuration.** Pydantic models validate recipes before execution - with field-specific errors. Accepted YAML satisfies its schema, and successful - builds produce correctly formatted KGX; this guarantees structure, not biological correctness. - YAML requires fewer changes than source-specific code, helping nontechnical contributors and - AI coding agents author graphs. This trades granular control and source-specific optimizations - for time savings and broader participation. - -Recipes are compiled 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 sharing an instruction prefix reuse -its computed snapshot. +## Embedded fullmap database The embedded fullmap database underpins the design. Built from BABEL [@babel] exports, it maps normalized synonyms and cross-vocabulary meta-identifiers onto canonical identifiers with -category and taxon metadata. Level-one normalization trims whitespace and quotes, -Unicode-lowercases text, Porter2-stems ASCII alphabetic tokens, then deduplicates, sorts, and -joins tokens; level two additionally removes non-word characters. - -Identical normalization is applied to synonyms and query text. The store uses redb [@redb], an -embedded, crash-safe Rust key-value engine suited to point lookups, built in a parallel, -memory-bounded streaming pass. The prebuilt archive for the current BABEL 22.2.0 export is 44.5 -GB compressed; the redb store used here, built from the 2026-07-22 export, occupies 92.9 GB +category and taxon metadata. Level one trims, lowercases, Porter2-stems ASCII tokens, then +deduplicates, sorts, and joins them; level two removes non-word characters. + +Synonyms and queries share normalization. Redb [@redb] offers crash-safe NoSQL point lookups +through one predictable, SQL-free query path. Graph builds never write to it, permitting +read-optimized, parallel, memory-bounded construction. The prebuilt archive for the current +BABEL 22.2.0 export is 44.5 GB compressed; the redb store used here, built from the 2026-07-22 export, occupies 92.9 GB across one primary and 16 shard database files. Resolution is exact-match over normalized keys rather than fuzzy search; approximate matches surface only in the audit stage, not in the graph. To keep the database smaller and query times faster, the build applies a fixed allowlist of the @@ -100,17 +83,29 @@ top 100 experimental taxa derived from Translator knowledge-graph node frequenci all OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some rare species-specific identifiers outside the allowlist. -Resolution is column-aware rather than string-in/CURIE-out. Each node column carries its own -constraints: an NCBI taxon filter that keeps taxon-bearing matches for the declared species -while retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also +Resolution is column-aware rather than string-in/CURIE-out. Each node column declares: an NCBI +taxon filter that keeps taxon-bearing matches for the declared species while retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also drops categories the installed model does not recognize), and CURIE prefix and regex exclusions. -A priority score ranks surviving candidates -- exact preferred-name equality ranks 1, -level-one-normalized equality at NLP level 1 ranks 5, everything else 10, multiplied by a -category bonus of 1 for prioritized categories and 50 otherwise -- then deduplicates them to the -best tier, retaining distinct CURIEs that tie. When column context is on, category frequency -across the column being resolved helps break remaining ties; this is a heuristic, not a +Candidates are ranked by exact preferred-name agreement, normalized-name agreement, and Biolink +category priority; the best tier retains tied CURIEs. When column context is on, category frequency across the column helps break remaining +ties; this is a heuristic, not a guarantee of semantic correctness. +## Declarative YAML configuration + +Pydantic models validate recipes before execution with field-specific errors. Accepted recipes +yield correctly formatted KGX after successful builds, guaranteeing structure, not biological +correctness. Recipes require fewer changes than code, helping nontechnical contributors and AI +agents author graphs. The deliberately non-Turing-complete DSL provides common, fast +transformations rather than arbitrary computation; custom operations require extending the tool. + +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 sharing an instruction prefix reuse +its computed snapshot. Lazy optimization means logical recipe order need not dictate physical +execution order. This often improves performance but can be suboptimal for particular workloads +and prevents transformations requiring strict procedural ordering. + Edges carry recipe-declared provenance, including source URLs and publications when provided; traceability depends on the source and configuration. Optional