From 789a2ca021e9f8c57fc639f4f947d9ed67b1682b Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 08:11:41 -0700 Subject: [PATCH 01/15] Report newick branch lengths that crash downstream consumers DM3's own tree inference emits negative branch lengths and at least one consumer crashes on them rather than degrading. NJ.bf:214-220 computes the three-taxon closed form (d01 + d02 - d12) / 2 with no Max(0, ...), so any triplet violating the triangle inequality yields a negative. NJ.bf:99 returns a saturation sentinel of 1000 for a saturated pair, which propagates through that same subtraction to roughly -499.9. Measured against AxoMEME 2.0's inference path, which computes log((node_count + 1.0) / (dist + 0.1)): any patristic distance <= -0.1 is a log of a negative and an uncaught ValueError. Running the ML team's own calculate_patristic_distances over 283 real DM3 trees raised that error on 13 of them (4.6%), worst case -114.9. This module reports and does not rewrite. Clamping changes branch lengths, branch lengths are the input a model's distances are built from, and quietly altering them would be a fabrication. The caller decides whether to refuse the run or to clamp with the user told. hasCrashingBranchLength is documented and tested as necessary but NOT sufficient: the threshold is on patristic path sums, which can be more negative than any single branch on the path. --- src/lib/utils/treeSanitation.js | 111 ++++++++++++++++++++++++++++ src/test/tree-sanitation.test.js | 120 +++++++++++++++++++++++++++++++ 2 files changed, 231 insertions(+) create mode 100644 src/lib/utils/treeSanitation.js create mode 100644 src/test/tree-sanitation.test.js diff --git a/src/lib/utils/treeSanitation.js b/src/lib/utils/treeSanitation.js new file mode 100644 index 0000000..76369c9 --- /dev/null +++ b/src/lib/utils/treeSanitation.js @@ -0,0 +1,111 @@ +/** + * treeSanitation.js — inspect a newick for branch lengths no downstream consumer should be handed. + * + * WHY THIS EXISTS. DM3's own tree inference emits negative branch lengths, and at least one + * consumer crashes on them rather than degrading. + * + * - src/data/shared/NJ.bf:214-220 computes the three-taxon closed form + * (d01 + d02 - d12) / 2 with no Max(0, ...), so any triplet violating the triangle inequality + * yields a negative. For four or more taxa the NJ core is C++ inside hyphy.wasm and likewise + * does not clamp. + * - src/data/shared/NJ.bf:99 returns a SATURATION SENTINEL of 1000 for a saturated pair, which + * propagates through that same subtraction to roughly -499.9. + * + * Measured consequence, on the AxoMEME 2.0 inference path: + * predict_regression_nexus.py:955 computes log((node_count + 1.0) / (dist + 0.1)). + * Any patristic distance <= -0.1 is log of a negative -> uncaught ValueError. Verified: -0.010 + * survives, -0.11 / -0.5 / -499.9 all crash. The handoff README's "clamps distances >= 0" refers + * to the TRAINING pipeline; the inference path does not clamp. + * + * So this is a DM3-side fact about DM3-side output, and it holds wherever a consumer runs — browser + * or server. It is deliberately a leaf module (imports nothing) so a caller can ask before loading + * anything heavy, matching the pattern in services/prescreen/scope.js. + * + * This module REPORTS. It does not silently rewrite a user's tree: clamping changes branch lengths, + * branch lengths are the input a model's distances are built from, and quietly altering them would + * be a fabrication of the same kind this codebase already refuses elsewhere. The caller decides + * whether to refuse the run or to clamp with the user told. + */ + +/** Matches a newick branch length, including scientific notation and a leading minus. */ +const BRANCH_LENGTH = /:(-?\d+\.?\d*(?:[eE][-+]?\d+)?)/g; + +/** + * The value NJ.bf:99 returns for a saturated pair. It is not a distance; it is a sentinel, and it + * reaches trees as a large positive length or, after the three-taxon subtraction, a large negative. + */ +export const NJ_SATURATION_SENTINEL = 1000; + +/** + * Every branch length in a newick, in document order. Empty array for a topology-only tree. + * @param {string|null} tree + * @returns {number[]} + */ +export function branchLengths(tree) { + if (!tree || typeof tree !== 'string') return []; + const out = []; + let m; + BRANCH_LENGTH.lastIndex = 0; + while ((m = BRANCH_LENGTH.exec(tree)) !== null) { + const v = parseFloat(m[1]); + if (Number.isFinite(v)) out.push(v); + } + return out; +} + +/** + * Describe what is wrong with a tree's branch lengths, without changing it. + * + * @param {string|null} tree + * @returns {{ + * total: number, negative: number, negativeFraction: number, min: number|null, + * saturated: number, hasLengths: boolean, ok: boolean, reasons: string[] + * }} + */ +export function inspectBranchLengths(tree) { + const lengths = branchLengths(tree); + const negatives = lengths.filter((v) => v < 0); + const saturated = lengths.filter((v) => Math.abs(v) >= NJ_SATURATION_SENTINEL); + const reasons = []; + + if (!lengths.length) reasons.push('topology-only: the tree carries no branch lengths'); + if (negatives.length) { + reasons.push( + `${negatives.length} of ${lengths.length} branch lengths are negative ` + + `(smallest ${Math.min(...negatives)})` + ); + } + if (saturated.length) { + reasons.push( + `${saturated.length} branch length(s) at or past the NJ saturation sentinel ` + + `(|value| >= ${NJ_SATURATION_SENTINEL}) — these are not distances` + ); + } + + return { + total: lengths.length, + negative: negatives.length, + negativeFraction: lengths.length ? negatives.length / lengths.length : 0, + min: lengths.length ? Math.min(...lengths) : null, + saturated: saturated.length, + hasLengths: lengths.some((v) => v > 0), + ok: reasons.length === 0, + reasons + }; +} + +/** + * Would a consumer that computes log((n + 1) / (d + 0.1)) over PATRISTIC distances crash? + * + * Deliberately conservative, and the comment matters more than the code: the crash threshold is on + * PATRISTIC distances (root-to-root path sums), not on individual branch lengths, and a path sum can + * be more negative than any single branch on it. A tree can therefore pass this check and still + * crash the consumer. Treat `false` as "no single branch proves a crash", never as "safe". + * + * @param {string|null} tree + * @param {number} [epsilon] the additive constant in the consumer's log argument + * @returns {boolean} + */ +export function hasCrashingBranchLength(tree, epsilon = 0.1) { + return branchLengths(tree).some((v) => v <= -epsilon); +} diff --git a/src/test/tree-sanitation.test.js b/src/test/tree-sanitation.test.js new file mode 100644 index 0000000..8e23443 --- /dev/null +++ b/src/test/tree-sanitation.test.js @@ -0,0 +1,120 @@ +/** + * Tests for treeSanitation.js. + * + * The fixtures are not invented. They are the shapes DM3's own NJ inference produces + * (src/data/shared/NJ.bf) and the values measured to crash the AxoMEME 2.0 inference path at + * predict_regression_nexus.py:955, which computes log((node_count + 1.0) / (dist + 0.1)). + */ +import { describe, it, expect } from 'vitest'; +import { + branchLengths, + inspectBranchLengths, + hasCrashingBranchLength, + NJ_SATURATION_SENTINEL +} from '../lib/utils/treeSanitation.js'; + +describe('branchLengths', () => { + it('reads plain, scientific and negative lengths', () => { + expect(branchLengths('((a:0.1,b:0.2):0.05,c:0.3);')).toEqual([0.1, 0.2, 0.05, 0.3]); + expect(branchLengths('(a:1.5e-2,b:3.0E-3);')).toEqual([0.015, 0.003]); + expect(branchLengths('(x:-0.5,y:0.2,z:0.4);')).toEqual([-0.5, 0.2, 0.4]); + }); + + it('returns nothing for a topology-only tree or non-string input', () => { + expect(branchLengths('((a,b),c);')).toEqual([]); + expect(branchLengths('')).toEqual([]); + expect(branchLengths(null)).toEqual([]); + }); + + it('is not confused by a bootstrap value or a quoted label', () => { + // )95: is a support value on an internal node, not a length; 'Homo:sapiens' is a name. + expect(branchLengths('((a:0.1,b:0.2)95:0.05,c:0.3);')).toEqual([0.1, 0.2, 0.05, 0.3]); + expect(branchLengths("(('Homo:sapiens':0.1,b:0.2):0.05);")).toEqual([0.1, 0.2, 0.05]); + }); + + it('is re-entrant — a global regex must not carry lastIndex between calls', () => { + const t = '((a:0.1,b:0.2):0.05,c:0.3);'; + expect(branchLengths(t)).toEqual(branchLengths(t)); + }); +}); + +describe('inspectBranchLengths', () => { + it('passes a clean tree', () => { + const r = inspectBranchLengths('((a:0.1,b:0.2):0.05,c:0.3);'); + expect(r.ok).toBe(true); + expect(r.negative).toBe(0); + expect(r.hasLengths).toBe(true); + expect(r.reasons).toEqual([]); + }); + + it('flags a topology-only tree', () => { + const r = inspectBranchLengths('((a,b),c);'); + expect(r.ok).toBe(false); + expect(r.hasLengths).toBe(false); + expect(r.reasons[0]).toMatch(/topology-only/); + }); + + it('counts negatives and reports the worst one', () => { + const r = inspectBranchLengths('(x:-0.5,y:0.2,z:-0.01);'); + expect(r.negative).toBe(2); + expect(r.total).toBe(3); + expect(r.min).toBe(-0.5); + expect(r.negativeFraction).toBeCloseTo(2 / 3, 10); + expect(r.reasons.join(' ')).toMatch(/negative/); + }); + + it('flags the NJ saturation sentinel, which is not a distance', () => { + // NJ.bf:99 returns 1000 for a saturated pair; the three-taxon closed form at NJ.bf:214-220 + // turns that into roughly -499.9. + const r = inspectBranchLengths(`(a:${NJ_SATURATION_SENTINEL},b:0.2);`); + expect(r.saturated).toBe(1); + expect(r.ok).toBe(false); + expect(r.reasons.join(' ')).toMatch(/saturation sentinel/); + + const derived = inspectBranchLengths('(a:-499.9,b:0.2);'); + expect(derived.negative).toBe(1); + expect(derived.ok).toBe(false); + }); + + it('does not modify the tree it inspects', () => { + // The module reports; it must never silently rewrite branch lengths, because those are the + // input a consumer's distances are built from. + const t = '(x:-0.5,y:0.2);'; + inspectBranchLengths(t); + expect(t).toBe('(x:-0.5,y:0.2);'); + }); +}); + +describe('hasCrashingBranchLength', () => { + // Measured against the real inference path: log((3 + 1.0) / (d + 0.1)) is a math domain error + // for d <= -0.1. -0.010 survives; -0.11, -0.5 and -499.9 all crash. + it('matches the measured crash threshold', () => { + expect(hasCrashingBranchLength('(a:-0.010,b:0.2);')).toBe(false); + expect(hasCrashingBranchLength('(a:-0.11,b:0.2);')).toBe(true); + expect(hasCrashingBranchLength('(a:-0.5,b:0.2);')).toBe(true); + expect(hasCrashingBranchLength('(a:-499.9,b:0.2);')).toBe(true); + }); + + it('is false for clean and topology-only trees', () => { + expect(hasCrashingBranchLength('((a:0.1,b:0.2):0.05,c:0.3);')).toBe(false); + expect(hasCrashingBranchLength('((a,b),c);')).toBe(false); + }); + + it('honours a different epsilon', () => { + expect(hasCrashingBranchLength('(a:-0.3,b:0.2);', 0.5)).toBe(false); + expect(hasCrashingBranchLength('(a:-0.6,b:0.2);', 0.5)).toBe(true); + }); + + it('is documented as necessary but NOT sufficient', () => { + // The consumer's threshold is on PATRISTIC distances — path sums — which can be more + // negative than any single branch. Two small negatives on one path sum past the threshold + // while no individual branch does. This test pins that limitation so nobody reads a false + // return as a safety guarantee. + const t = '((a:-0.06,b:-0.06):0.01,c:0.3);'; + expect(hasCrashingBranchLength(t)).toBe(false); // no single branch reaches -0.1 + const sum = branchLengths(t) + .filter((v) => v < 0) + .reduce((s, v) => s + v, 0); + expect(sum).toBeLessThan(-0.1); // but the path they share does + }); +}); From f366530d0ed115df9701cb8a51116f7dc50f23d7 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 08:11:53 -0700 Subject: [PATCH 02/15] Rescope the ML-runtime guards from a repo-wide ban to gate reachability These guards asserted "DM3 ships no ML runtime, anywhere". For as long as the MEME hit-likelihood gate was the only model in the repository, that was the same statement as the one actually worth defending. It is not any more: AxoMEME 2.0 is a 3.78 MB transformer whose graph is a real neural network, and no 40-line walker is going to execute it, so onnxruntime-web is now a legitimate dependency and the old assertion would fail for a legitimate reason. The fix someone reaches for when a guard fails legitimately is deleting the guard. What was being defended was never "no runtime exists". It was that the gate costs almost nothing and is reachable from every method, so it must not drag a runtime behind it -- the first version of that feature downloaded 13.5 MB of ONNX Runtime WASM for all fifteen methods and rendered nothing for fourteen of them. So the vitest guard now walks the gate's own import closure and proves no ML runtime is in it, which is strictly stronger than the per-file grep it replaces (that version listed four files by hand and would not have noticed a fifth). package.json is checked as an allowlist rather than a ban. Writing it surfaced a real hole: the first version did not fire when `import 'onnxruntime-web';` was injected into a reachable module, because a side-effect import has no `from` clause -- and that is exactly the shape a WASM runtime arrives in, since you import it for registration rather than for a binding. All three specifier forms are now matched, and both halves are proven to fail. The e2e byte guards do not weaken at all. They are scoped to two flows, select-FEL and select-MEME, and neither is AxoMEME; only their stated rationale was false. Their invariant was always per-flow: a method must not pay for a model it does not render. That matters more now, not less, because a stray static import resolves instead of erroring. --- e2e/18-meme-hit-likelihood.spec.js | 38 ++++--- package-lock.json | 125 ++++++++++++++++++++++ package.json | 3 + scripts/prescreen/requirements.txt | 8 +- src/test/meme-hit-likelihood.test.js | 153 ++++++++++++++++++++++----- 5 files changed, 287 insertions(+), 40 deletions(-) diff --git a/e2e/18-meme-hit-likelihood.spec.js b/e2e/18-meme-hit-likelihood.spec.js index 5c59969..651907a 100644 --- a/e2e/18-meme-hit-likelihood.spec.js +++ b/e2e/18-meme-hit-likelihood.spec.js @@ -6,7 +6,9 @@ * 2. WHAT IT COSTS. The original version of this feature downloaded 13.5 MB of ML-runtime WASM * for every method, including the ~14 that have no estimate, and then rendered nothing. An * "is the element absent?" assertion passed the whole time, because the element WAS absent — - * the bytes were the bug. So these tests count bytes, not just elements. + * the bytes were the bug. So these tests count bytes, not just elements. DM3 now ships + * onnxruntime-web for AxoMEME, which makes that regression cheap to reintroduce by accident; + * see RUNTIME_TOMBSTONE for why these flows must still transfer none of it. * * WHAT MOVED SINCE THE LAST REVISION. The estimator is no longer a converted coefficient table; it * is XGBoost's own `save_model()` JSON, shipped verbatim and walked in ~40 lines of plain JS. That @@ -159,12 +161,25 @@ const ESTIMATOR_MARKS = ['meme-hit-likelihood', 'hit-basis-toggle']; const SCOPE_LEAF = /prescreen\/scope\.js/; /** Dev-server source paths. Kept as a belt-and-braces net alongside the content markers. */ -const ESTIMATOR_ASSET = /prescreen|hit_likelihood|hitLikelihood|meme_gate|onnxruntime|ort-wasm|tfjs/i; +const ESTIMATOR_ASSET = + /prescreen|hit_likelihood|hitLikelihood|meme_gate|onnxruntime|ort-wasm|tfjs/i; /** - * A TOMBSTONE, not a live dependency. These names belong to the ML runtimes DM3 does not ship and - * must never start shipping again; the first version of this feature pulled 13.5 MB of the first - * one. Nothing else in the repository references them — that is the state this defends. + * A TOMBSTONE — and READ THIS BEFORE RELAXING IT, because the sentence that used to justify it is + * no longer true while the assertions themselves are unchanged and still exactly right. + * + * It used to say "nothing else in the repository references these names — that is the state this + * defends". DM3 now has a legitimate reason to depend on onnxruntime-web: AxoMEME 2.0 is a 3.78 MB + * transformer whose graph is a real neural network, and it cannot be walked in plain JS the way the + * gate's 500 three-feature trees can. So the runtime IS in package.json now. + * + * That does NOT weaken anything below, because these tests are scoped to two flows — select FEL, + * and select MEME — and neither one is AxoMEME. The invariant they defend was always per-flow, not + * per-repository: A METHOD MUST NOT PAY FOR A MODEL IT DOES NOT RENDER. The first version of this + * feature charged all fifteen methods 13.5 MB for an estimate fourteen of them never showed. Now + * that a runtime is a real dependency that resolves instead of erroring, an accidental static + * import would silently pull ~13 MB into the main graph and every one of these flows would pay it. + * The guard therefore matters MORE than it did when it was written, not less. * * Checked against EVERY response, and against response BODIES as well as URLs. Previously the byte * recorder discarded anything that did not already match ESTIMATOR_ASSET before the tombstone was @@ -333,7 +348,6 @@ test.describe('MEME hit-likelihood', () => { await expect(panel.locator('[data-testid="meme-hit-likelihood"]')).toHaveCount(1); await expect(panel.locator('.timing-estimate')).toHaveCount(1); }); - }); /** @@ -455,8 +469,10 @@ test.describe('MEME hit-likelihood cost', () => { // And the row really is driven by the estimator chunk, not by something inlined into the // main bundle — otherwise "the model is fetched lazily" would be true and irrelevant. - expect(all.filter(isEstimatorCode).length, 'the estimator chunk was never fetched') - .toBeGreaterThan(0); + expect( + all.filter(isEstimatorCode).length, + 'the estimator chunk was never fetched' + ).toBeGreaterThan(0); // No ML runtime, now or ever again — checked over every response in the session, by URL and // by content. @@ -506,11 +522,7 @@ test.describe('MEME hit-likelihood routing', () => { test('a discouraging estimate says what about the data would change it', async ({ page }) => { // 4 taxa x 200 codons, two substitutions each on disjoint sites: the thin-and-shallow shape // that the low band is made of (its real population medians 4 sequences and ~140 codons). - await uploadAndAnalyze( - page, - 'synthetic-thin.fna', - lowDivergenceAlignment(4, 200, 2) - ); + await uploadAndAnalyze(page, 'synthetic-thin.fna', lowDivergenceAlignment(4, 200, 2)); await selectMethod(page, 'MEME'); const row = page.locator('[data-testid="meme-hit-likelihood"]'); diff --git a/package-lock.json b/package-lock.json index 16766a7..4b51b14 100644 --- a/package-lock.json +++ b/package-lock.json @@ -17,6 +17,7 @@ "jszip": "^3.10.1", "lucide-svelte": "^0.559.0", "marked": "^15.0.12", + "onnxruntime-web": "^1.27.0", "phylotree": "^2.2.1", "socket.io-client": "^4.8.1", "socket.io-stream": "^0.9.1", @@ -2677,6 +2678,63 @@ "@opentelemetry/api": "^1.8" } }, + "node_modules/@protobufjs/aspromise": { + 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"https://registry.npmjs.org/flatbuffers/-/flatbuffers-25.9.23.tgz", + "integrity": "sha512-MI1qs7Lo4Syw0EOzUl0xjs2lsoeqFku44KpngfIduHBYvzm8h2+7K8YMQh1JtVVVrUvhLpNwqVi4DERegUJhPQ==", + "license": "Apache-2.0" + }, "node_modules/flatted": { "version": "3.3.2", "resolved": "https://registry.npmjs.org/flatted/-/flatted-3.3.2.tgz", @@ -7718,6 +7782,12 @@ "dev": true, "license": "MIT" }, + "node_modules/guid-typescript": { + "version": "1.0.9", + "resolved": "https://registry.npmjs.org/guid-typescript/-/guid-typescript-1.0.9.tgz", + "integrity": "sha512-Y8T4vYhEfwJOTbouREvG+3XDsjr8E3kIr7uf+JZ0BYloFsttiHU0WfvANVsR7TxNUJa/WpCnw/Ino/p+DeBhBQ==", + "license": "ISC" + }, "node_modules/has-flag": { "version": "4.0.0", "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz", @@ -8729,6 +8799,12 @@ "node": ">= 12.0.0" } }, + "node_modules/long": { + "version": "5.3.2", + "resolved": "https://registry.npmjs.org/long/-/long-5.3.2.tgz", + "integrity": 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"onnxruntime-common": "1.27.0", + "platform": "^1.3.6", + "protobufjs": "^7.2.4" + } + }, "node_modules/open": { "version": "8.4.2", "resolved": "https://registry.npmjs.org/open/-/open-8.4.2.tgz", @@ -9851,6 +9947,12 @@ "pathe": "^2.0.1" } }, + "node_modules/platform": { + "version": "1.3.6", + "resolved": "https://registry.npmjs.org/platform/-/platform-1.3.6.tgz", + "integrity": "sha512-fnWVljUchTro6RiCFvCXBbNhJc2NijN7oIQxbwsyL0buWJPG85v81ehlHI9fXrJsMNgTofEoWIQeClKpgxFLrg==", + "license": "MIT" + }, "node_modules/playwright": { "version": "1.57.0", "resolved": "https://registry.npmjs.org/playwright/-/playwright-1.57.0.tgz", @@ -10329,6 +10431,29 @@ "node": ">=0.4.0" } }, + "node_modules/protobufjs": { + "version": "7.6.5", + "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.6.5.tgz", + "integrity": "sha512-/FPD0nUc9jH6rfFjji9IBqOz4pcSE3CsT1m7Ep6Mdb0LxSUMj8hgl6GomOvZzpNpAqqGaXA0P3VSrZLFzIhQrw==", + "hasInstallScript": true, + "license": "BSD-3-Clause", + "dependencies": { + "@protobufjs/aspromise": "^1.1.2", + "@protobufjs/base64": "^1.1.2", + "@protobufjs/codegen": "^2.0.5", + "@protobufjs/eventemitter": "^1.1.1", + "@protobufjs/fetch": "^1.1.1", + "@protobufjs/float": "^1.0.2", + "@protobufjs/path": "^1.1.2", + "@protobufjs/pool": "^1.1.0", + "@protobufjs/utf8": "^1.1.1", + "@types/node": ">=13.7.0", + "long": "^5.3.2" + }, + "engines": { + "node": ">=12.0.0" + } + }, "node_modules/proxy-from-env": { "version": "1.1.0", "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz", diff --git a/package.json b/package.json index 544a80d..780e7b8 100644 --- a/package.json +++ b/package.json @@ -37,6 +37,8 @@ "test:e2e": "npm run test:e2e:fast && npm run test:e2e:slow", "test:e2e:fast": "playwright test --grep-invert @slow", "test:e2e:slow": "playwright test --grep @slow --workers=1", + "verify:axomeme-reference": "python3 scripts/axomeme/verify_preprocessing.py", + "verify:axomeme-preprocessing": "node scripts/axomeme/verify_preprocessing.mjs", "verify:model-parity": "python3 scripts/prescreen/verify_parity.py", "verify:model-parity:self-test": "python3 scripts/prescreen/verify_parity.py --self-test", "verify:model-parity:boundary": "python3 scripts/prescreen/verify_parity.py --boundary", @@ -105,6 +107,7 @@ "jszip": "^3.10.1", "lucide-svelte": "^0.559.0", "marked": "^15.0.12", + "onnxruntime-web": "^1.27.0", "phylotree": "^2.2.1", "socket.io-client": "^4.8.1", "socket.io-stream": "^0.9.1", diff --git a/scripts/prescreen/requirements.txt b/scripts/prescreen/requirements.txt index 5977e2d..0108924 100644 --- a/scripts/prescreen/requirements.txt +++ b/scripts/prescreen/requirements.txt @@ -1,10 +1,16 @@ # Verification-only dependencies for the MEME hit-likelihood gate. # -# These are NOT application dependencies and must never become one. DM3 ships no ML runtime at all: +# These are NOT application dependencies and must never become one. THE GATE ships no ML runtime: # the browser walks XGBoost's own save_model() JSON in about 40 lines of plain JS (xgbEnsemble.js # over meme_gate.json), with no converter and no intermediate format between the file the ML team # exports and the bytes the browser parses. # +# (This file used to say "DM3 ships no ML runtime at all". That stopped being true when AxoMEME 2.0 +# arrived — it is a 3.78 MB transformer and needs onnxruntime-web, which no hand-written walker is +# going to replace. The gate is the opposite case and stays runtime-free: three features, 500 trees, +# reachable from every method, so a runtime behind it would be paid for by all fifteen. The +# reachability guard in src/test/meme-hit-likelihood.test.js is what holds that line now.) +# # xgboost is here for exactly one reason: to be an INDEPENDENT SECOND IMPLEMENTATION of that walk, # reading the same bytes, so CI can prove the shipped JS reproduces the library's own scoring # (scripts/prescreen/verify_parity.py). It must stay on this side of the fence. Nothing here reaches diff --git a/src/test/meme-hit-likelihood.test.js b/src/test/meme-hit-likelihood.test.js index 261fe08..36c6ab3 100644 --- a/src/test/meme-hit-likelihood.test.js +++ b/src/test/meme-hit-likelihood.test.js @@ -406,7 +406,11 @@ describe('the walker refuses a malformed model', () => { const REFUSALS = [ // --- multi-class: the walker sums ONE tree group; K groups produce a plausible number with // --- no meaning at all. - ['multi-class (declared)', patch(modelDoc, [...LMP, 'num_class'], '3'), /num_class=3|multi-class/i], + [ + 'multi-class (declared)', + patch(modelDoc, [...LMP, 'num_class'], '3'), + /num_class=3|multi-class/i + ], [ 'multi-class (structural, via tree_info)', patch( @@ -429,10 +433,18 @@ describe('the walker refuses a malformed model', () => { ), /categorical/i ], - ['categorical split (categories_nodes)', patch(modelDoc, [...TREE0, 'categories_nodes'], [0]), /categorical/i], + [ + 'categorical split (categories_nodes)', + patch(modelDoc, [...TREE0, 'categories_nodes'], [0]), + /categorical/i + ], [ 'categorical encoding table (cats.enc)', - patch(modelDoc, [...GBM, 'cats'], { enc: [{ values: [1, 2, 3] }], feature_segments: [], sorted_idx: [] }), + patch(modelDoc, [...GBM, 'cats'], { + enc: [{ values: [1, 2, 3] }], + feature_segments: [], + sorted_idx: [] + }), /categorical/i ], @@ -480,7 +492,11 @@ describe('the walker refuses a malformed model', () => { patch(modelDoc, [...GBM, 'gbtree_model_param', 'num_parallel_tree'], '4'), /num_parallel_tree|forest/i ], - ['vector leaves', patch(modelDoc, [...TREE0, 'tree_param', 'size_leaf_vector'], '2'), /size_leaf_vector|vector leaves/i], + [ + 'vector leaves', + patch(modelDoc, [...TREE0, 'tree_param', 'size_leaf_vector'], '2'), + /size_leaf_vector|vector leaves/i + ], // --- topology: a child index past the end reads `undefined` and compares false forever; a // --- child index of 0 is a cycle back to the root, which would hang the browser. @@ -489,7 +505,9 @@ describe('the walker refuses a malformed model', () => { patch( modelDoc, [...TREE0, 'right_children'], - modelDoc.learner.gradient_booster.model.trees[0].right_children.map((r, i) => (i === 0 ? 999999 : r)) + modelDoc.learner.gradient_booster.model.trees[0].right_children.map((r, i) => + i === 0 ? 999999 : r + ) ), /out of range/i ], @@ -498,7 +516,9 @@ describe('the walker refuses a malformed model', () => { patch( modelDoc, [...TREE0, 'left_children'], - modelDoc.learner.gradient_booster.model.trees[0].left_children.map((l, i) => (i === 0 ? 0 : l)) + modelDoc.learner.gradient_booster.model.trees[0].left_children.map((l, i) => + i === 0 ? 0 : l + ) ), /out of range/i ], @@ -507,7 +527,9 @@ describe('the walker refuses a malformed model', () => { patch( modelDoc, [...TREE0, 'right_children'], - modelDoc.learner.gradient_booster.model.trees[0].right_children.map((r, i) => (i === 0 ? -1 : r)) + modelDoc.learner.gradient_booster.model.trees[0].right_children.map((r, i) => + i === 0 ? -1 : r + ) ), /one child set to -1/i ], @@ -528,7 +550,11 @@ describe('the walker refuses a malformed model', () => { /written by XGBoost 1\.7\.6/ ], ['no version array at all', patch(modelDoc, ['version'], undefined), /version/i], - ['a base_score the walker will not guess at', patch(modelDoc, [...LMP, 'base_score'], '[0.3,0.3,0.4]'), /components/i] + [ + 'a base_score the walker will not guess at', + patch(modelDoc, [...LMP, 'base_score'], '[0.3,0.3,0.4]'), + /components/i + ] ]; for (const [name, doc, pattern] of REFUSALS) { @@ -1187,7 +1213,10 @@ describe('the copy states a rate, and states the same rate everywhere', () => { expect(stated, `the uncertain band no longer quotes an interval: ${sentence}`).not.toBeNull(); const p = hit / n; const half = 1.96 * Math.sqrt((p * (1 - p)) / n); - for (const [i, want] of [Math.round(100 * (p - half)), Math.round(100 * (p + half))].entries()) { + for (const [i, want] of [ + Math.round(100 * (p - half)), + Math.round(100 * (p + half)) + ].entries()) { expect( Number(stated[i + 1]), `the copy quotes [${stated[1]}%, ${stated[2]}%]; ${hit}/${n} gives ` + @@ -1480,7 +1509,11 @@ describe('scope: the estimate only ever talks about MEME', () => { ]; const levelsSeen = new Set(); for (const [name, args] of states) { - const res = await estimateHitLikelihood({ ...args, model, opts: { resampleAvailable: true } }); + const res = await estimateHitLikelihood({ + ...args, + model, + opts: { resampleAvailable: true } + }); if (res.level) levelsSeen.add(res.level); const text = visibleText(res); expect(text, `state "${name}" named another method: ${text}`).not.toMatch(OTHER_METHODS); @@ -1533,20 +1566,84 @@ describe('scope: the estimate only ever talks about MEME', () => { expect(MODEL_BASIS).toMatch(/MEME/); }); - it('ships no ML runtime, in package.json or anywhere the estimator can reach', () => { - // A tombstone. This feature's first version downloaded 13.5 MB of ONNX Runtime WASM for - // every method, including the ~14 that render no estimate. The whole conversion pipeline is - // gone — the browser parses XGBoost's own save_model() output — and these two assertions are - // what stop a runtime coming back with a future model. + it('reaches no ML runtime from anywhere in the gate', () => { + // A tombstone, RESCOPED — read this before you touch it. + // + // This assertion was written as "DM3 ships no ML runtime, anywhere", and for as long as the + // gate was the only model in the repository those two statements were the same statement. + // They are not any more. AxoMEME 2.0 is a 3.78 MB transformer that genuinely needs + // onnxruntime-web: its graph is a real neural network, not 500 trees of three features, and + // no 40-line walker is going to execute it. So the repo-wide ban would now fail for a + // legitimate reason, and the fix someone reaches for when a guard fails legitimately is + // deleting the guard. + // + // What was actually being defended was never "no runtime exists". It was: THE GATE COSTS + // ALMOST NOTHING AND IS REACHABLE FROM EVERY METHOD, SO IT MUST NOT DRAG A RUNTIME BEHIND + // IT. The gate renders for one method out of fifteen; the first version of this feature + // downloaded 13.5 MB of ONNX Runtime WASM for all fifteen and then rendered nothing for + // fourteen of them. That failure mode gets MORE available once a runtime is a legitimate + // dependency, not less, because now a stray import resolves instead of erroring. + // + // So the guard is now about REACHABILITY, which is the property that was always load-bearing: + // walk the gate's own import closure and prove no ML runtime is in it. That is strictly + // stronger than the per-file grep it replaces — the old version listed four files by hand and + // would not have noticed a fifth. + const RUNTIME = /^(onnxruntime|@tensorflow\/|@xenova\/|onnx|torch|tflite)/i; + // THREE forms, because the first version of this test had only the first and therefore did + // not fire when `import 'onnxruntime-web';` was injected into a reachable module to prove it + // could. A side-effect import has no `from` clause, and it is the exact shape a runtime + // arrives in — you import it for the WASM registration, not for a binding. + const SPECIFIER_FORMS = [ + /\bfrom\s*['"]([^'"]+)['"]/g, // import … from 'x' / export … from 'x' + /\bimport\s*\(\s*['"]([^'"]+)['"]\s*\)/g, // dynamic import('x') + /^\s*import\s+['"]([^'"]+)['"]/gm // side-effect import 'x' + ]; + + const seen = new Set(); + const offenders = []; + const visit = (absPath) => { + if (seen.has(absPath) || !existsSync(absPath)) return; + seen.add(absPath); + // Strip comments before matching. hitLikelihoodModel.js documents its own usage with a + // literal `import … from './hitLikelihood.js'` inside a doc block, and a commented-out + // runtime import must not be reported as a live edge in either direction. + const src = readFileSync(absPath, 'utf8') + .replace(/\/\*[\s\S]*?\*\//g, '') + .replace(/^\s*\/\/.*$/gm, ''); + for (const re of SPECIFIER_FORMS) { + re.lastIndex = 0; + let m; + while ((m = re.exec(src)) !== null) { + const spec = m[1]; + if (!spec) continue; + if (RUNTIME.test(spec)) { + offenders.push(`${absPath.replace(REPO, '')} imports ${spec}`); + continue; + } + // Follow relative edges only. A bare specifier that is not a runtime is somebody + // else's package and cannot pull one in without appearing in package.json, which + // is checked separately below. + if (spec.startsWith('.')) visit(join(dirname(absPath), spec)); + } + } + }; + // Every entry point a caller can reach the gate through. + for (const f of ['scope.js', 'hitLikelihood.js', 'hitLikelihoodModel.js', 'xgbEnsemble.js']) { + visit(join(PRESCREEN, f)); + } + expect(offenders, 'an ML runtime is reachable from the gate').toEqual([]); + // The walk has to have actually walked. Without this the test passes trivially if the entry + // filenames are ever renamed out from under it. + expect(seen.size).toBeGreaterThanOrEqual(4); + + // package.json is still checked, but as an ALLOWLIST rather than a ban. onnxruntime-web is + // permitted because AxoMEME needs it; anything else in this family is not, and adding one is + // now a deliberate edit to this line rather than something that slips in with a lockfile. + const ALLOWED = new Set(['onnxruntime-web']); const pkg = JSON.parse(readFileSync(join(REPO, 'package.json'), 'utf8')); const deps = Object.keys({ ...pkg.dependencies, ...pkg.devDependencies }); - expect(deps.filter((d) => /onnx|tensorflow|tfjs|torch|tflite/i.test(d))).toEqual([]); - for (const f of ['xgbEnsemble.js', 'hitLikelihoodModel.js', 'hitLikelihood.js', 'scope.js']) { - const src = readFileSync(join(PRESCREEN, f), 'utf8'); - expect(src, `${f} references an ML runtime`).not.toMatch( - /\bonnx|onnxruntime|ort-wasm|tensorflow|tfjs\b/i - ); - } + const unexpected = deps.filter((d) => RUNTIME.test(d) && !ALLOWED.has(d)); + expect(unexpected, 'unexpected ML runtime in package.json').toEqual([]); }); }); @@ -1633,7 +1730,10 @@ describe.skipIf(!oos)('out-of-sample calibration (the rates the copy quotes)', ( console.log( `[meme-hit-likelihood] OUT-OF-SAMPLE on ${oos.length} unseen jobs: ` + ['unlikely', 'uncertain', 'likely'] - .map((b) => `${b} ${band[b].hit}/${band[b].n} = ${(100 * band[b].hit / band[b].n).toFixed(1)}%`) + .map( + (b) => + `${b} ${band[b].hit}/${band[b].n} = ${((100 * band[b].hit) / band[b].n).toFixed(1)}%` + ) .join(' | ') ); }); @@ -1678,8 +1778,10 @@ describe.skipIf(!oos)('out-of-sample calibration (the rates the copy quotes)', ( const level = byValue[value]; if (!level) continue; // the base-rate claim is checked below const rate = band[level].hit / band[level].n; - expect(Math.abs(rate - value), `panel's ${what} (${value}) vs measured ${rate.toFixed(3)}`) - .toBeLessThan(0.06); + expect( + Math.abs(rate - value), + `panel's ${what} (${value}) vs measured ${rate.toFixed(3)}` + ).toBeLessThan(0.06); } }); @@ -1688,4 +1790,3 @@ describe.skipIf(!oos)('out-of-sample calibration (the rates the copy quotes)', ( expect(Math.abs(hits / oos.length - 0.85)).toBeLessThan(0.03); }); }); - From ad5fcebb3013f459c13bb87f63a0a6545acc22a1 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 08:12:20 -0700 Subject: [PATCH 03/15] Port AxoMEME 2.0 preprocessing to JS and wire the ONNX session AxoMEME 2.0 predicts MEME's per-site statistics in seconds instead of the hours a full run takes. The exported graph is the MODEL ONLY: it takes five already-computed tensors and returns five, and the preprocessing that produces those tensors is not in the graph. torch.linalg.eigh has no ONNX lowering -- verified directly, the identical module exports with eigh removed and fails with it present -- so mds_coords is a graph input and the whole pipeline has to be rebuilt in JS. Parity is proven against the ML team's own functions, extracted verbatim from predict_regression_nexus.py and exec'd rather than reimplemented, over 270 real DataMonkey trees: patristic distances 10,405,350 cells, worst |delta| 0.000e+0 (exact) MDS coordinates 270/270 within 1e-5 relative, worst 3.35e-7 The MDS result was not expected to be reachable. Two things made it work. Their code already canonicalises eigenvector signs, which removes half the ambiguity for free. And the reference squares FLOAT32 distances, because dist_tensor is a torch.float32 tensor -- feeding it full float64 changes components 2 and 3 by 40-99% on real trees, since squared distances reach ~1e6 while the fourth eigenvalue can be ~1e-1. One Math.fround took the failures from 5 to 1 and the worst error from 2.03e-1 to 1.665e-4. The tokenizer deliberately implements the TRAINING vocabulary, not the one in the handoff's inference driver. Those disagree on 63 of 64 codons: the driver defines its own 60-codon alphabetical CODON_LIST, then redefines get_codon_token without redefining CODON_TO_IDX, and imports only three non-tokenizer names from the training module. TTA plus the three stops are absent from its list entirely and collapse to "unknown". A model trained on TCAG-64 must be served TCAG-64. Pinned from both sides so we fail if we drift from training and if someone aligns us to the driver. Cost discipline, because AxoMEME is one method of fifteen and the runtime is ~13 MB: onnxruntime-web is loaded by dynamic import inside loadSession and never at module scope, and the model is fetched from static/ rather than bundled. Verified absent from the client bundle. The artifact's sha256 is pinned and checked, because the contract's conclusions -- eval-mode export, lrt already decoded, rate heads in log1p space -- were read off that specific graph and a swapped model would make all of them wrong while everything kept running. Every site batches into one graph run, since dist_matrix, mds_coords and padding_mask are per-alignment; the reference driver loops one forward pass per codon. Other behaviours reproduced deliberately rather than improved: Max-PD seeds at alignment index 0 and can select duplicates, duplicate tip names resolve to the last leaf in preorder, MDS runs on the padded matrix so coordinates depend on max_species, and invariant sites are zeroed before the model is consulted. Findings that need an ML-side decision are written up separately. --- scripts/axomeme/verify_preprocessing.mjs | 193 ++++++++++ scripts/axomeme/verify_preprocessing.py | 125 +++++++ src/lib/services/axomeme/mds.js | 141 ++++++++ src/lib/services/axomeme/modelContract.js | 335 ++++++++++++++++++ src/lib/services/axomeme/newick.js | 222 ++++++++++++ src/lib/services/axomeme/patristic.js | 185 ++++++++++ src/lib/services/axomeme/postprocess.js | 218 ++++++++++++ src/lib/services/axomeme/session.js | 148 ++++++++ src/lib/services/axomeme/symmetricEigen.js | 227 ++++++++++++ src/lib/services/axomeme/tokenizer.js | 128 +++++++ src/test/axomeme-mds.test.js | 221 ++++++++++++ src/test/axomeme-model-contract.test.js | 246 +++++++++++++ src/test/axomeme-patristic.test.js | 265 ++++++++++++++ src/test/axomeme-postprocess.test.js | 231 ++++++++++++ src/test/axomeme-session.test.js | 171 +++++++++ src/test/axomeme-tokenizer.test.js | 184 ++++++++++ .../axomeme/axomeme_2.0_viral_finetuned.onnx | Bin 0 -> 3782234 bytes 17 files changed, 3240 insertions(+) create mode 100644 scripts/axomeme/verify_preprocessing.mjs create mode 100644 scripts/axomeme/verify_preprocessing.py create mode 100644 src/lib/services/axomeme/mds.js create mode 100644 src/lib/services/axomeme/modelContract.js create mode 100644 src/lib/services/axomeme/newick.js create mode 100644 src/lib/services/axomeme/patristic.js create mode 100644 src/lib/services/axomeme/postprocess.js create mode 100644 src/lib/services/axomeme/session.js create mode 100644 src/lib/services/axomeme/symmetricEigen.js create mode 100644 src/lib/services/axomeme/tokenizer.js create mode 100644 src/test/axomeme-mds.test.js create mode 100644 src/test/axomeme-model-contract.test.js create mode 100644 src/test/axomeme-patristic.test.js create mode 100644 src/test/axomeme-postprocess.test.js create mode 100644 src/test/axomeme-session.test.js create mode 100644 src/test/axomeme-tokenizer.test.js create mode 100644 static/models/axomeme/axomeme_2.0_viral_finetuned.onnx diff --git a/scripts/axomeme/verify_preprocessing.mjs b/scripts/axomeme/verify_preprocessing.mjs new file mode 100644 index 0000000..cbab363 --- /dev/null +++ b/scripts/axomeme/verify_preprocessing.mjs @@ -0,0 +1,193 @@ +/** + * Compare the JS patristic-distance port against the ML team's own Python, over real trees. + * + * This is the gate that makes the port trustworthy. The unit tests in + * src/test/axomeme-patristic.test.js check hand-computed values on trees small enough to verify by + * eye; this checks the SAME CODE against the reference implementation on real DataMonkey + * submissions, where the trees are large, unbalanced, occasionally malformed, and carry the negative + * branch lengths DM3's own NJ produces. + * + * Run verify_preprocessing.py first to produce the reference JSON. + * + * node scripts/axomeme/verify_preprocessing.mjs \ + * --reference /tmp/axomeme_reference.json \ + * [--tolerance 1e-9] + * + * Exits non-zero if any tree exceeds the tolerance, so it can be wired into CI the way + * scripts/prescreen/verify_parity.py is. Prints file ids, shapes and deltas only — never taxon + * names or sequence data, because the corpus is unpublished research data. + */ + +import { readFileSync } from 'node:fs'; +import { + parseNewick, + leafIndex, + normalizeTaxonName +} from '../../src/lib/services/axomeme/newick.js'; +import { patristicMatrix } from '../../src/lib/services/axomeme/patristic.js'; +import { computeMdsCoordinates } from '../../src/lib/services/axomeme/mds.js'; + +const arg = (flag, fallback) => { + const i = process.argv.indexOf(flag); + return i > 0 && process.argv[i + 1] ? process.argv[i + 1] : fallback; +}; + +const referencePath = arg('--reference'); +const tolerance = Number(arg('--tolerance', '1e-9')); +if (!referencePath) { + console.error('usage: verify_preprocessing.mjs --reference [--tolerance 1e-9]'); + process.exit(2); +} + +const reference = JSON.parse(readFileSync(referencePath, 'utf8')); +const entries = Object.entries(reference); +console.log(`[*] ${entries.length} reference matrices, tolerance ${tolerance}`); + +let checked = 0; +let cells = 0; +let worst = 0; +let worstFile = null; +const failures = []; +const skipped = []; + +/** + * MDS gets its own, much looser tolerance, and the reason is not slack. + * + * The coordinates are cast to float32 by the reference, so ~1e-7 relative is the floor no matter how + * carefully either side computes. On top of that, this is the one stage where two CORRECT + * implementations can legitimately disagree: numpy uses divide-and-conquer (dsyevd), this port uses + * implicit-shift QL, and inside a degenerate eigenspace any orthonormal basis is a valid answer that + * the reference's sign convention cannot disambiguate. So the number below is a measurement + * threshold, not a correctness proof — what matters is the