quality control audits each resolved name with a four-stage cascade: exact or alias match, fuzzy string similarity, @@ -120,44 +115,44 @@ similarity using a checkpoint fine-tuned on BABEL synonyms what cheaper stages could not clear. Suspect mappings are dropped and logged with their scores rather than silently retained. -For example, a DAKP FAERS audit (2026-10-22) rejected "factor ii" mapped to `HMDB:HMDB0000607` +For example, a DAKP FAERS 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]. This is one rejection, not measured QC precision or recall. -Structural validation is separate from scientific correctness: an optional study stage asserts -structural invariants over the emitted NDJSON (no duplicate or isolated nodes, no empty required -fields, no edges demoted to bare Association). Like KGX's schema-driven `validate` [@kgx], -`validate-kgx` checks graph records; it constructs records using category-specific Biolink -classes, knowledge-source identity is checked against the Translator infores registry offline, -and `--prune` removes edges failing its checks in place, with the exit code gating the -post-prune state. Passing these checks does not establish biological validity. +Output validation complements recipe validation. An optional study stage checks structural +invariants; `validate-kgx` checks category-specific Biolink records and Translator infores +identity offline, while `--prune` removes failing edges and gates the post-prune exit status. +These checks do not establish biological validity. + +## Maturin build system -Tablassert uses PyO3 Rust for normalization, resolution, database construction, and NDJSON -writing. GIL-released Rust regions use Rayon workers for CPU-heavy normalization and sharded -lookups, enabling parallel execution even on GIL-enabled Python 3.11, without requiring -free-threaded Python. Recipes and validation use Python. +PyO3 Rust stems and normalizes terms, performs sharded fullmap lookups, streams database +construction, and serializes NDJSON. GIL-released Rayon workers parallelize these hot paths on +standard Python 3.11; Python retains recipe parsing, Pydantic validation, and orchestration. +Maturin packages performant native code in PyPI wheels for Linux/macOS, standard Unix-like +platforms for major bioinformatics ecosystems such as +[Bioconda](https://github.com/bioconda/bioconda-recipes). Supported users need no Rust toolchain; +Windows users need WSL or a source build. The performance gain justifies added maintenance and +readability costs. ## Comparisons -Hosted NameResolution [@nameres], a separate SRI service, supports bulk lookup, taxon and -Biolink-type filters, and CURIE prefix inclusion/exclusion. Candidates from that separate -service can be canonicalized through SRI's separate Node Normalization service [@nodenorm] using -batched requests; two network stages need not mean two requests per term. - -Benchmarks clarify deployment: local fullmap needs substantial storage but no service calls; the -separate hosted services avoid local storage but add network and availability dependencies. On a -dual-socket Intel Xeon Gold 6230 host (40 physical cores, 80 hardware threads, 187 GiB RAM, SSD -RAID1, Ubuntu 24.04), 113 hand-selected ingest entries comprised 104 unique terms. Three -fresh-process local batch passes took a median of 0.886 seconds, excluding interpreter startup; -three in-process passes took a median 0.516 seconds. OS caches were not cleared. Tablassert's -local resolution returned complete identifier, name, and category fields for 93 terms. The -alternative hosted workflow used SRI NameResolution [@nameres] for candidate lookup followed by -the separate SRI Node Normalization service [@nodenorm] for canonical identifiers, preferred -names, and categories. One sweep returned complete top-ranked canonical records for 99 terms in -50.23 seconds: 104 lookups with four concurrent requests plus five batched normalization -requests, with no request errors. Matching policies and resource versions differ; this -convenience sample measures neither accuracy nor general service performance. +Tablassert resolved this sample faster than the separate hosted services. On a dual-socket Intel +Xeon Gold 6230 host (40 physical cores, 80 hardware threads, 187 GiB RAM, SSD RAID1, Ubuntu +24.04), 113 hand-selected entries comprised 104 unique terms. Median local time was 0.886 seconds +across three fresh-process passes, excluding startup, and 0.516 seconds across three in-process +passes; OS caches were not cleared. Local resolution returned complete identifier, name, and +category fields for 93 terms. One workflow using SRI NameResolution [@nameres] then the separate +SRI Node Normalization service [@nodenorm] returned complete top-ranked canonical records for 99 +terms in 50.23 seconds: 104 lookups with four concurrent requests plus five batched