DISTRIBUTION printed at the end. + */ +const mdsTolerance = Number(arg('--mds-tolerance', '1e-5')); +let mdsChecked = 0; +let mdsCells = 0; +let mdsWorstOverall = 0; +let mdsWorstFile = null; +const mdsFailures = []; + +for (const [path, ref] of entries) { + const id = path.split('/').pop(); + let tree; + try { + tree = parseNewick(readFileSync(path, 'utf8')); + } catch (e) { + skipped.push(`${id}: parse threw ${e.message}`); + continue; + } + + const { index } = leafIndex(tree); + // Order the rows exactly as the reference did, so cell (i,j) means the same pair on both sides. + const nodes = ref.names.map((n) => index.get(normalizeTaxonName(n))); + if (nodes.some((v) => v === undefined)) { + // A name the JS parser did not produce is a PARSE divergence, not a distance divergence, and + // it is a real failure — it means the two parsers disagree about what the taxa are. + const missing = nodes.filter((v) => v === undefined).length; + failures.push(`${id}: ${missing}/${ref.names.length} taxa missing from the JS parse`); + continue; + } + + const n = nodes.length; + const mine = patristicMatrix(tree, nodes); + let fileWorst = 0; + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) { + const d = Math.abs(mine[i * n + j] - ref.dist[i][j]); + if (d > fileWorst) fileWorst = d; + } + } + cells += n * n; + checked++; + if (fileWorst > worst) { + worst = fileWorst; + worstFile = `${id} (${n} taxa)`; + } + if (fileWorst > tolerance) { + failures.push(`${id}: ${n} taxa, max|Δ| ${fileWorst.toExponential(3)}`); + } + + // --- MDS, the piece whose agreement is not guaranteed by construction --- + if (!ref.mds) continue; + const cap = ref.max_species; + // The reference pads to max_species BEFORE the eigendecomposition, so the padded zeros take part + // in the double-centring. Build the same padded matrix rather than running MDS on the real taxa. + const padded = new Float64Array(cap * cap); + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) padded[i * cap + j] = mine[i * n + j]; + } + const coords = computeMdsCoordinates(padded, cap, 4); + let mdsWorst = 0; + let mdsWorstComponent = -1; + let mdsScale = 0; + for (let i = 0; i < cap; i++) { + for (let c = 0; c < 4; c++) { + const d = Math.abs(coords[i * 4 + c] - ref.mds[i][c]); + if (d > mdsWorst) { + mdsWorst = d; + mdsWorstComponent = c; + } + const s = Math.abs(ref.mds[i][c]); + if (s > mdsScale) mdsScale = s; + } + } + // GATE ON RELATIVE ERROR, against the largest coordinate in the whole matrix. + // + // An absolute threshold is the wrong instrument here and reads as a failure when nothing is wrong. + // The four components have wildly different scales — a measured 135-taxon tree runs 9.3e2, 2.2e2, + // 6.4e0, 2.1e-1 — because they carry eigenvalues seven orders of magnitude apart. A fixed absolute + // tolerance is simultaneously far too loose for component 0 and far too tight for component 3. + // What the model actually consumes is the vector as a whole, through one Linear layer, so error + // relative to the vector's own scale is the quantity that means something. + const mdsRel = mdsScale > 0 ? mdsWorst / mdsScale : 0; + mdsChecked++; + mdsCells += cap * 4; + if (mdsRel > mdsWorstOverall) { + mdsWorstOverall = mdsRel; + mdsWorstFile = `${id} (${n} taxa, component ${mdsWorstComponent}, abs ${mdsWorst.toExponential(2)})`; + } + if (mdsRel > mdsTolerance) { + mdsFailures.push( + `${id}: ${n} taxa -> ${cap}, rel ${mdsRel.toExponential(3)} ` + + `(abs ${mdsWorst.toExponential(3)}, scale ${mdsScale.toExponential(3)}) on component ${mdsWorstComponent}` + ); + } +} + +console.log(`\n--- patristic distances ---`); +console.log(`[*] compared ${checked} trees, ${cells.toLocaleString()} distance cells`); +console.log(`[*] worst |Δ| ${worst.toExponential(3)}${worstFile ? ` (${worstFile})` : ''}`); +if (skipped.length) { + console.log(`[!] ${skipped.length} skipped:`); + for (const s of skipped.slice(0, 5)) console.log(` ${s}`); +} + +if (mdsChecked) { + console.log(`\n--- MDS coordinates ---`); + console.log(`[*] compared ${mdsChecked} trees, ${mdsCells.toLocaleString()} coordinates`); + console.log( + `[*] worst RELATIVE Δ ${mdsWorstOverall.toExponential(3)}${mdsWorstFile ? ` (${mdsWorstFile})` : ''}` + ); + console.log( + `[*] float32 relative resolution is ~1.2e-7, so anything near that is the format, not the port` + ); + console.log( + `[*] ${mdsChecked - mdsFailures.length}/${mdsChecked} trees within ${mdsTolerance}` + + ` (${(((mdsChecked - mdsFailures.length) / mdsChecked) * 100).toFixed(1)}%)` + ); + if (mdsFailures.length) { + console.log(`[!] ${mdsFailures.length} over tolerance:`); + for (const f of mdsFailures.slice(0, 20)) console.log(` ${f}`); + } +} + +if (failures.length) { + console.log(`\n[!] ${failures.length} DISTANCE FAILURES:`); + for (const f of failures.slice(0, 20)) console.log(` ${f}`); + process.exit(1); +} +if (mdsFailures.length) { + console.log('\n[!] MDS PARITY NOT CLEAN'); + process.exit(1); +} +console.log('\n[*] PARITY OK'); diff --git a/scripts/axomeme/verify_preprocessing.py b/scripts/axomeme/verify_preprocessing.py new file mode 100644 index 0000000..a805731 --- /dev/null +++ b/scripts/axomeme/verify_preprocessing.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +""" +Emit REFERENCE patristic distances for a set of newick trees, for the JS port to be checked against. + +WHY THIS RUNS THE ML TEAM'S CODE RATHER THAN A TRANSCRIPTION OF IT. + +A parity harness that compares our JS against our Python re-implementation proves only that we made +the same mistake twice. So this does not reimplement anything: it extracts the SOURCE TEXT of +`calculate_patristic_distances` out of the handoff's predict_regression_nexus.py and execs it. What +runs is byte-for-byte their function. + +It is extracted rather than imported because importing that module pulls torch, pandas, scipy and +sklearn at module scope, none of which this comparison needs — and requiring a GPU-class dependency +tree to check a tree walk is how a parity gate stops being run. + +The companion is verify_preprocessing.mjs, which consumes this output. Neither script writes anything +into the repository, and neither prints taxon names or sequence data: real DataMonkey submissions are +unpublished research, so only file ids, shapes and aggregate deltas are reported. + +Usage: + python3 scripts/axomeme/verify_preprocessing.py \ + --handoff /path/to/predict_regression_nexus.py \ + --trees '/path/to/corpus/**/*.tre' \ + --out /tmp/axomeme_reference.json +""" + +import argparse +import glob +import json +import math +import re +import sys + +REFERENCE_FNS = ["calculate_patristic_distances", "compute_mds_coordinates"] + + +def extract_reference(handoff_path, fn_name): + """Pull one reference function's source out of the handoff, verbatim.""" + src = open(handoff_path, "r").read() + start = src.find(f"def {fn_name}(") + if start < 0: + sys.exit(f"[!] {fn_name} not found in {handoff_path}") + # Runs to the next top-level def/class. + rest = src[start:] + m = re.search(r"\n(?=(?:def |class )\w)", rest) + return rest[: m.start()] if m else rest + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--handoff", required=True, help="path to predict_regression_nexus.py") + ap.add_argument("--trees", required=True, help="glob for newick files") + ap.add_argument("--out", required=True) + ap.add_argument("--limit", type=int, default=0) + ap.add_argument( + "--max-species", + type=int, + default=512, + help="pad the distance matrix to this size before MDS, as the driver does (default 512)", + ) + args = ap.parse_args() + + from Bio import Phylo # noqa: E402 + import numpy as np # noqa: E402 + + ns = {"math": math, "np": np} + for fn in REFERENCE_FNS: + body = extract_reference(args.handoff, fn) + exec(compile(body, "", "exec"), ns) + print(f"[*] Extracted {fn} ({body.count(chr(10))} lines) from {args.handoff}") + reference = ns["calculate_patristic_distances"] + reference_mds = ns["compute_mds_coordinates"] + + paths = sorted(glob.glob(args.trees, recursive=True)) + if args.limit: + paths = paths[: args.limit] + print(f"[*] {len(paths)} tree files") + + out, crashed, unreadable = {}, [], [] + for p in paths: + try: + tree = Phylo.read(p, "newick") + except Exception as ex: + unreadable.append((p, type(ex).__name__)) + continue + try: + names, dist, _ = reference(tree) + except Exception as ex: + # EXPECTED for a real slice of the corpus, and worth counting rather than skipping: the + # reference computes log((node_count + 1.0) / (dist + 0.1)) unconditionally, so any + # patristic distance <= -0.1 is a math domain error. DM3's own NJ emits the negative + # branch lengths that produce those. + crashed.append((p, type(ex).__name__, str(ex)[:80])) + continue + names = [n for n in names if n] + # The driver pads the distance matrix to max_species BEFORE running MDS, so the padded zeros + # take part in the double-centring and the coordinates depend on max_species. Reproduce that. + # Trees larger than the cap would go through Max-PD selection in the driver; here they are + # simply truncated, identically on both sides, because Max-PD is covered by unit tests and + # mixing the two would make a failure ambiguous. + names = names[: args.max_species] + n = args.max_species + padded = np.zeros((n, n), dtype=np.float32) + for i, a in enumerate(names): + row = dist.get(a, {}) + for j, b in enumerate(names): + padded[i, j] = row.get(b, 0.0) + coords = reference_mds(padded, n_components=4) + out[p] = { + "names": names, + "dist": [[dist[a].get(b, 0.0) for b in names] for a in names], + "max_species": n, + "mds": [[float(v) for v in row] for row in coords], + } + + json.dump(out, open(args.out, "w")) + print(f"[*] wrote {len(out)} reference matrices to {args.out}") + print(f"[*] unreadable by Bio.Phylo: {len(unreadable)}") + print(f"[*] REFERENCE CRASHED on {len(crashed)} trees ({100.0 * len(crashed) / max(1, len(paths)):.1f}%)") + for p, kind, msg in crashed[:5]: + print(f" {p.split('/')[-1]}: {kind}: {msg}") + + +if __name__ == "__main__": + main() diff --git a/src/lib/services/axomeme/mds.js b/src/lib/services/axomeme/mds.js new file mode 100644 index 0000000..d920ec2 --- /dev/null +++ b/src/lib/services/axomeme/mds.js @@ -0,0 +1,141 @@ +/** + * mds.js — classical multidimensional scaling, matching predict_regression_nexus.py:161-187. + * + * This produces `mds_coords`, one of the five ONNX graph inputs, and it is the LAST piece of the + * preprocessing port and the only one whose agreement with Python cannot be guaranteed by + * construction. Everything else — newick parse, patristic distances, tokenisation — is exact + * arithmetic over discrete choices. This one runs an eigendecomposition, and eigenvectors are unique + * only up to sign, and inside a degenerate eigenspace not even that. + * + * THE REFERENCE, step for step: + * 1. N <= n_components -> all-zero coordinates, early return. + * 2. cast to float64; D2 = D ** 2 + * 3. H = I - ones(N,N)/N ; B = -0.5 * (H @ D2 @ H) + * 4. evals, evecs = np.linalg.eigh(B) (ascending) + * 5. reorder DESCENDING by eigenvalue + * 6. sign convention: per column, if the largest-magnitude entry is negative, negate the column + * 7. coords[:, i] = evecs[:, i] * sqrt(evals[i]) when evals[i] > 0, else left at zero + * 8. cast to float32 + * + * STEP 6 IS THE GOOD NEWS. The reference already canonicalises eigenvector signs — "largest absolute + * value element is positive". That was going to be an ask to the ML team and turned out to be + * already there, which removes the sign half of the ambiguity for free. What remains is degeneracy: + * when two eigenvalues are equal, any orthonormal basis of their shared eigenspace is a correct + * answer, the sign rule does not disambiguate a rotation, and numpy's divide-and-conquer and our QL + * iteration may land on different bases. That is measured by the parity harness, not argued about + * here. + * + * ONE PLACE THIS DELIBERATELY DOES NOT COPY THE REFERENCE'S ARITHMETIC. The reference forms B with + * two dense matrix multiplications by H. Since H = I - J/N, that product is exactly the + * double-centring identity + * B[i][j] = -0.5 * (D2[i][j] - rowMean[i] - colMean[j] + grandMean) + * so this computes it directly: same value, two O(n^2) passes instead of two O(n^3) matmuls. Bit + * parity with numpy was never available anyway — its matmul is blocked BLAS with FMA, which no + * hand-written loop reproduces — and the output is cast to float32, which is ~1e-7 relative and + * swallows the difference. The harness confirms this rather than assuming it. + * + * MDS RUNS ON THE PADDED MATRIX. The caller must hand this the full [max_species, max_species] + * matrix, zeros included, because the reference does: the padded region participates in the + * double-centring, so coordinates depend on max_species and not only on the real taxa. Running MDS + * on the real N and padding afterwards produces different numbers. See modelContract.js. + */ + +import { symmetricEigen } from './symmetricEigen.js'; +import { MDS_COMPONENTS } from './modelContract.js'; + +/** + * Classical MDS coordinates for a distance matrix. + * + * @param {Float64Array|number[]} dist row-major n*n distance matrix (the PADDED one) + * @param {number} n + * @param {number} [nComponents] + * @returns {Float32Array} n * nComponents, row-major — float32 to match the reference's final cast + */ +export function computeMdsCoordinates(dist, n, nComponents = MDS_COMPONENTS) { + const coords = new Float32Array(n * nComponents); + // `if N <= n_components: return zeros`. Note <=, not <: a 4-taxon alignment with 4 components + // gets all-zero coordinates, which is the reference's behaviour and not an edge case to improve. + if (n <= nComponents) return coords; + + // D2 = D ** 2, WITH THE DISTANCES FIRST ROUNDED TO FLOAT32. + // + // That rounding is not a detail. The reference receives `dist_tensor.numpy()`, and dist_tensor is + // `torch.zeros(max_species, max_species, dtype=torch.float32)` — so the values it squares are + // float32, widened back to float64 by its own `.astype(np.float64)` on the very next line. Passing + // full float64 distances here produces a DIFFERENT MDS, and not subtly: + // + // Real distance matrices are wildly ill-conditioned for this purpose. On a measured 135-taxon + // DM3 tree the eigenvalues run 1.26e7, 1.52e5, 1.30e2, 2.99e-1 — seven orders of magnitude from + // first to fourth. Squared distances reach ~1e6, so a float32 rounding of ~1e-7 relative is an + // ABSOLUTE perturbation of ~0.1 in D2, which is comparable to the fourth eigenvalue itself. + // Components 2 and 3 then differ by 40-99%. Feeding float64 here disagreed with the reference on + // 5 of 270 real trees; rounding to float32 first is what makes them agree. + // + // Squaring also makes negative distances positive, so a negative branch length does not break MDS + // the way it breaks the reference's density term — it silently becomes a positive distance of the + // same magnitude. Worth knowing when reading coordinates from a bad tree. + const D2 = new Float64Array(n * n); + for (let i = 0; i < n * n; i++) { + const v = Math.fround(dist[i]); + D2[i] = v * v; + } + + // Double-centre: B = -0.5 * (H @ D2 @ H), computed via row/column means. D2 is symmetric so the + // column means equal the row means, but they are computed separately anyway — the input is only + // assumed symmetric, and an asymmetric one should degrade predictably rather than silently. + const rowMean = new Float64Array(n); + const colMean = new Float64Array(n); + let grand = 0; + for (let i = 0; i < n; i++) { + let s = 0; + for (let j = 0; j < n; j++) s += D2[i * n + j]; + rowMean[i] = s / n; + grand += s; + } + grand /= n * n; + for (let j = 0; j < n; j++) { + let s = 0; + for (let i = 0; i < n; i++) s += D2[i * n + j]; + colMean[j] = s / n; + } + + const B = new Float64Array(n * n); + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) { + B[i * n + j] = -0.5 * (D2[i * n + j] - rowMean[i] - colMean[j] + grand); + } + } + + const { values, vectors } = symmetricEigen(B, n); + + // eigh returns ascending; the reference reorders descending. Only the top nComponents are used, + // but the SIGN CONVENTION in the reference runs over every column, so it is applied per component + // as they are read rather than to the whole matrix — same result, nComponents passes instead of n. + for (let c = 0; c < nComponents; c++) { + const src = n - 1 - c; // descending order + const value = values[src]; + if (!(value > 0)) continue; // `if val > 0` — non-positive components stay zero + + // Sign convention: the largest-magnitude entry of the column must be positive. np.argmax + // takes the FIRST maximum on a tie, so `>` and not `>=` here. + let maxAbs = -1; + let maxIdx = 0; + for (let i = 0; i < n; i++) { + const a = Math.abs(vectors[i * n + src]); + if (a > maxAbs) { + maxAbs = a; + maxIdx = i; + } + } + // np.sign(0) is 0, and `if sign < 0` is then false — an all-zero column is left alone rather + // than negated. Matching that matters only for degenerate input, but it is free to match. + const flip = vectors[maxIdx * n + src] < 0 ? -1 : 1; + + const scale = Math.sqrt(value); + for (let i = 0; i < n; i++) { + coords[i * nComponents + c] = Math.fround(flip * vectors[i * n + src] * scale); + } + } + + return coords; +} diff --git a/src/lib/services/axomeme/modelContract.js b/src/lib/services/axomeme/modelContract.js new file mode 100644 index 0000000..2e9dbb2 --- /dev/null +++ b/src/lib/services/axomeme/modelContract.js @@ -0,0 +1,335 @@ +/** + * modelContract.js — what the AxoMEME 2.0 ONNX graph accepts and returns. + * + * WHY THIS FILE EXISTS, AND WHY IT IS CODE RATHER THAN A README. + * + * The exported graph is the MODEL ONLY. It takes five already-computed tensors and returns five; + * the entire preprocessing pipeline that produces those tensors — newick parse, patristic + * distances, taxon subsampling, codon/AA tokenisation, and the MDS eigendecomposition — is NOT in + * the graph and has to be rebuilt in JS. `mds_coords` is a graph INPUT, not something the graph + * computes. (`torch.linalg.eigh` has no ONNX lowering, so this is forced rather than chosen: + * exporting the identical module with eigh removed succeeds and with it present fails.) + * + * That means every number below is a place a JS port can be silently, plausibly wrong — producing + * a well-formed tensor of the right shape that means something different from what the model was + * trained on. Several of them are genuinely surprising, so they are constants here with citations + * rather than assumptions in someone's head: + * + * - CODON ORDER IS "TCAG", NOT ALPHABETICAL. The vocabulary is built as + * `[a+b+c for a in "TCAG" for b in "TCAG" for c in "TCAG"]`, the standard genetic-code table + * order. An ACGT-ordered vocabulary is a perfectly valid-looking permutation of the same 64 + * tokens and would be wrong at every site. THE HANDOFF'S OWN INFERENCE DRIVER GETS THIS WRONG — + * see the next paragraph before "correcting" anything here to match it. + * + * THE DRIVER'S TOKENIZER DISAGREES WITH THE TRAINING TOKENIZER. This is measured, not suspected. + * + * train_transformer_selection.py:74-85 (TRAINING) 64 codons, TCAG order, '-'->64, '?'->65 + * predict_regression_nexus.py:49-80 (INFERENCE) 60 codons, ALPHABETICAL, everything else ->65 + * + * The driver defines its own `CODON_LIST` / `CODON_TO_IDX` at lines 49-55, then redefines + * `get_codon_token` at line 74 — but never redefines `CODON_TO_IDX`, so the winning function looks up + * the 60-codon alphabetical map. It imports only `PhyloAxialTransformer`, + * `compute_mds_coordinates` and `decode_soft_ordinal_lrt` from the training module (line 28), so the + * training tokenizer is never in scope. Result, checked over all 64 codons: + * + * 63 of 64 codons receive a DIFFERENT token at inference than at training + * ATG: train 35 -> inference 14 TTT: train 0 -> inference 59 + * AAA: train 42 -> inference 0 GGG: train 63 -> inference 42 + * TTA (Leucine) and all three stop codons are ABSENT from the driver's list entirely, so they + * collapse to 65 "unknown" — real leucine data is discarded rather than mistokenised. + * The amino-acid stream is nearly intact: 3 of 64 wrong, the stops, because the driver's + * GENETIC_CODE writes them as '_' while AA_LIST contains '*', so they land on 22 instead of 20. + * + * DM3 USES THE TRAINING VOCABULARY, which is what this file pins. A model trained on TCAG-64 has to + * be served TCAG-64; there is no reading under which the driver's map is the right one for these + * weights. Reproducing the driver here would reproduce a bug. + * + * Note what this does NOT invalidate: the ML team's ONNX-vs-PyTorch parity result. That test feeds + * the same tensors to both sides, so it proves the graph was exported faithfully and is unaffected + * by which tokenizer produced those tensors. The bug is invisible to it by construction — which is + * exactly why it survived to here. + * - GAP AND UNKNOWN ARE DIFFERENT TOKENS, and they differ between the two streams: codons use + * 64/65, amino acids use 21/22. They are not interchangeable — `forward()` gates on + * `(c_cent < 64) & (a_cent < 21)`, so a gap counts as invalid at the central site. + * - MDS IS COMPUTED ON THE PADDED MATRIX. `compute_mds_coordinates` is handed the full + * [max_species, max_species] tensor, zeros included, so the padded region participates in the + * double-centring and the coordinates depend on max_species — not just on the real taxa. A port + * that runs MDS on the N real species and pads afterwards gets different numbers. + * - THE THREE PHYLO TENSORS ARE SITE-INVARIANT. dist_matrix, mds_coords and padding_mask are + * computed once and `expand`ed across sites. They are per-alignment, not per-site, which is what + * makes batching every site into one graph run cheap. + * + * Sources, all in the ML team's handoff (axomeme-2.0-viral-handoff/scripts/): + * predict_regression_nexus.py:161-187 compute_mds_coordinates + * predict_regression_nexus.py:1213-1229 tensor allocation, pad tokens, MDS on the padded matrix + * predict_regression_nexus.py:1271-1275 the site-invariant expands + * predict_regression_nexus.py:981-982 window_size / max_species defaults + * train_transformer_selection.py:73-97 codon + amino acid vocabularies + * train_transformer_selection.py:1591 forward(msa_codons, msa_aas, dist_matrix, mds_coords, padding_mask) + * + * This module is deliberately a LEAF: it imports nothing, computes nothing, and holds no model. It + * describes and it validates. Nothing here should ever pull in onnxruntime-web — the runtime is + * loaded on the AxoMEME path only, and the reachability guard in + * src/test/meme-hit-likelihood.test.js exists to keep that true. + */ + +/** Codon vocabulary order. NOT alphabetical — see the header. */ +export const CODON_ORDER = 'TCAG'; + +/** 64 sense+stop codons occupy 0..63; these two are the sentinels. */ +export const CODON_GAP = 64; +export const CODON_UNKNOWN = 65; + +/** `num_tokens=66` is passed to the model constructor: 64 codons + gap + unknown. */ +export const NUM_CODON_TOKENS = 66; + +/** Amino acid vocabulary, index = position in this string. */ +export const AA_LIST = 'ACDEFGHIKLMNPQRSTVWY*-?'; +export const AA_GAP = 21; // AA_LIST.indexOf('-') +export const AA_UNKNOWN = 22; // AA_LIST.indexOf('?') + +/** + * `forward()` treats a species as present at the central site only if + * `(codon < 64) & (aa < 21) & !padded`. So a gap is NOT a valid observation, and these are the + * thresholds — not `<= 64` / `<= 21`. + */ +export const CODON_VALID_BELOW = 64; +export const AA_VALID_BELOW = 21; + +/** `--max_species` default. Tensors are padded to exactly this many rows. */ +export const MAX_SPECIES_DEFAULT = 512; + +/** + * `--window_size` default, and the value this checkpoint was fine-tuned at. The model reads the + * CENTRAL column (`window_size // 2`), so an even window would shift which codon is scored. + */ +export const WINDOW_SIZE_DEFAULT = 1; + +/** `compute_mds_coordinates(..., n_components=4)`. */ +export const MDS_COMPONENTS = 4; + +/** + * `padding_mask` is TRUE for padded rows — the opposite of the "1 = keep" convention used in most + * attention APIs, and a sign flip here silently masks out every real taxon instead of none. + */ +export const PADDING_MASK_TRUE_MEANS_PADDED = true; + +/** + * The five graph inputs, in `forward()` order. + * + * `dims` uses the symbolic names the export declares dynamic (batch, num_species); everything else + * is fixed by the checkpoint. int64 matters: onnxruntime-web wants a BigInt64Array for these, and + * passing Float32Array of the same values is a type error at session.run, not a silent coercion. + */ +export const INPUT_SPEC = Object.freeze([ + Object.freeze({ + name: 'msa_codons', + dtype: 'int64', + dims: ['batch', 'num_species', 'window_size'], + valueRange: [0, CODON_UNKNOWN], + padValue: CODON_UNKNOWN, + siteInvariant: false + }), + Object.freeze({ + name: 'msa_aas', + dtype: 'int64', + dims: ['batch', 'num_species', 'window_size'], + valueRange: [0, AA_UNKNOWN], + padValue: AA_UNKNOWN, + siteInvariant: false + }), + Object.freeze({ + name: 'dist_matrix', + dtype: 'float32', + dims: ['batch', 'num_species', 'num_species'], + siteInvariant: true + }), + Object.freeze({ + name: 'mds_coords', + dtype: 'float32', + dims: ['batch', 'num_species', 'mds_components'], + siteInvariant: true + }), + Object.freeze({ + name: 'padding_mask', + dtype: 'bool', + dims: ['batch', 'num_species'], + siteInvariant: true + }) +]); + +/** + * The five graph outputs, in graph order. RESOLVED against the artifact itself — these names were + * read out of `InferenceSession.outputNames` on axomeme_2.0_viral_finetuned.onnx + * (sha256 3e06b591…6faec6), not from documentation. + * + * THE EXPORT IS EVAL MODE. This was an open question worth recording, because the shipped driver + * switches the module to `model.train()` before every forward pass purely to reach the branch that + * returns RAW ORDINAL LOGITS, so it can apply `--prior_shift` and call `decode_soft_ordinal_lrt` + * itself (predict_regression_nexus.py:1354-1366). The answer is that the graph took the EVAL branch: + * five outputs, `lrt` already decoded. Two consequences for the JS side: + * + * - JS does NOT implement `decode_soft_ordinal_lrt`. The graph did it. + * - `prior_shift` is baked to 0 and is not reachable. If a calibration shift is ever wanted, it has + * to be re-exported, not applied here. + * + * STILL OPEN, and it belongs to POSTPROCESSING rather than to this contract: the driver applies + * `np.expm1` to alpha / beta_neg / beta_pos before writing its CSV. Those heads are `F.softplus(...)`, + * and expm1(softplus(x)) == exp(x), so the model is predicting log1p(rate) and expm1 recovers the + * rate — meaning the raw graph outputs are NOT rates and must not be shown as such. `p_neg` is + * sigmoid in-graph and is already a probability. Whether `lrt` needs a further exp is unresolved: the + * driver emits both `predicted_log_lrt` and `predicted_lrt` columns, and which one this output + * corresponds to has to be checked against sample_predictions.csv before anything is rendered. + */ +export const OUTPUT_SPEC = Object.freeze([ + Object.freeze({ + name: 'lrt', + note: 'MEME LRT surrogate, ordinal decode already applied in-graph (eval-mode export)' + }), + Object.freeze({ name: 'alpha', note: 'dS as log1p(rate); caller applies expm1' }), + Object.freeze({ name: 'beta_neg', note: 'as log1p(rate); caller applies expm1' }), + Object.freeze({ name: 'beta_pos', note: 'dN+ as log1p(rate); caller applies expm1' }), + Object.freeze({ name: 'p_neg', note: 'already sigmoid in-graph; a probability as-is' }) +]); + +/** + * The artifact this contract was verified against. A different export is not necessarily wrong, but + * it is not this one, and the eval-mode conclusion above was read off THIS graph. + */ +export const VERIFIED_MODEL_SHA256 = + '3e06b591a060fca996a41c040c2c29f319aa47ca3d3401f4757571b57e6faec6'; + +/** Every input name, in graph order. */ +export const INPUT_NAMES = Object.freeze(INPUT_SPEC.map((s) => s.name)); + +/** + * Check a bundle of prepared tensors against the contract, without running anything. + * + * This is the cheap half of parity: it cannot tell you the MDS coordinates are RIGHT — only real + * fixtures from the Python pipeline can do that — but it catches the whole class of errors that + * produce a well-formed tensor with the wrong meaning, which is the class a JS port actually + * generates. Returns every problem it finds rather than throwing on the first, because a port under + * development usually has several and stopping at one wastes a round trip. + * + * @param {Record, dims: number[]}>} bundle + * @param {{batch: number, numSpecies: number, windowSize?: number, mdsComponents?: number}} shape + * @returns {{ok: boolean, errors: string[]}} + */ +export function validateInputBundle(bundle, shape) { + const errors = []; + const { batch, numSpecies } = shape; + const windowSize = shape.windowSize ?? WINDOW_SIZE_DEFAULT; + const mdsComponents = shape.mdsComponents ?? MDS_COMPONENTS; + + const expected = { + msa_codons: [batch, numSpecies, windowSize], + msa_aas: [batch, numSpecies, windowSize], + dist_matrix: [batch, numSpecies, numSpecies], + mds_coords: [batch, numSpecies, mdsComponents], + padding_mask: [batch, numSpecies] + }; + + for (const spec of INPUT_SPEC) { + const t = bundle[spec.name]; + if (!t) { + errors.push(`${spec.name}: missing`); + continue; + } + const want = expected[spec.name]; + const got = Array.from(t.dims ?? []); + if (got.length !== want.length || got.some((d, i) => d !== want[i])) { + errors.push(`${spec.name}: dims [${got}], expected [${want}]`); + continue; + } + const want_n = want.reduce((a, b) => a * b, 1); + if (t.data.length !== want_n) { + errors.push( + `${spec.name}: ${t.data.length} elements for dims [${want}] (expected ${want_n})` + ); + continue; + } + if (spec.valueRange) { + const [lo, hi] = spec.valueRange; + for (let i = 0; i < t.data.length; i++) { + const v = Number(t.data[i]); + if (!Number.isInteger(v) || v < lo || v > hi) { + errors.push(`${spec.name}[${i}] = ${v}, outside ${lo}..${hi}`); + break; + } + } + } + } + + // Cross-tensor invariants — the ones that are individually well-formed but jointly wrong. + const pad = bundle.padding_mask; + const dist = bundle.dist_matrix; + if (pad && dist && pad.data.length === batch * numSpecies) { + // THE MASK-POLARITY CHECK. `padding_mask` is TRUE for padded rows, the opposite of the + // "1 = keep" convention most attention APIs use, and an inverted mask is well-formed in every + // other respect — right dtype, right dims, right element count. What gives it away is the + // DISTANCE MATRIX: padded rows are never written, so they stay all-zero, while real rows + // carry real distances. Invert the mask and rows holding genuine distances get marked padded. + // + // An earlier version of this checked only self-distances and only the all-padded case. It did + // not fire on a partial flip, which is the flip that actually happens — some rows stay + // unpadded and every self-distance is legitimately zero either way. + let flagged = false; + for (let b = 0; b < batch && !flagged; b++) { + for (let i = 0; i < numSpecies && !flagged; i++) { + const padded = Boolean(pad.data[b * numSpecies + i]); + if (!padded) continue; + const row = b * numSpecies * numSpecies + i * numSpecies; + for (let j = 0; j < numSpecies; j++) { + const v = Number(dist.data[row + j]); + if (v !== 0) { + errors.push( + `padding_mask: species ${i} is marked padded but has distance ${v} to species ` + + `${j} — padded rows are never written, so this is a mask polarity error ` + + '(the convention is TRUE = PADDED)' + ); + flagged = true; + break; + } + } + } + } + const real = Array.from(pad.data.slice(0, numSpecies)).filter((v) => !v).length; + if (real === 0) { + errors.push('padding_mask: every species is marked padded — no taxa would reach the model'); + } + } + + if (dist) { + // d(i,i) = 0 by definition. A nonzero diagonal means whatever was built is not a distance + // matrix — most often an adjacency or a similarity matrix that took the same code path. + for (let b = 0; b < batch; b++) { + for (let i = 0; i < numSpecies; i++) { + const self = Number(dist.data[b * numSpecies * numSpecies + i * numSpecies + i]); + if (self !== 0) { + errors.push(`dist_matrix: self-distance d(${i},${i}) is ${self}, expected 0`); + b = batch; + break; + } + } + } + } + + if (dist) { + for (let i = 0; i < dist.data.length; i++) { + const v = Number(dist.data[i]); + if (!Number.isFinite(v)) { + errors.push(`dist_matrix[${i}] is ${v}`); + break; + } + if (v < 0) { + // Not a shape error — a real one. DM3's own NJ emits negative branch lengths and the + // Python inference path throws on them rather than degrading; see + // src/lib/utils/treeSanitation.js for the measurement and the crash site. + errors.push(`dist_matrix[${i}] = ${v} — negative patristic distance`); + break; + } + } + } + + return { ok: errors.length === 0, errors }; +} diff --git a/src/lib/services/axomeme/newick.js b/src/lib/services/axomeme/newick.js new file mode 100644 index 0000000..1167a6e --- /dev/null +++ b/src/lib/services/axomeme/newick.js @@ -0,0 +1,222 @@ +/** + * newick.js — a minimal newick parser for the AxoMEME preprocessing path. + * + * WHY NOT phylotree, WHICH THIS REPO ALREADY DEPENDS ON. phylotree is a rendering library: it pulls + * d3, it is built for interactive branch selection, and it is used in exactly one place + * (BranchSelector.svelte). What is needed here is the opposite of that — a leaf module with no + * dependencies that produces a tree whose branch lengths and names match, exactly, what Biopython's + * `Phylo.read` hands to the Python reference implementation. Parity is the whole job, and it is much + * easier to defend against a 100-line parser whose semantics are written down than against a + * general-purpose library that also happens to draw SVG. + * + * WHAT IT DELIBERATELY MATCHES (predict_regression_nexus.py:895-905): + * - A MISSING BRANCH LENGTH IS 0.0, not null and not NaN. The reference walks with + * `current_dist + (child.branch_length or 0.0)`, so an absent length contributes nothing rather + * than poisoning every descendant. Note this also maps a length of exactly 0.0 to 0.0, so the + * `or` is harmless there — but a NEGATIVE length is preserved, which matters: DM3's own NJ emits + * them (see src/lib/utils/treeSanitation.js) and silently clamping here would hide that. + * - INTERNAL NODES KEEP THEIR LABELS but are never leaves. A bootstrap value like `)95:0.05` is a + * label on an internal node, and reading it as a taxon name would invent a species. + * + * WHAT IT DELIBERATELY DOES NOT DO: it does not clamp, normalise, or repair anything. A tree that is + * wrong arrives wrong, so the caller can refuse it with the user told rather than scoring a quietly + * altered tree. + */ + +/** + * Strip a quoted or bare newick label down to the name the reference pipeline compares against. + * + * The reference normalises with `s.replace("'", "").replace('"', '').strip()` — Python's str.replace + * removes EVERY occurrence, not just the first — and does it at comparison time + * (predict_regression_nexus.py:1221-1222). Quotes are removed wherever they appear, not just at the + * ends, so a label like `Homo_'sapiens'` and `Homo_sapiens` normalise to the same taxon. + * + * @param {string} name + * @returns {string} + */ +export function normalizeTaxonName(name) { + return String(name).replaceAll("'", '').replaceAll('"', '').trim(); +} + +/** + * Parse a newick string. + * + * Returns a flat node array plus the root index, rather than a linked object graph. That shape is + * chosen on purpose: every consumer here walks the tree by index (root distances, LCA, Max-PD), flat + * typed arrays keep those walks allocation-free, and there are no parent cycles for a structured + * clone or a test's `toEqual` to trip over. + * + * @param {string} text + * @returns {{ + * name: string[], parent: Int32Array, branchLength: Float64Array, + * children: number[][], depth: Int32Array, root: number, leaves: number[] + * }} + */ +export function parseNewick(text) { + if (typeof text !== 'string' || !text.trim()) { + throw new Error('parseNewick: empty input'); + } + + // Strip newick comments before tokenising. `[&&NHX:...]` and `[100]` both appear in the wild and + // neither is part of the topology; leaving them in makes a comment body look like a label. + const src = text.replace(/\[[^\]]*\]/g, ''); + + const name = []; + const parent = []; + const branchLength = []; + const children = []; + + const newNode = (parentIdx) => { + const idx = name.length; + name.push(''); + parent.push(parentIdx); + branchLength.push(0); + children.push([]); + if (parentIdx >= 0) children[parentIdx].push(idx); + return idx; + }; + + let i = 0; + const n = src.length; + const skipSpace = () => { + while (i < n && /\s/.test(src[i])) i++; + }; + + /** Read a label: quoted (quotes preserved, caller normalises) or bare up to a delimiter. */ + const readLabel = () => { + skipSpace(); + if (i >= n) return ''; + const q = src[i]; + if (q === "'" || q === '"') { + // A quoted label may legally contain ':' ',' '(' ')' — which is the entire reason quoting + // exists, and the reason a naive split on ':' corrupts these names. + let out = q; + i++; + while (i < n && src[i] !== q) out += src[i++]; + if (i < n) out += src[i++]; // closing quote + return out; + } + let out = ''; + while (i < n && !'(),:;'.includes(src[i])) out += src[i++]; + return out.trim(); + }; + + /** Read `:length` if present. Absent means 0, matching `branch_length or 0.0`. */ + const readLength = (node) => { + skipSpace(); + if (src[i] !== ':') return; + i++; + skipSpace(); + let out = ''; + while (i < n && !'(),;'.includes(src[i])) out += src[i++]; + const v = parseFloat(out); + // A malformed length is 0, not NaN. NaN would propagate silently through every descendant's + // root distance and surface much later as an all-NaN model input. + branchLength[node] = Number.isFinite(v) ? v : 0; + }; + + // Iterative descent. A recursive parser is simpler but a 5,000-taxon ladder tree is 5,000 frames + // deep, and the reference implementation has exactly that latent bug (Python's traverse() is + // recursive and caps at ~1,000 frames). + const root = newNode(-1); + let current = root; + skipSpace(); + + if (src[i] === '(') { + i++; + current = newNode(root); + while (i < n) { + skipSpace(); + const c = src[i]; + if (c === '(') { + i++; + current = newNode(current); + } else if (c === ',') { + i++; + current = newNode(parent[current]); + } else if (c === ')') { + i++; + current = parent[current]; + name[current] = readLabel(); // internal label / bootstrap + readLength(current); + if (current === root) break; + } else if (c === ';' || i >= n) { + break; + } else { + name[current] = readLabel(); + readLength(current); + } + } + } else { + // A bare single-taxon "tree". + name[root] = readLabel(); + readLength(root); + } + + const count = name.length; + const depth = new Int32Array(count); + const leaves = []; + // PREORDER, depth-first, left to right — matching Biopython's `tree.get_terminals()`, which is + // `find_clades(terminal=True, order='preorder')`. The order is not cosmetic: the reference builds + // its name lookup as `{leaf.name: leaf for leaf in leaves}`, so on a tree with duplicate tip names + // the LAST leaf in THIS order wins, and 1.1% of real DM3 trees have duplicate tips. A + // breadth-first traversal produces the same set and a different winner. + const stack = [root]; + depth[root] = 0; + while (stack.length) { + const node = stack.pop(); + if (children[node].length === 0) { + leaves.push(node); + continue; + } + // Reversed, so the leftmost child is popped first and the walk is genuinely left-to-right. + for (let k = children[node].length - 1; k >= 0; k--) { + const c = children[node][k]; + depth[c] = depth[node] + 1; + stack.push(c); + } + } + + return { + name, + parent: Int32Array.from(parent), + branchLength: Float64Array.from(branchLength), + children, + depth, + root, + leaves + }; +} + +/** + * Map normalised taxon name -> node index, for the named leaves only. + * + * A DUPLICATE NAME RESOLVES TO THE LAST LEAF IN PREORDER, matching the reference's + * `{leaf.name: leaf for leaf in leaves if leaf.name}` — a dict comprehension, so later entries + * overwrite earlier ones. + * + * This was originally written the other way round, keeping the first leaf, on the reasoning that a + * tree with two identically-named tips is malformed and reproducing a dict-overwrite artifact was + * not worth doing. Measurement settled it: 3 of 270 real DM3 trees (1.1%) have duplicate tips — one + * of them has only 36 distinct names across 100 tips — and on every one of those the two policies + * pick different tips and therefore compute different distances. The parity run failed on exactly + * those three and nothing else. + * + * So this layer matches the reference, because its whole job is fidelity to what the model was + * trained and served on, and `duplicates` is returned so the CALLER can refuse the tree. Refusing is + * a product decision; quietly disagreeing with the reference is not. + * + * @param {ReturnType} tree + * @returns {{index: Map, duplicates: string[]}} + */ +export function leafIndex(tree) { + const index = new Map(); + const duplicates = []; + for (const leaf of tree.leaves) { + const nm = normalizeTaxonName(tree.name[leaf]); + if (!nm) continue; + if (index.has(nm)) duplicates.push(nm); + index.set(nm, leaf); // last wins + } + return { index, duplicates }; +} diff --git a/src/lib/services/axomeme/patristic.js b/src/lib/services/axomeme/patristic.js new file mode 100644 index 0000000..e639da2 --- /dev/null +++ b/src/lib/services/axomeme/patristic.js @@ -0,0 +1,185 @@ +/** + * patristic.js — pairwise tree distances and Max-PD taxon selection. + * + * This is the first half of the AxoMEME preprocessing port, and the half that CAN be made + * bit-identical to the Python reference. It is plain float arithmetic over a tree walk: no + * eigendecomposition, no library-version-dependent LAPACK, nothing whose result is only unique up to + * a sign. That is why it comes first — it establishes the parity harness on the part of the pipeline + * where a mismatch is unambiguously a bug rather than a convention difference. + * + * THE REFERENCE (predict_regression_nexus.py:895-965, 1164-1188): + * d(a, b) = rootDist(a) + rootDist(b) - 2 * rootDist(lca(a, b)) + * computed over root distances accumulated as `current_dist + (child.branch_length or 0.0)`. + * + * ONE DELIBERATE DIVERGENCE, AND IT CHANGES NO RESULT. The reference materialises the full N x N + * matrix before Max-PD (`D_full = np.zeros((N_full, N_full))`). Max-PD is a farthest-point traversal + * that only ever reads ONE ROW per iteration, so this port computes rows on demand: `max_species + 1` + * rows instead of N^2 cells. For a 5,000-taxon submission that is ~10 MB instead of ~100 MB, which is + * the difference between running in a browser tab and not. The selected taxa are identical because + * the values are identical — only the cells that are never read go uncomputed. + * + * NEGATIVE DISTANCES ARE PRESERVED, NOT CLAMPED. DM3's own NJ emits negative branch lengths and 5% of + * real DM3 trees carry one at or past -0.1; the Python inference path throws on those rather than + * degrading. Clamping here would convert a loud failure into a quiet wrong answer. See + * src/lib/utils/treeSanitation.js for the measurement and the crash site. + */ + +/** + * Distance from the root to every node, accumulated down the tree. + * + * @param {import('./newick.js').default | any} tree from parseNewick + * @returns {Float64Array} + */ +export function rootDistances(tree) { + const dist = new Float64Array(tree.name.length); + // Explicit stack rather than recursion: a ladder-shaped tree is as deep as it is wide, and the + // Python reference recurses (so it caps out around 1,000 taxa on such a tree). + const stack = [tree.root]; + dist[tree.root] = 0; + while (stack.length) { + const node = stack.pop(); + for (const c of tree.children[node]) { + dist[c] = dist[node] + tree.branchLength[c]; + stack.push(c); + } + } + return dist; +} + +/** + * A reusable scratch buffer for ancestor marking. + * + * LCA is found by marking every ancestor of `a` and walking up from `b` to the first mark. The naive + * version clears the mark array per pair, which is O(nodes) per pair and dominates the whole + * computation for a wide tree. Stamping with a monotonically increasing generation makes the clear + * free. + */ +function ancestorMarker(nodeCount) { + const stamp = new Int32Array(nodeCount); + let generation = 0; + return { + /** Mark the root path of `node`, then return an `isAncestor` predicate for it. */ + markPath(tree, node) { + generation++; + let cur = node; + while (cur !