normalization +requests, without errors. Hosted lookup used `limit=5` and `only_taxa=NCBITaxon:9606`; +NameResolution also supports bulk lookup, Biolink-type filters, and CURIE prefix +inclusion/exclusion. Matching policies and resource versions differ; this convenience sample +measures neither accuracy nor general service performance. + +KGX's schema-driven `validate` [@kgx] likewise checks Biolink conformance; Tablassert adds +study-level structural invariants, offline infores checks, and in-place pruning. The Tablassert tutorial uses 56 Tablassert YAML lines of code (LOC) versus 72 Koza [@koza] YAML/Python LOC (1,152 versus 1,981 non-whitespace characters), excluding blanks, comments, and From e859192e4fd10fb3fd46fecdb85f09b2a24db0ca Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 14:41:54 -0700 Subject: [PATCH 23/25] docs(paper): merge manuscript audit fixes --- paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index b4cbb716..e61134c4 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'Tablassert: Declarative Extraction of NCATS Translator Complicate Knowledge Graphs from Biomedical Tables' +title: 'Tablassert: Declarative Extraction of NCATS Translator Compliant Knowledge Graphs from Biomedical Tables' tags: - knowledge graph - KGX @@ -83,7 +83,7 @@ top 100 experimental taxa derived from Translator knowledge-graph node frequenci all OrganismTaxon rows and rows without taxon metadata; this deliberate trade-off excludes some rare species-specific identifiers outside the allowlist. -Resolution is column-aware rather than string-in/CURIE-out. Each node column declares: an NCBI +Resolution is column-aware rather than string-in/CURIE-out. Each node column declares an NCBI taxon filter that keeps taxon-bearing matches for the declared species while retaining taxon-less rows, Biolink category prioritize and avoid lists (where avoid also drops categories the installed model does not recognize), and CURIE prefix and regex exclusions. Candidates are ranked by exact preferred-name agreement, normalized-name agreement, and Biolink @@ -143,7 +143,7 @@ Xeon Gold 6230 host (40 physical cores, 80 hardware threads, 187 GiB RAM, SSD RA 24.04), 113 hand-selected entries comprised 104 unique terms. Median local time was 0.886 seconds across three fresh-process passes, excluding startup, and 0.516 seconds across three in-process passes; OS caches were not cleared. Local resolution returned complete identifier, name, and -category fields for 93 terms. One workflow using SRI NameResolution [@nameres] then the separate +category fields for 93 terms. One workflow using SRI NameResolution [@nameres], then the separate SRI Node Normalization service [@nodenorm] returned complete top-ranked canonical records for 99 terms in 50.23 seconds: 104 lookups with four concurrent requests plus five batched normalization requests, without errors. Hosted lookup used `limit=5` and `only_taxa=NCBITaxon:9606`; From 692ed40a799bb60096114c218b089f53b1b88cdd Mon Sep 17 00:00:00 2001 From: SkyeAv Date: Fri, 30 Oct 2026 14:56:02 -0700 Subject: [PATCH 24/25] docs(paper): cite Bioconda publication --- paper/paper.bib | 11 +++++++++++ paper/paper.md | 2 +- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index 7542813d..958534c6 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -171,6 +171,17 @@ @article{spires 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}, diff --git a/paper/paper.md b/paper/paper.md index e61134c4..24e4800f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -132,7 +132,7 @@ construction, and serializes NDJSON. GIL-released Rayon workers parallelize thes standard Python 3.11; Python retains recipe parsing, Pydantic validation, and orchestration. Maturin packages performant native code in PyPI wheels for Linux/macOS, standard Unix-like platforms for major bioinformatics ecosystems such as -[Bioconda](https://github.com/bioconda/bioconda-recipes). Supported users need no Rust toolchain; +Bioconda [@bioconda]. Supported users need no Rust toolchain; Windows users need WSL or a source build. The performance gain justifies added maintenance and readability costs. From afaa01ff83ec1028eb998d000201e2bca3d2bbba Mon Sep 17 00:00:00 2001 From: Skye Lane Goetz Date: Fri, 30 Oct 2026 15:16:51 -0700 Subject: [PATCH 25/25] Fix capitalization in acknowledgements section Corrected capitalization in acknowledgements section. --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 24e4800f..3ee90f2a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -211,9 +211,9 @@ All authors wrote, revised, and proofread the manuscript. # Acknowledgements -This work was supported by the National Science Foundation (award 2150265) and by the National +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, OT2TR003428, and OT2TR005706). Any opinions expressed in this manuscript are +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.