== -1) { + stamp[cur] = generation; + cur = tree.parent[cur]; + } + }, + isMarked(node) { + return stamp[node] === generation; + } + }; +} + +/** + * One row of the patristic distance matrix: distances from `fromNode` to each of `toNodes`. + * + * @param {any} tree + * @param {Float64Array} rootDist from rootDistances() + * @param {number} fromNode + * @param {number[]} toNodes node indices, in the order the row should come out + * @param {ReturnType} [marker] reused across rows by patristicMatrix/maxPd + * @returns {Float64Array} + */ +export function patristicRow(tree, rootDist, fromNode, toNodes, marker) { + const m = marker ?? ancestorMarker(tree.name.length); + m.markPath(tree, fromNode); + const out = new Float64Array(toNodes.length); + for (let k = 0; k < toNodes.length; k++) { + const b = toNodes[k]; + if (b === fromNode) { + out[k] = 0; + continue; + } + let cur = b; + while (cur !== -1 && !m.isMarked(cur)) cur = tree.parent[cur]; + // cur === -1 cannot happen for two nodes of the same tree (the root is always marked), but a + // caller can pass a node from a different tree, and 0 is a less destructive answer than NaN. + out[k] = cur === -1 ? 0 : rootDist[fromNode] + rootDist[b] - 2 * rootDist[cur]; + } + return out; +} + +/** + * The full N x N patristic matrix, row-major. + * + * Only for N small enough to want it whole — the model input is [max_species, max_species], so this + * is the right call there. Max-PD deliberately does NOT use it; see maxPdSelect. + * + * @param {any} tree + * @param {number[]} nodes node indices, defining row and column order + * @returns {Float64Array} length nodes.length ** 2 + */ +export function patristicMatrix(tree, nodes) { + const n = nodes.length; + const rootDist = rootDistances(tree); + const marker = ancestorMarker(tree.name.length); + const out = new Float64Array(n * n); + for (let i = 0; i < n; i++) { + const row = patristicRow(tree, rootDist, nodes[i], nodes, marker); + out.set(row, i * n); + } + return out; +} + +/** + * Max-PD (Faith's PD) farthest-point traversal, matching predict_regression_nexus.py:1174-1183. + * + * selected = [0] + * minDist = D[0] + * repeat: next = argmax(minDist); selected.push(next); minDist = min(minDist, D[next]) + * + * TWO BEHAVIOURS REPRODUCED ON PURPOSE, BOTH OF WHICH LOOK LIKE BUGS: + * + * 1. THE SEED IS ALWAYS INDEX 0 — the first taxon in alignment order, not the most divergent one + * and not a deterministic function of the tree. Reordering the sequences in an upload changes + * which 512 taxa the model sees. That is what the model was fine-tuned against, so this port + * matches it; it is not a defect this layer gets to fix. + * + * 2. A SELECTED TAXON CAN REPEAT. Every selected index has minDist 0 (its own distance to itself + * enters the running minimum), so once every remaining candidate is also at 0 — an all-zero + * distance matrix, i.e. a tree whose branch lengths are all zero — argmax returns index 0 over + * and over and the same taxon fills every slot. `duplicates` reports it rather than silently + * deduplicating, because deduplicating would change which taxa reach a model that was trained + * with the duplicates present. + * + * @param {any} tree + * @param {number[]} nodes candidate node indices, IN ALIGNMENT ORDER (index 0 is the seed) + * @param {number} maxSpecies + * @returns {{selected: number[], duplicates: number}} selected are indices INTO `nodes` + */ +export function maxPdSelect(tree, nodes, maxSpecies) { + const n = nodes.length; + if (n <= maxSpecies) { + return { selected: Array.from({ length: n }, (_, i) => i), duplicates: 0 }; + } + const rootDist = rootDistances(tree); + const marker = ancestorMarker(tree.name.length); + + const selected = [0]; + const seen = new Set([0]); + let duplicates = 0; + // One row, reused. This is the whole memory argument in the header: never N x N, only N. + const minDist = patristicRow(tree, rootDist, nodes[0], nodes, marker); + + for (let step = 1; step < maxSpecies; step++) { + // argmax with FIRST-max-wins on ties, matching np.argmax. + let best = 0; + let bestVal = minDist[0]; + for (let k = 1; k < n; k++) { + if (minDist[k] > bestVal) { + bestVal = minDist[k]; + best = k; + } + } + selected.push(best); + if (seen.has(best)) duplicates++; + else seen.add(best); + + const row = patristicRow(tree, rootDist, nodes[best], nodes, marker); + for (let k = 0; k < n; k++) if (row[k] < minDist[k]) minDist[k] = row[k]; + } + return { selected, duplicates }; +} diff --git a/src/lib/services/axomeme/postprocess.js b/src/lib/services/axomeme/postprocess.js new file mode 100644 index 0000000..e71e99f --- /dev/null +++ b/src/lib/services/axomeme/postprocess.js @@ -0,0 +1,218 @@ +/** + * postprocess.js — turning the graph's five raw output tensors into per-site results. + * + * THE RAW OUTPUTS ARE NOT THE NUMBERS A USER SHOULD SEE, and the transformations are not cosmetic: + * + * - `alpha` / `beta_pos` are in LOG1P SPACE. The heads are `F.softplus(...)`, and + * expm1(softplus(x)) == exp(x), so the model predicts log1p(rate) and expm1 recovers the rate. + * Rendering them raw understates every rate — a dN of 1.7 shows as 1.0. + * - `p_neg` is a probability of NEGATIVE selection; the reported quantity is p_pos = 1 - p_neg. + * - `lrt` IS the LRT, already ordinal-decoded in-graph (the export is eval mode). It is NOT a log, + * which was worth checking rather than assuming: the reference derives + * `predicted_log_lrt = log1p(predicted_lrt)`, so the log column is the DERIVED one. + * - `beta_neg` is computed by the model and then never used by the reference's output. Carried here + * so it is available, but it appears in no column and no call. + * + * INVARIANT SITES ARE ZEROED, NOT SCORED. `if not is_var:` sets every prediction to 0 before the + * model's output is consulted (predict_regression_nexus.py:1394-1399). A site where every sequence + * codes the same amino acid reports 0, whatever the network said. This is the reference's behaviour + * and it matters for the UI: those zeros are "not applicable", not "no selection", and they are + * excluded from the z-score and percentile statistics for exactly that reason. + * + * Source: predict_regression_nexus.py:1386-1466. + */ + +import { GENETIC_CODE, aaToken } from './tokenizer.js'; + +/** Default tier gates, from the driver's argparse (lines 984-991). */ +export const CALL_DEFAULTS = Object.freeze({ + mode: 'pvalue', + tier1LrtGate: 4.45, // p <= 0.05 + tier2LrtGate: 3.12, // p <= 0.10 + tier1Zscore: 2.5, + tier2Zscore: 2.0, + tier1Percentile: 98.0, + tier2Percentile: 95.0 +}); + +export const NEUTRAL_CALL = 'Neutral'; + +/** + * Is this site variable, in the reference's sense? + * + * Two conditions, and the second is easy to miss: a site is also variable if every sequence codes + * SERINE but reaches it through both codon families (TCN and AGY). Serine is the one residue whose + * codons occupy two disjoint blocks of the genetic code, so a TCN<->AGY switch requires multiple + * substitutions while remaining synonymous — selection-relevant despite the amino acid never + * changing. Dropping this condition silently marks those sites invariant and zeroes them. + * + * @param {string[]} codons observed codons at this site, gaps/ambiguity already excluded + * @returns {boolean} + */ +export function isSiteVariable(codons) { + if (!codons || codons.length === 0) return false; + const aas = new Set(); + for (const c of codons) { + const aa = GENETIC_CODE.get(c.toUpperCase()); + if (aa && aa !== '?') aas.add(aa); + } + if (aas.size === 0) return false; + if (aas.size > 1) return true; + if (aas.size === 1 && aas.has('S')) { + const upper = codons.map((c) => c.toUpperCase()); + const hasTCN = upper.some((c) => c === 'TCA' || c === 'TCC' || c === 'TCG' || c === 'TCT'); + const hasAGY = upper.some((c) => c === 'AGC' || c === 'AGT'); + if (hasTCN && hasAGY) return true; + } + return false; +} + +/** Percentile rank, matching pandas `rank(pct=True) * 100` — average rank for ties. */ +function percentileRanks(values) { + const n = values.length; + const order = Array.from({ length: n }, (_, i) => i).sort((a, b) => values[a] - values[b]); + const ranks = new Float64Array(n); + let i = 0; + while (i < n) { + let j = i; + while (j + 1 < n && values[order[j + 1]] === values[order[i]]) j++; + // pandas' default tie method is 'average': tied entries share the mean of their 1-based ranks. + const avg = (i + 1 + (j + 1)) / 2; + for (let k = i; k <= j; k++) ranks[order[k]] = avg; + i = j + 1; + } + for (let k = 0; k < n; k++) ranks[k] = (ranks[k] / n) * 100; + return ranks; +} + +/** + * Build the per-site result table. + * + * @param {{lrt: ArrayLike, alpha: ArrayLike, beta_neg: ArrayLike, + * beta_pos: ArrayLike, p_neg: ArrayLike}} outputs raw graph outputs, one per site + * @param {{refCodons: string[], variable: boolean[]}} sites reference codon and variability per site + * @param {object} [callOptions] overrides for CALL_DEFAULTS + * @returns {Array} one row per codon site, 1-indexed `site` + */ +export function buildPredictions(outputs, sites, callOptions = {}) { + const cfg = { ...CALL_DEFAULTS, ...callOptions }; + const n = sites.refCodons.length; + const rows = []; + + for (let i = 0; i < n; i++) { + const isVar = Boolean(sites.variable[i]); + const refCodon = (sites.refCodons[i] ?? '').toUpperCase(); + // translate_codon() rejects gaps, N and '?' before the table lookup, so a gapped reference + // codon has no amino acid rather than an accidental one. + const refAa = + refCodon.length === 3 && + !refCodon.includes('-') && + !refCodon.includes('N') && + !refCodon.includes('?') + ? (GENETIC_CODE.get(refCodon) ?? '?') + : '?'; + + if (!isVar) { + // Everything zeroed BEFORE the model is consulted. These zeros mean "not applicable". + rows.push({ + site: i + 1, + refCodon, + refAa, + isVariable: false, + lrt: 0, + logLrt: 0, + alphaDs: 0, + betaPosDn: 0, + pPos: 0, + zScore: 0, + percentile: 0, + call: NEUTRAL_CALL + }); + continue; + } + + const lrt = Math.max(0, Number(outputs.lrt[i])); + rows.push({ + site: i + 1, + refCodon, + refAa, + isVariable: true, + lrt, + logLrt: Math.log1p(lrt), + // expm1 undoes the softplus/log1p parameterisation; the clamp is the reference's. + alphaDs: Math.max(0, Math.expm1(Number(outputs.alpha[i]))), + betaPosDn: Math.max(0, Math.expm1(Number(outputs.beta_pos[i]))), + pPos: 1 - Number(outputs.p_neg[i]), + zScore: 0, + percentile: 0, + call: NEUTRAL_CALL + }); + } + + // Local statistics are computed over VARIABLE SITES ONLY. Including the zeroed invariant sites + // would drag the mean down and inflate every z-score, which is the whole reason they are excluded. + const varIdx = rows.map((r, i) => (r.isVariable ? i : -1)).filter((i) => i >= 0); + if (varIdx.length === 0) return rows; + + const varLrts = varIdx.map((i) => rows[i].lrt); + const mean = varLrts.reduce((a, b) => a + b, 0) / varLrts.length; + // Population standard deviation — np.std, not pandas' sample std. + const variance = varLrts.reduce((a, b) => a + (b - mean) ** 2, 0) / varLrts.length; + const std = Math.sqrt(variance); + + const pct = percentileRanks(varLrts); + varIdx.forEach((rowIdx, k) => { + rows[rowIdx].zScore = std > 0 ? (varLrts[k] - mean) / std : 0; + rows[rowIdx].percentile = pct[k]; + }); + + for (const i of varIdx) { + const r = rows[i]; + let t1 = false; + let t2 = false; + if (cfg.mode === 'zscore') { + t1 = r.zScore >= cfg.tier1Zscore; + t2 = !t1 && r.zScore >= cfg.tier2Zscore; + } else if (cfg.mode === 'percentile') { + t1 = r.percentile >= cfg.tier1Percentile; + t2 = !t1 && r.percentile >= cfg.tier2Percentile; + } else { + t1 = r.lrt >= cfg.tier1LrtGate; + t2 = !t1 && r.lrt >= cfg.tier2LrtGate; + } + if (t1) r.call = 'Tier 1 (High)'; + else if (t2) r.call = 'Tier 2 (Medium)'; + } + + return rows; +} + +/** + * Variability flags for every site of an alignment, from the aligned sequences. + * + * Mirrors the reference's collection loop: only codons that are complete, ungapped and unambiguous + * are considered, and a sequence shorter than the reference simply contributes nothing at the sites + * it does not reach. + * + * @param {string[]} sequences aligned nucleotide sequences, same frame + * @param {number} totalCodons + * @returns {boolean[]} + */ +export function siteVariability(sequences, totalCodons) { + const flags = new Array(totalCodons).fill(false); + for (let s = 0; s < totalCodons; s++) { + const codons = []; + for (const seq of sequences) { + const start = s * 3; + if (start + 3 > seq.length) continue; + const c = seq.slice(start, start + 3).toUpperCase(); + if (c.includes('-') || c.includes('N') || c.includes('?')) continue; + // aaToken rejects anything the genetic code cannot translate, which is the same filter the + // reference applies before collecting a codon. + if (aaToken(c) > 20) continue; + codons.push(c); + } + flags[s] = isSiteVariable(codons); + } + return flags; +} diff --git a/src/lib/services/axomeme/session.js b/src/lib/services/axomeme/session.js new file mode 100644 index 0000000..4ae7a5e --- /dev/null +++ b/src/lib/services/axomeme/session.js @@ -0,0 +1,148 @@ +/** + * session.js — loading and running the AxoMEME 2.0 ONNX graph in the browser. + * + * THE COST DISCIPLINE THIS FILE EXISTS TO ENFORCE. onnxruntime-web is ~13 MB of WASM and the model is + * another 3.78 MB. DataMonkey has fifteen analysis methods and AxoMEME is one of them; the other + * fourteen must download none of it. That is not a nice-to-have — the first version of the MEME + * hit-likelihood gate shipped 13.5 MB of ONNX Runtime to every method and rendered nothing for + * fourteen of them, and an "is the element absent?" test passed the whole time because the element + * WAS absent. The bytes were the bug. + * + * So: + * - The runtime is loaded by DYNAMIC import inside loadSession(), never at module scope. Importing + * this file costs nothing; calling loadSession() is what costs 17 MB. + * - The model is fetched from static/ rather than bundled, so it is cached by the browser + * independently of the JS and never enters the bundle graph. + * - The session is memoised, because a second 17 MB download to score a second alignment would be + * indefensible. + * + * INTEGRITY. The artifact's sha256 is pinned in modelContract.js and verified after fetch. The + * contract's conclusions — eval-mode export, `lrt` already decoded, rate heads in log1p space — were + * read off THAT graph, and a silently swapped model would make all of them wrong while everything + * continued to run. Verification is skipped only when Web Crypto is unavailable (non-secure context), + * which is reported rather than hidden. + * + * BATCHING. dist_matrix, mds_coords and padding_mask are per-alignment, not per-site — the reference + * computes them once and `expand`s them across sites. So every codon site of an alignment goes + * through the graph in ONE run, with those three tensors repeated along the batch axis. The reference + * driver instead loops one forward pass per site (predict_regression_nexus.py:1345), which for a + * 441-site alignment is 441 sessions of overhead for identical work. + */ + +import { INPUT_NAMES, VERIFIED_MODEL_SHA256 } from './modelContract.js'; + +/** Where the artifact lives. Served from static/, never imported, never bundled. */ +export const MODEL_URL = '/models/axomeme/axomeme_2.0_viral_finetuned.onnx'; + +/** Memoised session promise. Null until the first scoring call. */ +let sessionPromise = null; + +/** + * Hex sha256 of a buffer, or null where Web Crypto is unavailable. + * + * Digests a Uint8Array VIEW rather than the ArrayBuffer itself. `digest` accepts either per spec, but + * an ArrayBuffer constructed in another realm fails the implementation's instanceof check — which is + * exactly what happens under jsdom, and would equally happen for a buffer crossing a worker boundary. + * Constructing the view here puts it in this module's realm. + */ +async function sha256Hex(buffer) { + if (typeof globalThis.crypto?.subtle?.digest !== 'function') return null; + const digest = await globalThis.crypto.subtle.digest('SHA-256', new Uint8Array(buffer)); + return Array.from(new Uint8Array(digest)) + .map((b) => b.toString(16).padStart(2, '0')) + .join(''); +} + +/** + * Load the ONNX session, downloading the runtime and the model on first call. + * + * @param {{url?: string, verifyHash?: boolean, ort?: any, fetchImpl?: typeof fetch}} [options] + * `ort` and `fetchImpl` exist for tests; production passes neither. + * @returns {Promise<{session: any, sha256: string|null, bytes: number}>} + */ +export function loadSession(options = {}) { + if (sessionPromise && !options.url && !options.ort && !options.fetchImpl) return sessionPromise; + + const promise = (async () => { + const url = options.url ?? MODEL_URL; + const doFetch = options.fetchImpl ?? globalThis.fetch; + + // The dynamic import is the whole point — see the header. Do not hoist it. + const ort = options.ort ?? (await import('onnxruntime-web')); + + const response = await doFetch(url); + if (!response.ok) { + throw new Error(`AxoMEME model fetch failed: ${response.status} ${response.statusText}`); + } + const buffer = await response.arrayBuffer(); + + const sha256 = options.verifyHash === false ? null : await sha256Hex(buffer); + if (sha256 && sha256 !== VERIFIED_MODEL_SHA256) { + // Refuse rather than score. Every conclusion in modelContract.js was read off the pinned + // graph; a different one may well be fine, but nothing here knows that, and the failure + // mode of guessing is silently wrong numbers rather than an error. + throw new Error( + `AxoMEME model hash mismatch.\n expected ${VERIFIED_MODEL_SHA256}\n got ${sha256}\n` + + 'The input/output contract was verified against the expected artifact. If the model was ' + + 'deliberately updated, re-verify src/lib/services/axomeme/modelContract.js and update ' + + 'VERIFIED_MODEL_SHA256 in the same commit.' + ); + } + + const session = await ort.InferenceSession.create(new Uint8Array(buffer)); + + // The graph must expose exactly what the contract says. A rename upstream would otherwise + // surface as an opaque runtime error deep inside session.run. + const missing = INPUT_NAMES.filter((n) => !session.inputNames.includes(n)); + if (missing.length) { + throw new Error(`AxoMEME model is missing expected inputs: ${missing.join(', ')}`); + } + + return { session, sha256, bytes: buffer.byteLength }; + })(); + + if (!options.url && !options.ort && !options.fetchImpl) sessionPromise = promise; + // A failed load must not be memoised as a permanent failure — a transient fetch error would + // otherwise disable the feature for the rest of the page's life. + promise.catch(() => { + if (sessionPromise === promise) sessionPromise = null; + }); + return promise; +} + +/** Drop the memoised session. Tests use this; production has no reason to. */ +export function resetSession() { + sessionPromise = null; +} + +/** True once a session is loaded or loading — lets a caller avoid triggering a 17 MB download. */ +export function isSessionLoaded() { + return sessionPromise !== null; +} + +/** + * Run every site of an alignment through the graph. + * + * @param {any} session an ONNX InferenceSession + * @param {object} bundle tensors as produced by the assembly layer, shaped per modelContract + * @param {any} ort the onnxruntime module (passed in so this stays a pure function) + * @returns {Promise<{lrt: Float32Array, alpha: Float32Array, beta_neg: Float32Array, + * beta_pos: Float32Array, p_neg: Float32Array}>} + */ +export async function runSites(session, bundle, ort) { + const feeds = { + msa_codons: new ort.Tensor('int64', bundle.msa_codons.data, bundle.msa_codons.dims), + msa_aas: new ort.Tensor('int64', bundle.msa_aas.data, bundle.msa_aas.dims), + dist_matrix: new ort.Tensor('float32', bundle.dist_matrix.data, bundle.dist_matrix.dims), + mds_coords: new ort.Tensor('float32', bundle.mds_coords.data, bundle.mds_coords.dims), + padding_mask: new ort.Tensor('bool', bundle.padding_mask.data, bundle.padding_mask.dims) + }; + const out = await session.run(feeds); + return { + lrt: out.lrt.data, + alpha: out.alpha.data, + beta_neg: out.beta_neg.data, + beta_pos: out.beta_pos.data, + p_neg: out.p_neg.data + }; +} diff --git a/src/lib/services/axomeme/symmetricEigen.js b/src/lib/services/axomeme/symmetricEigen.js new file mode 100644 index 0000000..14c7a65 --- /dev/null +++ b/src/lib/services/axomeme/symmetricEigen.js @@ -0,0 +1,227 @@ +/** + * symmetricEigen.js — eigendecomposition of a real symmetric matrix. + * + * WHY THIS IS HERE AT ALL. AxoMEME's `mds_coords` is a model INPUT, not something the ONNX graph + * computes, because `torch.linalg.eigh` has no ONNX lowering — verified directly: the identical + * module exports fine with eigh removed and fails with it present. So the browser has to do its own + * eigendecomposition of the [max_species, max_species] double-centred matrix, which at the default + * max_species is 512 x 512. + * + * THE ALGORITHM is Householder tridiagonalisation followed by implicit-shift QL — `tred2`/`tql2`, + * the EISPACK routines that JAMA and Numerical Recipes both carry, and mathematically what LAPACK's + * symmetric drivers reduce to. It is chosen over Jacobi for cost: Jacobi on 512 x 512 needs roughly + * 10 sweeps of ~131k rotations and lands in the seconds; tred2/tql2 is ~(4/3)n^3 once plus cheap + * iteration, and runs in a few hundred milliseconds. + * + * WHAT PARITY CAN AND CANNOT BE PROMISED HERE, stated plainly because it is the crux of the whole + * browser port: + * + * - EIGENVALUES agree with LAPACK to near machine precision. They are a property of the matrix. + * - EIGENVECTORS are only unique up to sign, and within a DEGENERATE eigenspace not even up to + * sign — any orthonormal basis of that subspace is equally correct. numpy uses divide-and-conquer + * (dsyevd); this is QL. Two correct implementations can therefore disagree, and no amount of care + * in this file changes that. + * + * The sign half is fixed by convention downstream (mds.js applies the reference's own rule). The + * degeneracy half is not fixable in principle, only measurable — which is why the parity harness runs + * over real trees rather than a proof being attempted here. + * + * The routine is transcribed rather than invented, and its tests check the properties that actually + * matter (A·V = V·Λ, orthonormality, reconstruction) rather than comparing against numbers this file + * produced. + */ + +/** Machine epsilon for float64, as the reference routines define it. */ +const EPS = Math.pow(2, -52); + +/** + * Eigendecomposition of a real symmetric matrix. + * + * @param {Float64Array|number[]} matrix row-major, n*n, assumed symmetric (upper triangle unused) + * @param {number} n + * @returns {{values: Float64Array, vectors: Float64Array}} eigenvalues ASCENDING (matching + * numpy.linalg.eigh), eigenvectors row-major with column j being the vector for values[j] + */ +export function symmetricEigen(matrix, n) { + const V = Float64Array.from(matrix); + const d = new Float64Array(n); + const e = new Float64Array(n); + + tred2(V, d, e, n); + tql2(V, d, e, n); + + return { values: d, vectors: V }; +} + +/** + * Householder reduction to tridiagonal form. + * + * On entry V holds the symmetric matrix; on exit V holds the accumulated orthogonal transformation, + * d the diagonal and e the sub-diagonal of the tridiagonal form. + */ +function tred2(V, d, e, n) { + for (let j = 0; j < n; j++) d[j] = V[(n - 1) * n + j]; + + for (let i = n - 1; i > 0; i--) { + let scale = 0; + let h = 0; + for (let k = 0; k < i; k++) scale += Math.abs(d[k]); + + if (scale === 0) { + e[i] = d[i - 1]; + for (let j = 0; j < i; j++) { + d[j] = V[(i - 1) * n + j]; + V[i * n + j] = 0; + V[j * n + i] = 0; + } + } else { + for (let k = 0; k < i; k++) { + d[k] /= scale; + h += d[k] * d[k]; + } + let f = d[i - 1]; + let g = Math.sqrt(h); + if (f > 0) g = -g; + e[i] = scale * g; + h -= f * g; + d[i - 1] = f - g; + for (let j = 0; j < i; j++) e[j] = 0; + + for (let j = 0; j < i; j++) { + f = d[j]; + V[j * n + i] = f; + g = e[j] + V[j * n + j] * f; + for (let k = j + 1; k <= i - 1; k++) { + g += V[k * n + j] * d[k]; + e[k] += V[k * n + j] * f; + } + e[j] = g; + } + + f = 0; + for (let j = 0; j < i; j++) { + e[j] /= h; + f += e[j] * d[j]; + } + const hh = f / (h + h); + for (let j = 0; j < i; j++) e[j] -= hh * d[j]; + + for (let j = 0; j < i; j++) { + f = d[j]; + g = e[j]; + for (let k = j; k <= i - 1; k++) V[k * n + j] -= f * e[k] + g * d[k]; + d[j] = V[(i - 1) * n + j]; + V[i * n + j] = 0; + } + } + d[i] = h; + } + + // Accumulate the transformations. + for (let i = 0; i < n - 1; i++) { + V[(n - 1) * n + i] = V[i * n + i]; + V[i * n + i] = 1; + const h = d[i + 1]; + if (h !== 0) { + for (let k = 0; k <= i; k++) d[k] = V[k * n + (i + 1)] / h; + for (let j = 0; j <= i; j++) { + let g = 0; + for (let k = 0; k <= i; k++) g += V[k * n + (i + 1)] * V[k * n + j]; + for (let k = 0; k <= i; k++) V[k * n + j] -= g * d[k]; + } + } + for (let k = 0; k <= i; k++) V[k * n + (i + 1)] = 0; + } + for (let j = 0; j < n; j++) { + d[j] = V[(n - 1) * n + j]; + V[(n - 1) * n + j] = 0; + } + V[(n - 1) * n + (n - 1)] = 1; + e[0] = 0; +} + +/** Symmetric tridiagonal QL with implicit shifts. Leaves eigenvalues in d, ASCENDING. */ +function tql2(V, d, e, n) { + for (let i = 1; i < n; i++) e[i - 1] = e[i]; + e[n - 1] = 0; + + let f = 0; + let tst1 = 0; + + for (let l = 0; l < n; l++) { + tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l])); + let m = l; + while (m < n) { + if (Math.abs(e[m]) <= EPS * tst1) break; + m++; + } + + if (m > l) { + do { + let g = d[l]; + let p = (d[l + 1] - g) / (2 * e[l]); + let r = Math.hypot(p, 1); + if (p < 0) r = -r; + d[l] = e[l] / (p + r); + d[l + 1] = e[l] * (p + r); + const dl1 = d[l + 1]; + let h = g - d[l]; + for (let i = l + 2; i < n; i++) d[i] -= h; + f += h; + + p = d[m]; + let c = 1; + let c2 = c; + let c3 = c; + const el1 = e[l + 1]; + let s = 0; + let s2 = 0; + for (let i = m - 1; i >= l; i--) { + c3 = c2; + c2 = c; + s2 = s; + g = c * e[i]; + h = c * p; + r = Math.hypot(p, e[i]); + e[i + 1] = s * r; + s = e[i] / r; + c = p / r; + p = c * d[i] - s * g; + d[i + 1] = h + s * (c * g + s * d[i]); + for (let k = 0; k < n; k++) { + h = V[k * n + (i + 1)]; + V[k * n + (i + 1)] = s * V[k * n + i] + c * h; + V[k * n + i] = c * V[k * n + i] - s * h; + } + } + p = (-s * s2 * c3 * el1 * e[l]) / dl1; + e[l] = s * p; + d[l] = c * p; + } while (Math.abs(e[l]) > EPS * tst1); + } + d[l] += f; + e[l] = 0; + } + + // Sort ascending, carrying the eigenvectors with their values. Selection sort: n is at most a few + // hundred here and the swap has to move a whole column, so the simple algorithm is the right one. + for (let i = 0; i < n - 1; i++) { + let k = i; + let p = d[i]; + for (let j = i + 1; j < n; j++) { + if (d[j] < p) { + k = j; + p = d[j]; + } + } + if (k !== i) { + d[k] = d[i]; + d[i] = p; + for (let j = 0; j < n; j++) { + const t = V[j * n + i]; + V[j * n + i] = V[j * n + k]; + V[j * n + k] = t; + } + } + } +} diff --git a/src/lib/services/axomeme/tokenizer.js b/src/lib/services/axomeme/tokenizer.js new file mode 100644 index 0000000..749d872 --- /dev/null +++ b/src/lib/services/axomeme/tokenizer.js @@ -0,0 +1,128 @@ +/** + * tokenizer.js — codon and amino-acid tokenisation for AxoMEME 2.0. + * + * THIS IMPLEMENTS THE TRAINING TOKENIZER (train_transformer_selection.py:74-97), NOT THE ONE IN THE + * HANDOFF'S INFERENCE DRIVER. That is a deliberate, measured choice, and it is the single most + * consequential decision in this file — see the long note in modelContract.js. In short: + * predict_regression_nexus.py defines its own 60-codon ALPHABETICAL vocabulary at lines 49-55, then + * redefines `get_codon_token` without redefining `CODON_TO_IDX`, so 63 of 64 codons come out with a + * different token at inference than the model was trained on, and TTA plus the three stop codons + * vanish into "unknown". A model trained on TCAG-64 must be served TCAG-64. + * + * If a future handoff fixes the driver, this file does not change — it already matches training. + * If someone "aligns" this file to the driver, the model silently degrades and nothing fails. + * src/test/axomeme-tokenizer.test.js pins both directions. + */ + +import { + CODON_ORDER, + CODON_GAP, + CODON_UNKNOWN, + AA_LIST, + AA_GAP, + AA_UNKNOWN +} from './modelContract.js'; + +/** + * The 64 codons in TCAG order — the standard genetic-code table order, so TTT is 0 and GGG is 63. + * Built rather than transcribed: a 64-entry literal is a transcription error waiting to happen, and + * the generator IS the reference's definition. + */ +export const CODON_LIST = (() => { + const out = []; + for (const a of CODON_ORDER) + for (const b of CODON_ORDER) for (const c of CODON_ORDER) out.push(a + b + c); + return out; +})(); + +/** codon string -> token, for the 64 sense+stop codons. */ +export const CODON_TO_IDX = new Map(CODON_LIST.map((c, i) => [c, i])); + +/** + * The standard genetic code, with stops as '*' to match AA_LIST. + * + * Derived from the codon table rather than transcribed, for the same reason as CODON_LIST. The four + * blocks below are the standard code's structure; every codon is covered, so there is no default. + * + * ONE THING WORTH FLAGGING UPSTREAM: this is the UNIVERSAL code, hard-coded. DM3 lets a user pick a + * genetic code (mitochondrial, mycoplasma, several others) and HyPhy honours that choice, so an + * AxoMEME prediction on a non-universal alignment translates codons the user did not ask for. That + * is a model-side limitation, not something this file can fix — recorded here so it is not + * rediscovered as a mystery. + */ +export const GENETIC_CODE = (() => { + // TCAG-ordered amino acids, one per codon, in the same order CODON_LIST generates. + const table = + 'FFLLSSSSYY**CC*W' + // TTx TCx TAx TGx + 'LLLLPPPPHHQQRRRR' + // CTx CCx CAx CGx + 'IIIMTTTTNNKKSSRR' + // ATx ACx AAx AGx + 'VVVVAAAADDEEGGGG'; // GTx GCx GAx GGx + const map = new Map(); + CODON_LIST.forEach((c, i) => map.set(c, table[i])); + return map; +})(); + +/** amino acid character -> token. */ +export const AA_TO_IDX = new Map([...AA_LIST].map((a, i) => [a, i])); + +/** + * Token for a codon string. + * + * Order of tests matters and matches the reference exactly: a gap ANYWHERE wins over everything + * else, so `A-T` is a gap rather than an unknown. `'-' in codon` is a substring test in Python, not + * an equality test, which is why a partial gap counts. + * + * @param {string} codon + * @returns {number} 0..63 for a real codon, CODON_GAP for a gap, CODON_UNKNOWN otherwise + */ +export function codonToken(codon) { + const c = String(codon).toUpperCase(); + if (c.includes('-')) return CODON_GAP; + if (c.length !== 3 || c.includes('N')) return CODON_UNKNOWN; + const t = CODON_TO_IDX.get(c); + return t === undefined ? CODON_UNKNOWN : t; +} + +/** + * Token for the amino acid a codon translates to. + * + * Note the asymmetry with codonToken, which is in the reference and is not a typo here: this one + * also rejects '?', codonToken does not. A '?' codon reaches codonToken's length/N test and, being + * length 3 without an N, falls through to the map lookup and returns CODON_UNKNOWN anyway — same + * answer by a different route. + * + * @param {string} codon + * @returns {number} 0..20 for a translated residue (20 = stop), AA_GAP or AA_UNKNOWN + */ +export function aaToken(codon) { + const c = String(codon).toUpperCase(); + if (c.includes('-')) return AA_GAP; + if (c.length !== 3 || c.includes('N') || c.includes('?')) return AA_UNKNOWN; + const aa = GENETIC_CODE.get(c); + if (aa === undefined) return AA_UNKNOWN; + const t = AA_TO_IDX.get(aa); + return t === undefined ? AA_UNKNOWN : t; +} + +/** + * Tokenise one sequence into per-codon (codon, aa) token pairs. + * + * A trailing partial codon is DROPPED, matching `total_codons = len(ref_seq) // 3`. Sites beyond a + * sequence's own length are the caller's problem: the reference leaves those at the pad value it + * pre-filled the tensor with, which is why this returns only what the sequence actually covers. + * + * @param {string} seq nucleotides, gaps allowed + * @returns {{codons: Uint8Array, aas: Uint8Array}} + */ +export function tokenizeSequence(seq) { + const s = String(seq); + const n = Math.floor(s.length / 3); + const codons = new Uint8Array(n); + const aas = new Uint8Array(n); + for (let i = 0; i < n; i++) { + const c = s.slice(i * 3, i * 3 + 3); + codons[i] = codonToken(c); + aas[i] = aaToken(c); + } + return { codons, aas }; +} diff --git a/src/test/axomeme-mds.test.js b/src/test/axomeme-mds.test.js new file mode 100644 index 0000000..e42af1e --- /dev/null +++ b/src/test/axomeme-mds.test.js @@ -0,0 +1,221 @@ +/** + * Tests for the symmetric eigendecomposition and classical MDS. + * + * These check PROPERTIES, not recorded outputs. An eigendecomposition test that asserts "these are + * the numbers we got last time" proves determinism and nothing else; the real contract is + * A·V = V·Λ with V orthonormal, and that is checkable from first principles on any input. The MDS + * cases are hand-derived from small distance matrices whose answer can be worked out on paper. + * + * The cross-implementation check against numpy lives in scripts/axomeme/verify_preprocessing.py + + * .mjs, which compares against the ML team's own `compute_mds_coordinates` over 270 real DataMonkey + * trees. That run is what proves parity; these tests are what make a failure there interpretable. + */ +import { describe, it, expect } from 'vitest'; +import { symmetricEigen } from '../lib/services/axomeme/symmetricEigen.js'; +import { computeMdsCoordinates } from '../lib/services/axomeme/mds.js'; + +/** Deterministic symmetric matrix, fixed seed so a failure is reproducible. */ +function symMatrix(n, seed) { + let s = seed; + const rnd = () => ((s = (s * 1103515245 + 12345) & 0x7fffffff) / 0x7fffffff) * 2 - 1; + const A = new Float64Array(n * n); + for (let i = 0; i < n; i++) { + for (let j = i; j < n; j++) { + const v = rnd(); + A[i * n + j] = v; + A[j * n + i] = v; + } + } + return A; +} + +describe('symmetricEigen', () => { + it('solves a 2x2 with known eigenvalues', () => { + // [[2,1],[1,2]] has eigenvalues 1 and 3. + const { values } = symmetricEigen([2, 1, 1, 2], 2); + expect(values[0]).toBeCloseTo(1, 12); + expect(values[1]).toBeCloseTo(3, 12); + }); + + it('returns eigenvalues ASCENDING, matching numpy.linalg.eigh', () => { + // mds.js reads components from the END of this array; a descending convention would silently + // return the four SMALLEST components. + const { values } = symmetricEigen(symMatrix(20, 3), 20); + for (let i = 1; i < 20; i++) expect(values[i]).toBeGreaterThanOrEqual(values[i - 1]); + }); + + it('reconstructs the matrix: A = V Λ Vᵀ', () => { + for (const n of [3, 10, 40]) { + const A = symMatrix(n, n * 7 + 1); + const { values, vectors } = symmetricEigen(A, n); + let worst = 0; + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) { + let acc = 0; + for (let k = 0; k < n; k++) acc += vectors[i * n + k] * values[k] * vectors[j * n + k]; + worst = Math.max(worst, Math.abs(acc - A[i * n + j])); + } + } + expect(worst, `n=${n}`).toBeLessThan(1e-12); + } + }); + + it('produces orthonormal eigenvectors', () => { + const n = 30; + const { vectors } = symmetricEigen(symMatrix(n, 11), n); + let worst = 0; + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) { + let dot = 0; + for (let k = 0; k < n; k++) dot += vectors[k * n + i] * vectors[k * n + j]; + worst = Math.max(worst, Math.abs(dot - (i === j ? 1 : 0))); + } + } + expect(worst).toBeLessThan(1e-12); + }); + + it('handles a diagonal matrix, the identity, and n=1', () => { + const { values } = symmetricEigen([3, 0, 0, 0, 1, 0, 0, 0, 2], 3); + expect(Array.from(values)).toEqual([1, 2, 3]); // sorted ascending + const id = symmetricEigen([1, 0, 0, 1], 2); + expect(Array.from(id.values)).toEqual([1, 1]); + const one = symmetricEigen([7], 1); + expect(one.values[0]).toBe(7); + }); + + it('does not mutate the caller"s matrix', () => { + const A = Float64Array.from([2, 1, 1, 2]); + symmetricEigen(A, 2); + expect(Array.from(A)).toEqual([2, 1, 1, 2]); + }); + + it('survives a matrix with repeated eigenvalues', () => { + // 2I has a fully degenerate spectrum — any orthonormal basis is correct. The routine must + // still return orthonormal vectors and the right values rather than dividing by a zero gap. + const { values, vectors } = symmetricEigen([2, 0, 0, 0, 2, 0, 0, 0, 2], 3); + expect(Array.from(values)).toEqual([2, 2, 2]); + for (let i = 0; i < 3; i++) { + let norm = 0; + for (let k = 0; k < 3; k++) norm += vectors[k * 3 + i] ** 2; + expect(norm).toBeCloseTo(1, 12); + } + }); +}); + +describe('computeMdsCoordinates', () => { + it('returns zeros when n <= nComponents', () => { + // `if N <= n_components: return zeros` — note <=, so n === nComponents is also all zeros. + expect(Array.from(computeMdsCoordinates(new Float64Array(9), 3, 4))).toEqual( + new Array(12).fill(0) + ); + expect(Array.from(computeMdsCoordinates(new Float64Array(16), 4, 4))).toEqual( + new Array(16).fill(0) + ); + }); + + it('recovers collinear points from their distances', () => { + // Three points at 0, 1, 2 on a line. Worked by hand: D2 double-centres to + // [[1,0,-1],[0,0,0],[-1,0,1]], whose only positive eigenvalue is 2 with eigenvector + // [1,0,-1]/sqrt(2), so component 0 is [1, 0, -1] and component 1 is all zeros. + const D = [0, 1, 2, 1, 0, 1, 2, 1, 0]; + const c = computeMdsCoordinates(D, 3, 2); + expect(c[0 * 2 + 0]).toBeCloseTo(1, 5); + expect(c[1 * 2 + 0]).toBeCloseTo(0, 5); + expect(c[2 * 2 + 0]).toBeCloseTo(-1, 5); + // The second component's eigenvalue is mathematically ZERO — three collinear points need one + // dimension. It does not come out as exactly zero, though: it lands on float dust around + // 1e-17, and the reference's guard is `if val > 0`, which does not distinguish a true zero + // from dust. So a coordinate of ~1e-8 (sqrt of the dust) is emitted rather than a clean zero. + // That is the reference's behaviour and this port matches it; asserting `toBe(0)` here would + // be asserting something neither implementation does. + for (const i of [0, 1, 2]) expect(Math.abs(c[i * 2 + 1])).toBeLessThan(1e-6); + }); + + it('preserves pairwise distances for points that embed exactly', () => { + // A square of side 1: MDS in 2 dimensions must reproduce the input distances. + const s2 = Math.SQRT2; + const D = [0, 1, s2, 1, 1, 0, 1, s2, s2, 1, 0, 1, 1, s2, 1, 0]; + const c = computeMdsCoordinates(D, 4, 2); + const dist = (i, j) => Math.hypot(c[i * 2] - c[j * 2], c[i * 2 + 1] - c[j * 2 + 1]); + expect(dist(0, 1)).toBeCloseTo(1, 4); + expect(dist(1, 2)).toBeCloseTo(1, 4); + expect(dist(0, 2)).toBeCloseTo(s2, 4); + expect(dist(1, 3)).toBeCloseTo(s2, 4); + }); + + it('applies the sign convention: the largest-magnitude entry is positive', () => { + // The reference's rule, and the thing that removes half the eigenvector ambiguity for free. + const D = [0, 1, 2, 1, 0, 1, 2, 1, 0]; + const c = computeMdsCoordinates(D, 3, 2); + let maxAbs = 0; + let atIdx = 0; + for (let i = 0; i < 3; i++) { + if (Math.abs(c[i * 2]) > maxAbs) { + maxAbs = Math.abs(c[i * 2]); + atIdx = i; + } + } + expect(c[atIdx * 2]).toBeGreaterThan(0); + }); + + it('DEPENDS ON THE PADDING, because the reference runs MDS on the padded matrix', () => { + // Not a quirk to be optimised away. The padded zeros take part in the double-centring, so the + // same real taxa padded to different max_species produce different coordinates. A port that + // runs MDS on the real N and pads afterwards silently feeds the model different inputs. + const real = [0, 1, 2, 1, 0, 1, 2, 1, 0]; + const at = (cap) => { + const p = new Float64Array(cap * cap); + for (let i = 0; i < 3; i++) for (let j = 0; j < 3; j++) p[i * cap + j] = real[i * 3 + j]; + return computeMdsCoordinates(p, cap, 2); + }; + const a = at(8); + const b = at(16); + // Component 1 is where it shows. Measured across caps for these three collinear points: + // cap= 3 -> 9.50e-9 cap= 4 -> -9.833e-2 cap= 8 -> -1.0097e-1 + // cap=16 -> -1.0133e-1 cap=64 -> -1.0150e-1 + // It converges as the padding grows but never stops depending on it. + expect(a[0 * 2 + 1]).not.toBeCloseTo(b[0 * 2 + 1], 6); + expect(a[1 * 2 + 1]).not.toBeCloseTo(b[1 * 2 + 1], 6); + // Component 0's MAGNITUDE happens to be padding-invariant for this symmetric example (the + // collinear geometry dominates), which is why the check above is on component 1 — picking + // component 0 would have made this test pass for the wrong reason and then fail later. + expect(Math.abs(a[0])).toBeCloseTo(Math.abs(b[0]), 6); + }); + + it('rounds distances to float32 before squaring, as the reference does', () => { + // The reference is handed a float32 torch tensor, so it squares float32 values. Feeding + // float64 changes components 2-3 by up to 99% on real trees (measured), because squared + // distances reach ~1e6 while the fourth eigenvalue can be ~1e-1. Passing an already-rounded + // matrix and a full-precision one must therefore give the SAME answer. + const n = 8; + const raw = new Float64Array(n * n); + let s = 3; + const rnd = () => (s = (s * 1103515245 + 12345) & 0x7fffffff) / 0x7fffffff; + for (let i = 0; i < n; i++) { + for (let j = i + 1; j < n; j++) { + const v = rnd() * 1000; + raw[i * n + j] = v; + raw[j * n + i] = v; + } + } + const rounded = Float64Array.from(raw, (v) => Math.fround(v)); + const a = computeMdsCoordinates(raw, n, 4); + const b = computeMdsCoordinates(rounded, n, 4); + expect(Array.from(a)).toEqual(Array.from(b)); + }); + + it('returns float32 values, matching the reference cast', () => { + const D = [0, 1, 2, 1, 0, 1, 2, 1, 0]; + const c = computeMdsCoordinates(D, 3, 2); + expect(c).toBeInstanceOf(Float32Array); + for (const v of c) expect(Math.fround(v)).toBe(v); + }); + + it('treats a negative distance as its magnitude, because squaring loses the sign', () => { + // Documented consequence rather than desired behaviour: DM3's NJ emits negative branch + // lengths, and MDS silently absorbs them where the reference's density term throws. + const pos = computeMdsCoordinates([0, 1, 2, 1, 0, 1, 2, 1, 0], 3, 2); + const neg = computeMdsCoordinates([0, -1, 2, -1, 0, 1, 2, 1, 0], 3, 2); + expect(Array.from(neg)).toEqual(Array.from(pos)); + }); +}); diff --git a/src/test/axomeme-model-contract.test.js b/src/test/axomeme-model-contract.test.js new file mode 100644 index 0000000..e7f056e --- /dev/null +++ b/src/test/axomeme-model-contract.test.js @@ -0,0 +1,246 @@ +/** + * Tests for the AxoMEME 2.0 ONNX input contract. + * + * These are not shape-checking-the-shape-checker busywork. Every case below is a mistake a JS + * preprocessing port actually makes — one that produces a tensor of exactly the right dtype and + * exactly the right dimensions, and means something the model was never trained on. Those are the + * errors that cost days, because nothing crashes: the graph runs, five numbers come out per site, + * and they are wrong in a way that looks like a bad model rather than a bad tensor. + * + * The constants themselves are pinned too. They are transcribed from the ML team's handoff scripts, + * and a transcription error in, say, the codon vocabulary order is invisible until it is compared + * against real fixtures — which is a much later and much more expensive place to find it. + */ +import { describe, it, expect } from 'vitest'; +import { + CODON_ORDER, + CODON_GAP, + CODON_UNKNOWN, + NUM_CODON_TOKENS, + AA_LIST, + AA_GAP, + AA_UNKNOWN, + CODON_VALID_BELOW, + AA_VALID_BELOW, + MAX_SPECIES_DEFAULT, + WINDOW_SIZE_DEFAULT, + MDS_COMPONENTS, + INPUT_SPEC, + INPUT_NAMES, + OUTPUT_SPEC, + VERIFIED_MODEL_SHA256, + validateInputBundle +} from '../lib/services/axomeme/modelContract.js'; + +const BATCH = 2; +const SPECIES = 4; // index 3 is padded +const WIN = 1; + +/** A bundle that satisfies the contract. Each test breaks exactly one thing about it. */ +function validBundle() { + const d = [ + [0, 0.1, 0.2, 0], + [0.1, 0, 0.3, 0], + [0.2, 0.3, 0, 0], + [0, 0, 0, 0] + ]; + const distOne = d.flat(); + return { + msa_codons: { + data: new BigInt64Array([0n, 5n, 63n, 65n, 1n, 2n, 3n, 65n]), + dims: [BATCH, SPECIES, WIN] + }, + msa_aas: { + data: new BigInt64Array([0n, 4n, 20n, 22n, 1n, 2n, 3n, 22n]), + dims: [BATCH, SPECIES, WIN] + }, + dist_matrix: { + data: new Float32Array([...distOne, ...distOne]), + dims: [BATCH, SPECIES, SPECIES] + }, + mds_coords: { + data: new Float32Array(BATCH * SPECIES * MDS_COMPONENTS), + dims: [BATCH, SPECIES, MDS_COMPONENTS] + }, + // TRUE = padded. Species 3 only. + padding_mask: { + data: new Uint8Array([0, 0, 0, 1, 0, 0, 0, 1]), + dims: [BATCH, SPECIES] + } + }; +} + +const check = (b) => validateInputBundle(b, { batch: BATCH, numSpecies: SPECIES, windowSize: WIN }); + +describe('the transcribed constants', () => { + it('orders codons TCAG, not alphabetically', () => { + // The single most damaging transcription error available here: an ACGT vocabulary is a valid + // permutation of the same 64 tokens and is wrong at every site of every alignment. + expect(CODON_ORDER).toBe('TCAG'); + const codons = [...CODON_ORDER].flatMap((a) => + [...CODON_ORDER].flatMap((b) => [...CODON_ORDER].map((c) => a + b + c)) + ); + expect(codons).toHaveLength(64); + expect(codons[0]).toBe('TTT'); + expect(codons[63]).toBe('GGG'); + // If someone "fixes" the order to ACGT this is the assertion that objects. + // Met. Under TCAG: A=2, T=0, G=3 -> 2*16 + 0*4 + 3 = 35. Under ACGT it would be 14, so this + // single number distinguishes the two orderings. + expect(codons.indexOf('ATG')).toBe(35); + }); + + it('keeps gap and unknown distinct, and different between the two streams', () => { + expect(CODON_GAP).toBe(64); + expect(CODON_UNKNOWN).toBe(65); + expect(NUM_CODON_TOKENS).toBe(66); + expect(AA_GAP).toBe(AA_LIST.indexOf('-')); + expect(AA_UNKNOWN).toBe(AA_LIST.indexOf('?')); + expect(AA_GAP).toBe(21); + expect(AA_UNKNOWN).toBe(22); + expect(CODON_GAP).not.toBe(AA_GAP); + }); + + it('sets the validity thresholds so that a GAP is not a valid observation', () => { + // forward() gates on (c < 64) & (a < 21). A gap is 64 / 21, so it fails both — deliberately. + expect(CODON_VALID_BELOW).toBe(CODON_GAP); + expect(AA_VALID_BELOW).toBe(AA_GAP); + expect(CODON_GAP < CODON_VALID_BELOW).toBe(false); + expect(AA_GAP < AA_VALID_BELOW).toBe(false); + }); + + it('pins the checkpoint defaults', () => { + expect(MAX_SPECIES_DEFAULT).toBe(512); + expect(MDS_COMPONENTS).toBe(4); + // window_size 1 means the central index is 0 and every window IS the site. An even window + // would put the scored codon off-centre. + expect(WINDOW_SIZE_DEFAULT).toBe(1); + expect(Math.floor(WINDOW_SIZE_DEFAULT / 2)).toBe(0); + }); + + it('lists the five inputs in forward() order and marks the site-invariant ones', () => { + expect(INPUT_NAMES).toEqual([ + 'msa_codons', + 'msa_aas', + 'dist_matrix', + 'mds_coords', + 'padding_mask' + ]); + // These three are computed once per alignment and expanded across sites. That is what makes + // batching every site into a single graph run cheap, so it is worth asserting rather than + // rediscovering. + const invariant = INPUT_SPEC.filter((s) => s.siteInvariant).map((s) => s.name); + expect(invariant).toEqual(['dist_matrix', 'mds_coords', 'padding_mask']); + }); + + it('names the five outputs the graph actually exposes', () => { + // Read from InferenceSession.outputNames on the real artifact, not from its README. This was an + // open question — the shipped driver reaches for the TRAIN branch to get raw ordinal logits — + // and the answer is that the export took the EVAL branch, so `lrt` arrives already decoded. + expect(OUTPUT_SPEC.map((o) => o.name)).toEqual([ + 'lrt', + 'alpha', + 'beta_neg', + 'beta_pos', + 'p_neg' + ]); + expect(OUTPUT_SPEC[0].note).toMatch(/already applied in-graph/); + }); + + it('records that the rate heads are log1p and need expm1 downstream', () => { + // The heads are softplus, and expm1(softplus(x)) == exp(x) — so these outputs are log1p(rate), + // not rates. Rendering them raw would understate every rate. p_neg is the exception. + for (const name of ['alpha', 'beta_neg', 'beta_pos']) { + expect(OUTPUT_SPEC.find((o) => o.name === name).note, name).toMatch(/expm1/); + } + expect(OUTPUT_SPEC.find((o) => o.name === 'p_neg').note).toMatch(/sigmoid/); + }); + + it('pins the artifact the contract was verified against', () => { + // A different export is not necessarily wrong, but the eval-mode conclusion was read off THIS + // graph, so swapping the model without revisiting this file is a mistake worth failing on. + expect(VERIFIED_MODEL_SHA256).toMatch(/^[0-9a-f]{64}$/); + }); + + it('cannot be mutated by a caller', () => { + expect(() => { + INPUT_SPEC.push({ name: 'nope' }); + }).toThrow(); + }); +}); + +describe('validateInputBundle', () => { + it('accepts a well-formed bundle', () => { + const r = check(validBundle()); + expect(r.errors).toEqual([]); + expect(r.ok).toBe(true); + }); + + it('reports a missing tensor by name', () => { + const b = validBundle(); + delete b.mds_coords; + expect(check(b).errors).toContain('mds_coords: missing'); + }); + + it('catches a transposed distance matrix shape', () => { + const b = validBundle(); + b.dist_matrix.dims = [BATCH, SPECIES, MDS_COMPONENTS + 1]; + expect(check(b).ok).toBe(false); + }); + + it('catches dims that are right but data that is short', () => { + // The shape says one thing and the buffer says another — onnxruntime will happily read past + // the end of the meaningful data or throw something opaque. + const b = validBundle(); + b.mds_coords.data = new Float32Array(4); + expect(check(b).errors.join(' ')).toMatch(/mds_coords: 4 elements/); + }); + + it('catches an out-of-vocabulary token', () => { + const b = validBundle(); + b.msa_codons.data = new BigInt64Array([0n, 5n, 63n, 66n, 1n, 2n, 3n, 65n]); + expect(check(b).errors.join(' ')).toMatch(/msa_codons\[3\] = 66/); + }); + + it('catches a FLIPPED padding mask — the error that passes every shape check', () => { + // This is the one worth having the whole validator for. `padding_mask` is TRUE for padded + // rows, which is the opposite of the "1 = keep" convention most attention APIs use. Invert it + // and every tensor is still perfectly well-formed; the model simply masks out every real + // taxon and attends to nothing. + const b = validBundle(); + b.padding_mask.data = new Uint8Array([1, 1, 1, 0, 1, 1, 1, 0]); + const r = check(b); + expect(r.ok).toBe(false); + expect(r.errors.join(' ')).toMatch(/TRUE = PADDED/); + }); + + it('catches a negative patristic distance, which is a real DM3 tree and a Python crash', () => { + // 5% of real DM3 trees carry a branch length <= -0.1; the Python inference path throws on + // them at predict_regression_nexus.py:955 rather than degrading. See treeSanitation.js. + const b = validBundle(); + b.dist_matrix.data[1] = -0.4; + const r = check(b); + expect(r.ok).toBe(false); + expect(r.errors.join(' ')).toMatch(/negative patristic distance/); + }); + + it('catches NaN before it reaches the graph', () => { + const b = validBundle(); + b.dist_matrix.data[2] = NaN; + expect(check(b).errors.join(' ')).toMatch(/dist_matrix\[2\] is NaN/); + }); + + it('catches a nonzero self-distance, which means the matrix is not a distance matrix', () => { + const b = validBundle(); + b.dist_matrix.data[0] = 0.5; // d(0,0) + expect(check(b).errors.join(' ')).toMatch(/self-distance/); + }); + + it('reports every independent problem, not just the first', () => { + // A port under development usually has several at once; stopping at the first costs a whole + // round trip per error. + const b = validBundle(); + delete b.msa_aas; + b.mds_coords.dims = [BATCH, SPECIES, 3]; + expect(check(b).errors.length).toBeGreaterThanOrEqual(2); + }); +}); diff --git a/src/test/axomeme-patristic.test.js b/src/test/axomeme-patristic.test.js new file mode 100644 index 0000000..ed5946e --- /dev/null +++ b/src/test/axomeme-patristic.test.js @@ -0,0 +1,265 @@ +/** + * Tests for the newick parser and patristic distances — the first half of the AxoMEME preprocessing + * port. + * + * The distances here are hand-computed from the newick, not recorded from this code. That + * distinction is the whole point: an expectation captured from the implementation asserts only that + * the implementation is deterministic, which it would be even if the arithmetic were wrong. Every + * number below can be checked by reading the tree string. + * + * The cross-implementation check against Python lives in scripts/axomeme/verify_preprocessing.py and + * is what actually proves parity; these tests are what make a failure there interpretable. + */ +import { describe, it, expect } from 'vitest'; +import { parseNewick, leafIndex, normalizeTaxonName } from '../lib/services/axomeme/newick.js'; +import { + rootDistances, + patristicRow, + patristicMatrix, + maxPdSelect +} from '../lib/services/axomeme/patristic.js'; + +/** ((A:0.1,B:0.2):0.05,C:0.3); — the worked example used throughout. */ +const SIMPLE = '((A:0.1,B:0.2):0.05,C:0.3);'; + +/** Node index of the leaf named `n`. */ +const leafOf = (tree, n) => leafIndex(tree).index.get(n); + +describe('parseNewick', () => { + it('builds the topology of a nested tree', () => { + const t = parseNewick(SIMPLE); + const { index } = leafIndex(t); + expect([...index.keys()].sort()).toEqual(['A', 'B', 'C']); + // A and B share a parent; C hangs off the root. + expect(t.parent[index.get('A')]).toBe(t.parent[index.get('B')]); + expect(t.parent[index.get('C')]).toBe(t.root); + expect(t.parent[t.parent[index.get('A')]]).toBe(t.root); + }); + + it('handles arbitrary nesting on both sides', () => { + const t = parseNewick('((A,B),(C,D));'); + const { index } = leafIndex(t); + expect([...index.keys()].sort()).toEqual(['A', 'B', 'C', 'D']); + expect(t.parent[index.get('A')]).toBe(t.parent[index.get('B')]); + expect(t.parent[index.get('C')]).toBe(t.parent[index.get('D')]); + expect(t.parent[index.get('A')]).not.toBe(t.parent[index.get('C')]); + }); + + it('nests a clade that appears after a leaf', () => { + // (A,(B,C)) exercises the branch where ',' and '(' arrive back to back. + const t = parseNewick('(A,(B,C));'); + const { index } = leafIndex(t); + expect(t.parent[index.get('A')]).toBe(t.root); + expect(t.parent[index.get('B')]).toBe(t.parent[index.get('C')]); + expect(t.parent[index.get('B')]).not.toBe(t.root); + }); + + it('reads plain, scientific and NEGATIVE branch lengths', () => { + const t = parseNewick('(a:1.5e-2,b:3.0E-3,c:-0.5);'); + const { index } = leafIndex(t); + expect(t.branchLength[index.get('a')]).toBeCloseTo(0.015, 12); + expect(t.branchLength[index.get('b')]).toBeCloseTo(0.003, 12); + // Preserved, NOT clamped — DM3's own NJ emits these and hiding them here would turn a loud + // downstream failure into a quiet wrong answer. + expect(t.branchLength[index.get('c')]).toBe(-0.5); + }); + + it('treats a missing branch length as 0, matching `branch_length or 0.0`', () => { + const t = parseNewick('((A,B),C);'); + expect(Array.from(t.branchLength).every((v) => v === 0)).toBe(true); + expect(Array.from(rootDistances(t)).every((v) => v === 0)).toBe(true); + }); + + it('does not mistake a bootstrap value for a taxon', () => { + // )95: is a label on an internal node. Reading it as a name invents a species. + const t = parseNewick('((A:0.1,B:0.2)95:0.05,C:0.3);'); + const { index } = leafIndex(t); + expect([...index.keys()].sort()).toEqual(['A', 'B', 'C']); + expect(index.has('95')).toBe(false); + // The label is kept on the internal node, just not treated as a leaf. + expect(t.name[t.parent[index.get('A')]]).toBe('95'); + }); + + it('keeps a quoted label containing a colon intact', () => { + // The entire reason newick quoting exists, and the case a naive split on ':' corrupts. + const t = parseNewick("(('Homo:sapiens':0.1,b:0.2):0.05);"); + const { index } = leafIndex(t); + expect(index.has('Homo:sapiens')).toBe(true); + expect(t.branchLength[index.get('Homo:sapiens')]).toBeCloseTo(0.1, 12); + }); + + it('strips newick comments rather than reading them as labels', () => { + const t = parseNewick('((A[&&NHX:x=1]:0.1,B:0.2):0.05,C:0.3);'); + expect([...leafIndex(t).index.keys()].sort()).toEqual(['A', 'B', 'C']); + }); + + it('parses without a trailing semicolon, and a bare single taxon', () => { + expect([...leafIndex(parseNewick('(A:0.1,B:0.2)')).index.keys()].sort()).toEqual(['A', 'B']); + const solo = parseNewick('A:0.1;'); + expect([...leafIndex(solo).index.keys()]).toEqual(['A']); + }); + + it('rejects empty input rather than returning an empty tree', () => { + expect(() => parseNewick('')).toThrow(/empty/); + expect(() => parseNewick(' ')).toThrow(/empty/); + }); + + it('lists leaves in PREORDER, matching Biopython get_terminals()', () => { + // Not cosmetic. The reference resolves duplicate tip names by dict overwrite, so the winner is + // the last leaf in THIS order; a breadth-first walk yields the same set and a different winner. + const t = parseNewick('((A:0.1,B:0.2):0.05,C:0.3);'); + expect(t.leaves.map((n) => t.name[n])).toEqual(['A', 'B', 'C']); + const t2 = parseNewick('(A,((B,C),D));'); + expect(t2.leaves.map((n) => t2.name[n])).toEqual(['A', 'B', 'C', 'D']); + }); + + it('resolves a duplicate tip name to the LAST leaf, and reports it', () => { + // Matches `{leaf.name: leaf for leaf in leaves}` — later entries overwrite earlier. Measured: + // 3 of 270 real DM3 trees have duplicate tips, and first-wins disagreed with the reference on + // every one of them. `duplicates` is what lets a caller refuse; the index itself stays + // faithful. + const t = parseNewick('((A:0.1,A:0.2):0.05,C:0.3);'); + const { index, duplicates } = leafIndex(t); + expect(duplicates).toEqual(['A']); + expect(index.size).toBe(2); + // The SECOND 'A' — the one with branch length 0.2. + expect(t.branchLength[index.get('A')]).toBeCloseTo(0.2, 12); + }); +}); + +describe('normalizeTaxonName', () => { + it('removes every quote anywhere, matching the reference', () => { + // Python's str.replace removes ALL occurrences; the reference does + // s.replace("'", "").replace('"', '').strip(). + expect(normalizeTaxonName("'Homo sapiens'")).toBe('Homo sapiens'); + expect(normalizeTaxonName("Homo_'sapiens'")).toBe('Homo_sapiens'); + expect(normalizeTaxonName(' "x" ')).toBe('x'); + }); +}); + +describe('rootDistances and patristic distances', () => { + it('accumulates root distances down the tree', () => { + const t = parseNewick(SIMPLE); + const d = rootDistances(t); + expect(d[leafOf(t, 'A')]).toBeCloseTo(0.15, 12); // 0.05 + 0.1 + expect(d[leafOf(t, 'B')]).toBeCloseTo(0.25, 12); // 0.05 + 0.2 + expect(d[leafOf(t, 'C')]).toBeCloseTo(0.3, 12); + expect(d[t.root]).toBe(0); + }); + + it('computes hand-checkable pairwise distances', () => { + const t = parseNewick(SIMPLE); + const nodes = ['A', 'B', 'C'].map((n) => leafOf(t, n)); + const m = patristicMatrix(t, nodes); + const at = (i, j) => m[i * 3 + j]; + expect(at(0, 1)).toBeCloseTo(0.3, 12); // A-B: 0.1 + 0.2 + expect(at(0, 2)).toBeCloseTo(0.45, 12); // A-C: 0.1 + 0.05 + 0.3 + expect(at(1, 2)).toBeCloseTo(0.55, 12); // B-C: 0.2 + 0.05 + 0.3 + }); + + it('is symmetric with a zero diagonal', () => { + const t = parseNewick('((A:0.1,B:0.2):0.05,(C:0.3,D:0.15):0.02);'); + const nodes = ['A', 'B', 'C', 'D'].map((n) => leafOf(t, n)); + const m = patristicMatrix(t, nodes); + for (let i = 0; i < 4; i++) { + expect(m[i * 4 + i]).toBe(0); + for (let j = 0; j < 4; j++) expect(m[i * 4 + j]).toBeCloseTo(m[j * 4 + i], 12); + } + }); + + it('reuses its ancestor marker across rows without leaking marks', () => { + // The stamped marker is the one piece of state shared between rows. If a stale generation + // leaked, an LCA would resolve to a node on the PREVIOUS row's path and distances would come + // out too small — so compute a matrix (shared marker) and compare against fresh single rows. + const t = parseNewick('(((A:0.1,B:0.2):0.05,C:0.3):0.01,(D:0.4,E:0.05):0.2);'); + const names = ['A', 'B', 'C', 'D', 'E']; + const nodes = names.map((n) => leafOf(t, n)); + const shared = patristicMatrix(t, nodes); + const rd = rootDistances(t); + for (let i = 0; i < nodes.length; i++) { + const fresh = patristicRow(t, rd, nodes[i], nodes); // no marker -> its own + for (let j = 0; j < nodes.length; j++) { + expect(shared[i * nodes.length + j]).toBeCloseTo(fresh[j], 12); + } + } + }); + + it('propagates a negative branch length into the distance instead of hiding it', () => { + const t = parseNewick('((A:-0.5,B:0.2):0.05,C:0.3);'); + const nodes = ['A', 'B', 'C'].map((n) => leafOf(t, n)); + const m = patristicMatrix(t, nodes); + expect(m[0 * 3 + 1]).toBeCloseTo(-0.3, 12); // A-B: -0.5 + 0.2 + expect(m[0 * 3 + 2]).toBeCloseTo(-0.15, 12); // A-C: -0.5 + 0.05 + 0.3 + }); + + it('gives every pair distance 0 on a topology-only tree', () => { + const t = parseNewick('((A,B),C);'); + const nodes = ['A', 'B', 'C'].map((n) => leafOf(t, n)); + expect(Array.from(patristicMatrix(t, nodes)).every((v) => v === 0)).toBe(true); + }); + + it('handles a deep ladder tree without recursing', () => { + // 3,000 nested clades. The Python reference recurses here and dies at its frame limit; this + // port must not, because DM3 accepts uploads far larger than 1,000 taxa. + const N = 3000; + let s = 'L0:0.001'; + for (let i = 1; i < N; i++) s = `(${s},L${i}:0.001)`; + const t = parseNewick(s + ';'); + const idx = leafIndex(t).index; + expect(idx.size).toBe(N); + const d = rootDistances(t); + // L0 is the deepest tip: N-1 internal branches (all 0, no lengths given) plus its own 0.001. + expect(Number.isFinite(d[idx.get('L0')])).toBe(true); + expect(d[idx.get(`L${N - 1}`)]).toBeCloseTo(0.001, 12); + }); +}); + +describe('maxPdSelect', () => { + it('returns everything, in order, when under the cap', () => { + const t = parseNewick(SIMPLE); + const nodes = ['A', 'B', 'C'].map((n) => leafOf(t, n)); + expect(maxPdSelect(t, nodes, 512).selected).toEqual([0, 1, 2]); + }); + + it('seeds at index 0 and then takes the farthest point', () => { + // A-B are close, C and D are far. Seeded at A (index 0), the next pick is whichever is + // farthest from A, then the one farthest from {A, that}. + const t = parseNewick('((A:0.01,B:0.01):0.05,(C:1.0,D:2.0):0.5);'); + const nodes = ['A', 'B', 'C', 'D'].map((n) => leafOf(t, n)); + const { selected } = maxPdSelect(t, nodes, 3); + expect(selected[0]).toBe(0); // always the seed + expect(selected[1]).toBe(3); // D, farthest from A + expect(selected[2]).toBe(2); // C, farthest from {A, D} + }); + + it('is sensitive to input ORDER, because the seed is index 0 — not a defect this layer fixes', () => { + // The reference always seeds at the first taxon in alignment order, so reordering the + // sequences in an upload changes which taxa the model sees. Pinned so nobody "fixes" it into + // divergence from the model's training-time behaviour. + const t = parseNewick('((A:0.01,B:0.01):0.05,(C:1.0,D:2.0):0.5);'); + const asIs = ['A', 'B', 'C', 'D'].map((n) => leafOf(t, n)); + const rotated = ['C', 'D', 'A', 'B'].map((n) => leafOf(t, n)); + const a = maxPdSelect(t, asIs, 2).selected.map((i) => t.name[asIs[i]]); + const b = maxPdSelect(t, rotated, 2).selected.map((i) => t.name[rotated[i]]); + expect(a).toEqual(['A', 'D']); + expect(b).toEqual(['C', 'D']); + expect(a).not.toEqual(b); + }); + + it('reports duplicates on an all-zero distance matrix instead of hiding them', () => { + // Every selected index has minDist 0, so once all candidates are 0 argmax returns index 0 + // forever and one taxon fills every slot. Reproduced (the model was trained with it) but + // counted, so a caller can refuse. + const t = parseNewick('((A,B),(C,D));'); // no branch lengths -> all distances 0 + const nodes = ['A', 'B', 'C', 'D'].map((n) => leafOf(t, n)); + const { selected, duplicates } = maxPdSelect(t, nodes, 3); + expect(selected).toEqual([0, 0, 0]); + expect(duplicates).toBe(2); + }); + + it('selects exactly maxSpecies when over the cap', () => { + const t = parseNewick('((A:0.1,B:0.2):0.05,(C:0.3,D:0.15):0.02);'); + const nodes = ['A', 'B', 'C', 'D'].map((n) => leafOf(t, n)); + expect(maxPdSelect(t, nodes, 2).selected).toHaveLength(2); + }); +}); diff --git a/src/test/axomeme-postprocess.test.js b/src/test/axomeme-postprocess.test.js new file mode 100644 index 0000000..7b0cb0d --- /dev/null +++ b/src/test/axomeme-postprocess.test.js @@ -0,0 +1,231 @@ +/** + * Tests for AxoMEME output postprocessing. + * + * The transformations here are the ones that turn model outputs into numbers a researcher reads, and + * every one of them is a place where "looks plausible" and "correct" differ by a lot. A missing + * expm1 does not crash; it just reports every rate too low. An invariant site scored rather than + * zeroed does not crash; it reports selection at a site where nothing varies. + * + * Expected values are derived from the reference's arithmetic, not recorded from this code. + */ +import { describe, it, expect } from 'vitest'; +import { + buildPredictions, + isSiteVariable, + siteVariability, + CALL_DEFAULTS, + NEUTRAL_CALL +} from '../lib/services/axomeme/postprocess.js'; + +/** Raw graph outputs for `n` sites, all heads constant unless overridden. */ +function outputs(n, over = {}) { + const fill = (v) => new Float32Array(n).fill(v); + return { + lrt: over.lrt ?? fill(1), + alpha: over.alpha ?? fill(0), + beta_neg: over.beta_neg ?? fill(0), + beta_pos: over.beta_pos ?? fill(0), + p_neg: over.p_neg ?? fill(0.25) + }; +} + +describe('isSiteVariable', () => { + it('is true when more than one amino acid is present', () => { + expect(isSiteVariable(['ATG', 'TTA'])).toBe(true); // M, L + }); + + it('is false when every codon codes the same amino acid', () => { + expect(isSiteVariable(['TTA', 'TTG', 'CTA'])).toBe(false); // all Leucine + expect(isSiteVariable(['ATG', 'ATG'])).toBe(false); + }); + + it('is TRUE for a serine island — synonymous but selection-relevant', () => { + // The condition that is easy to drop. Serine is the one residue whose codons occupy two + // disjoint blocks (TCN and AGY), so switching between them is synonymous yet needs multiple + // substitutions. Every codon here is Serine, so the amino-acid test alone says "invariant". + expect(isSiteVariable(['TCA', 'AGC'])).toBe(true); + expect(isSiteVariable(['TCT', 'AGT'])).toBe(true); + // ...but only when BOTH families are present. + expect(isSiteVariable(['TCA', 'TCG'])).toBe(false); + expect(isSiteVariable(['AGC', 'AGT'])).toBe(false); + }); + + it('is false for an empty site', () => { + expect(isSiteVariable([])).toBe(false); + expect(isSiteVariable(null)).toBe(false); + }); +}); + +describe('siteVariability', () => { + it('ignores gapped and ambiguous codons when judging a site', () => { + // Site 0: ATG / --- / ANT -> only one usable codon, so not variable. + // Site 1: TTA / TTG / TTT -> Leu, Leu, Phe -> variable. + const flags = siteVariability(['ATGTTA', '---TTG', 'ANTTTT'], 2); + expect(flags).toEqual([false, true]); + }); + + it('lets a short sequence contribute nothing past its end', () => { + const flags = siteVariability(['ATGTTA', 'ATG'], 2); + expect(flags[1]).toBe(false); // only one codon reaches site 1 + }); +}); + +describe('buildPredictions', () => { + const sites = (n, variable = true) => ({ + refCodons: new Array(n).fill('ATG'), + variable: new Array(n).fill(variable) + }); + + it('applies expm1 to the rate heads', () => { + // The heads are softplus, so the graph emits log1p(rate). expm1(1) = e - 1 = 1.7182818... + const rows = buildPredictions( + outputs(1, { alpha: new Float32Array([1]), beta_pos: new Float32Array([2]) }), + sites(1) + ); + expect(rows[0].alphaDs).toBeCloseTo(Math.E - 1, 6); + expect(rows[0].betaPosDn).toBeCloseTo(Math.exp(2) - 1, 6); + }); + + it('reports p_pos as 1 - p_neg', () => { + const rows = buildPredictions(outputs(1, { p_neg: new Float32Array([0.25]) }), sites(1)); + expect(rows[0].pPos).toBeCloseTo(0.75, 6); + }); + + it('treats lrt as the LRT and DERIVES the log, not the other way round', () => { + // Worth pinning: the export is eval mode, so `lrt` is already ordinal-decoded and is the LRT + // itself. The reference computes predicted_log_lrt = log1p(predicted_lrt). + const rows = buildPredictions(outputs(1, { lrt: new Float32Array([4]) }), sites(1)); + expect(rows[0].lrt).toBeCloseTo(4, 6); + expect(rows[0].logLrt).toBeCloseTo(Math.log1p(4), 6); + }); + + it('clamps a negative lrt or rate to zero', () => { + const rows = buildPredictions( + outputs(1, { lrt: new Float32Array([-2]), alpha: new Float32Array([-3]) }), + sites(1) + ); + expect(rows[0].lrt).toBe(0); + expect(rows[0].alphaDs).toBe(0); + }); + + it('ZEROES an invariant site instead of scoring it', () => { + // The reference zeroes before consulting the model. A large model output at an invariant site + // must not reach the user — those zeros mean "not applicable", not "no selection". + const rows = buildPredictions(outputs(2, { lrt: new Float32Array([99, 99]) }), { + refCodons: ['ATG', 'ATG'], + variable: [false, true] + }); + expect(rows[0].lrt).toBe(0); + expect(rows[0].call).toBe(NEUTRAL_CALL); + expect(rows[0].isVariable).toBe(false); + expect(rows[1].lrt).toBe(99); + }); + + it('excludes invariant sites from the local statistics', () => { + // If the zeroed sites were included they would drag the mean down and inflate every z-score. + const lrt = new Float32Array([0, 10, 20, 30]); + const withInvariant = buildPredictions(outputs(4, { lrt }), { + refCodons: new Array(4).fill('ATG'), + variable: [false, true, true, true] + }); + const onlyVariable = buildPredictions(outputs(3, { lrt: new Float32Array([10, 20, 30]) }), { + refCodons: new Array(3).fill('ATG'), + variable: [true, true, true] + }); + expect(withInvariant[1].zScore).toBeCloseTo(onlyVariable[0].zScore, 10); + expect(withInvariant[3].zScore).toBeCloseTo(onlyVariable[2].zScore, 10); + }); + + it('computes z-scores with the POPULATION standard deviation', () => { + // np.std, not pandas' sample std — the difference is a factor of sqrt(n/(n-1)), which for + // three sites is 22%. + const rows = buildPredictions(outputs(3, { lrt: new Float32Array([1, 2, 3]) }), sites(3)); + // mean 2, population std = sqrt(2/3) = 0.8165 + expect(rows[0].zScore).toBeCloseTo(-1 / Math.sqrt(2 / 3), 6); + expect(rows[1].zScore).toBeCloseTo(0, 10); + expect(rows[2].zScore).toBeCloseTo(1 / Math.sqrt(2 / 3), 6); + }); + + it('gives a zero z-score when every site is identical, rather than dividing by zero', () => { + const rows = buildPredictions(outputs(3, { lrt: new Float32Array([5, 5, 5]) }), sites(3)); + for (const r of rows) expect(r.zScore).toBe(0); + }); + + it('averages percentile ranks across ties, matching pandas', () => { + // [10, 20, 20, 40]: the tied pair share rank (2+3)/2 = 2.5 -> 62.5%. + const rows = buildPredictions( + outputs(4, { lrt: new Float32Array([10, 20, 20, 40]) }), + sites(4) + ); + expect(rows[0].percentile).toBeCloseTo(25, 6); + expect(rows[1].percentile).toBeCloseTo(62.5, 6); + expect(rows[2].percentile).toBeCloseTo(62.5, 6); + expect(rows[3].percentile).toBeCloseTo(100, 6); + }); + + it('calls tiers off the LRT gates by default', () => { + expect(CALL_DEFAULTS.mode).toBe('pvalue'); + expect(CALL_DEFAULTS.tier1LrtGate).toBe(4.45); + expect(CALL_DEFAULTS.tier2LrtGate).toBe(3.12); + const rows = buildPredictions( + outputs(4, { lrt: new Float32Array([5.0, 3.5, 3.13, 1.0]) }), + sites(4) + ); + expect(rows[0].call).toMatch(/Tier 1/); + expect(rows[1].call).toMatch(/Tier 2/); + expect(rows[2].call).toMatch(/Tier 2/); + expect(rows[3].call).toBe(NEUTRAL_CALL); + }); + + it('does NOT call a site whose float32 LRT lands just under the gate', () => { + // A boundary worth pinning rather than smoothing. The gates are float64 literals but the model + // emits float32, and 3.12 is not representable: Float32Array([3.12])[0] is 3.119999885559082, + // which fails `>= 3.12`. The reference behaves identically — torch's .item() widens the same + // float32 to float64 — so this is faithful, not a rounding bug to fix. It does mean a site + // sitting exactly on a gate falls to the lower tier. + const exact = new Float32Array([3.12]); + expect(exact[0]).toBeLessThan(3.12); + const rows = buildPredictions(outputs(1, { lrt: exact }), sites(1)); + expect(rows[0].call).toBe(NEUTRAL_CALL); + }); + + it('supports the zscore and percentile call modes', () => { + // NOTE the site count. With a POPULATION standard deviation, |z| is bounded by sqrt(n-1), so + // zscore mode cannot call anything at all on fewer than 5 variable sites — at n=4 the maximum + // possible z is 1.732, below even the Tier 2 threshold of 2.0. Ten sites clears it. + const lrt = new Float32Array([1, 1, 1, 1, 1, 1, 1, 1, 1, 100]); + const z = buildPredictions(outputs(10, { lrt }), sites(10), { mode: 'zscore' }); + expect(z[9].call).toMatch(/Tier/); // the outlier + expect(z[0].call).toBe(NEUTRAL_CALL); + + const p = buildPredictions(outputs(10, { lrt }), sites(10), { + mode: 'percentile', + tier1Percentile: 90, + tier2Percentile: 70 + }); + expect(p[9].call).toMatch(/Tier 1/); + expect(p[0].call).toBe(NEUTRAL_CALL); + }); + + it('zscore mode is structurally unable to call short alignments', () => { + // The consequence of the sqrt(n-1) bound, stated as its own fact so it is not rediscovered as + // "the model found nothing". Four variable sites, one of them enormous, and nothing calls. + const rows = buildPredictions( + outputs(4, { lrt: new Float32Array([1, 2, 3, 1000]) }), + sites(4), + { mode: 'zscore' } + ); + expect(Math.max(...rows.map((r) => r.zScore))).toBeLessThan(Math.sqrt(3) + 1e-9); + expect(rows.every((r) => r.call === NEUTRAL_CALL)).toBe(true); + }); + + it('does not invent an amino acid for a gapped reference codon', () => { + const rows = buildPredictions(outputs(1), { refCodons: ['A-G'], variable: [true] }); + expect(rows[0].refAa).toBe('?'); + }); + + it('numbers sites from 1', () => { + const rows = buildPredictions(outputs(3), sites(3)); + expect(rows.map((r) => r.site)).toEqual([1, 2, 3]); + }); +}); diff --git a/src/test/axomeme-session.test.js b/src/test/axomeme-session.test.js new file mode 100644 index 0000000..7e57199 --- /dev/null +++ b/src/test/axomeme-session.test.js @@ -0,0 +1,171 @@ +/** + * Tests for AxoMEME session loading. + * + * The load path is where 17 MB of runtime and model either does or does not reach a user, and where a + * swapped artifact either is or is not caught. Both failure modes are silent — the wrong model still + * returns five plausible numbers per site — so every guard here is exercised in both directions. + * + * The ONNX runtime is faked. These tests are about the loading policy, not about inference; the real + * graph is exercised by the end-to-end check, which needs the 3.78 MB artifact and a browser. + */ +import { describe, it, expect, beforeEach, vi } from 'vitest'; +import { + loadSession, + resetSession, + isSessionLoaded, + runSites, + MODEL_URL +} from '../lib/services/axomeme/session.js'; +import { VERIFIED_MODEL_SHA256 } from '../lib/services/axomeme/modelContract.js'; + +/** Bytes whose sha256 is the pinned value — impossible to forge, so tests bypass the check instead. */ +const SOME_BYTES = new Uint8Array([1, 2, 3, 4]).buffer; + +function fakeOrt( + inputNames = ['msa_codons', 'msa_aas', 'dist_matrix', 'mds_coords', 'padding_mask'] +) { + const created = []; + return { + created, + Tensor: class { + constructor(type, data, dims) { + this.type = type; + this.data = data; + this.dims = dims; + } + }, + InferenceSession: { + create: vi.fn(async (bytes) => { + created.push(bytes.byteLength ?? bytes.length); + return { + inputNames, + outputNames: ['lrt', 'alpha', 'beta_neg', 'beta_pos', 'p_neg'], + run: vi.fn(async (feeds) => { + const n = feeds.msa_codons.dims[0]; + const f = () => ({ data: new Float32Array(n).fill(1) }); + return { lrt: f(), alpha: f(), beta_neg: f(), beta_pos: f(), p_neg: f() }; + }) + }; + }) + } + }; +} + +const okFetch = (buffer = SOME_BYTES) => + vi.fn(async () => ({ ok: true, status: 200, statusText: 'OK', arrayBuffer: async () => buffer })); + +describe('loadSession', () => { + beforeEach(() => resetSession()); + + it('does not load anything just by importing the module', () => { + // The whole cost discipline in one assertion: fourteen other methods import nothing by + // touching this file. + expect(isSessionLoaded()).toBe(false); + }); + + it('fetches the model from static/, not from the bundle', () => { + // A bundled model would be a build-time import and would land in the JS graph. + expect(MODEL_URL).toMatch(/^\/models\//); + expect(MODEL_URL).toMatch(/\.onnx$/); + }); + + it('loads and reports the byte count', async () => { + const ort = fakeOrt(); + const r = await loadSession({ ort, fetchImpl: okFetch(), verifyHash: false }); + expect(r.bytes).toBe(4); + expect(ort.InferenceSession.create).toHaveBeenCalledTimes(1); + }); + + it('REFUSES a model whose hash is not the verified one', async () => { + // The contract's conclusions — eval-mode export, lrt already decoded, rate heads in log1p + // space — were read off one specific graph. A different one may be fine; nothing here knows + // that, and guessing produces wrong numbers rather than an error. + const err = await loadSession({ ort: fakeOrt(), fetchImpl: okFetch() }).catch((e) => e); + expect(err).toBeInstanceOf(Error); + expect(err.message).toMatch(/hash mismatch/); + expect(err.message).toContain(VERIFIED_MODEL_SHA256); + // And it must say what to do about it, not just that it failed. + expect(err.message).toMatch(/VERIFIED_MODEL_SHA256/); + }); + + it('refuses a graph that is missing a contract input', async () => { + const ort = fakeOrt(['msa_codons', 'msa_aas', 'dist_matrix']); // no mds_coords / padding_mask + const err = await loadSession({ ort, fetchImpl: okFetch(), verifyHash: false }).catch((e) => e); + expect(err.message).toMatch(/missing expected inputs/); + expect(err.message).toMatch(/mds_coords/); + expect(err.message).toMatch(/padding_mask/); + }); + + it('reports a failed fetch with its status', async () => { + const fetchImpl = vi.fn(async () => ({ ok: false, status: 404, statusText: 'Not Found' })); + const err = await loadSession({ ort: fakeOrt(), fetchImpl, verifyHash: false }).catch((e) => e); + expect(err.message).toMatch(/404/); + }); + + it('memoises, so a second alignment does not re-download 17 MB', async () => { + const ort = fakeOrt(); + const fetchImpl = okFetch(); + // The memo only applies to the production path (no overrides), so drive it through a module + // where the default URL is used but the runtime is injected — the override check is by + // identity of the options, so assert on the fetch count for the overridden path instead. + await loadSession({ ort, fetchImpl, verifyHash: false }); + await loadSession({ ort, fetchImpl, verifyHash: false }); + // Overridden calls deliberately bypass the memo so tests stay independent. + expect(fetchImpl).toHaveBeenCalledTimes(2); + expect(isSessionLoaded()).toBe(false); + }); + + it('does not memoise a FAILURE, so a transient error is recoverable', async () => { + // Caching a rejected promise would disable the feature for the life of the page. + await loadSession({ ort: fakeOrt(), fetchImpl: okFetch() }).catch(() => {}); + expect(isSessionLoaded()).toBe(false); + }); +}); + +describe('runSites', () => { + it('feeds every tensor with the dtype the graph expects', async () => { + const ort = fakeOrt(); + const { session } = await loadSession({ ort, fetchImpl: okFetch(), verifyHash: false }); + const B = 2; + const N = 3; + const bundle = { + msa_codons: { data: new BigInt64Array(B * N), dims: [B, N, 1] }, + msa_aas: { data: new BigInt64Array(B * N), dims: [B, N, 1] }, + dist_matrix: { data: new Float32Array(B * N * N), dims: [B, N, N] }, + mds_coords: { data: new Float32Array(B * N * 4), dims: [B, N, 4] }, + padding_mask: { data: new Uint8Array(B * N), dims: [B, N] } + }; + const out = await runSites(session, bundle, ort); + const feeds = session.run.mock.calls[0][0]; + // int64 for the token streams: passing float32 of the same values is a type error at + // session.run, not a silent coercion. + expect(feeds.msa_codons.type).toBe('int64'); + expect(feeds.msa_aas.type).toBe('int64'); + expect(feeds.dist_matrix.type).toBe('float32'); + expect(feeds.mds_coords.type).toBe('float32'); + expect(feeds.padding_mask.type).toBe('bool'); + expect(Object.keys(out).sort()).toEqual(['alpha', 'beta_neg', 'beta_pos', 'lrt', 'p_neg']); + expect(out.lrt).toHaveLength(B); + }); + + it('runs ALL sites in one call, not one call per site', async () => { + // The three phylo tensors are per-alignment, so batching is nearly free. The reference driver + // loops one forward pass per codon; for a 441-site alignment that is 441x the overhead. + const ort = fakeOrt(); + const { session } = await loadSession({ ort, fetchImpl: okFetch(), verifyHash: false }); + const B = 441; + const N = 2; + await runSites( + session, + { + msa_codons: { data: new BigInt64Array(B * N), dims: [B, N, 1] }, + msa_aas: { data: new BigInt64Array(B * N), dims: [B, N, 1] }, + dist_matrix: { data: new Float32Array(B * N * N), dims: [B, N, N] }, + mds_coords: { data: new Float32Array(B * N * 4), dims: [B, N, 4] }, + padding_mask: { data: new Uint8Array(B * N), dims: [B, N] } + }, + ort + ); + expect(session.run).toHaveBeenCalledTimes(1); + }); +}); diff --git a/src/test/axomeme-tokenizer.test.js b/src/test/axomeme-tokenizer.test.js new file mode 100644 index 0000000..77e5907 --- /dev/null +++ b/src/test/axomeme-tokenizer.test.js @@ -0,0 +1,184 @@ +/** + * Tests for AxoMEME codon/amino-acid tokenisation. + * + * The block that matters most here is the last one. This port deliberately implements the TRAINING + * tokenizer and deliberately does NOT implement the one in the handoff's inference driver, because + * those two disagree on 63 of 64 codons. That is the kind of decision a future reader "corrects" in + * good faith — the driver is, after all, the script the ML team ships — so the divergence is pinned + * from both sides: these tests fail if we drift away from training, AND they fail if someone aligns + * us to the driver. + * + * Everything else is the ordinary business of a tokenizer, with one thing worth stating: the + * expected translations are the standard genetic code, checkable against any codon table, not values + * recorded from this implementation. + */ +import { describe, it, expect } from 'vitest'; +import { + CODON_LIST, + CODON_TO_IDX, + GENETIC_CODE, + AA_TO_IDX, + codonToken, + aaToken, + tokenizeSequence +} from '../lib/services/axomeme/tokenizer.js'; +import { + CODON_GAP, + CODON_UNKNOWN, + AA_GAP, + AA_UNKNOWN +} from '../lib/services/axomeme/modelContract.js'; + +describe('the codon vocabulary', () => { + it('is the 64 codons in TCAG order', () => { + expect(CODON_LIST).toHaveLength(64); + expect(new Set(CODON_LIST).size).toBe(64); + expect(CODON_LIST[0]).toBe('TTT'); + expect(CODON_LIST[63]).toBe('GGG'); + // Third position varies fastest, matching [a][b][c] with c innermost. + expect(CODON_LIST.slice(0, 4)).toEqual(['TTT', 'TTC', 'TTA', 'TTG']); + }); + + it('includes stop codons, which are real tokens and not sentinels', () => { + for (const stop of ['TAA', 'TAG', 'TGA']) { + expect(CODON_TO_IDX.has(stop)).toBe(true); + expect(codonToken(stop)).toBeLessThan(64); + } + }); +}); + +describe('the genetic code', () => { + it('translates the standard code', () => { + // Spot values anyone can check against a codon table. + const expected = { + ATG: 'M', // Met / start + TGG: 'W', // the other single-codon residue + TTT: 'F', + TTA: 'L', + AAA: 'K', + GGG: 'G', + TAA: '*', + TAG: '*', + TGA: '*' + }; + for (const [codon, aa] of Object.entries(expected)) { + expect(GENETIC_CODE.get(codon), codon).toBe(aa); + } + }); + + it('covers every codon and has exactly three stops', () => { + expect(GENETIC_CODE.size).toBe(64); + for (const c of CODON_LIST) expect(GENETIC_CODE.get(c), c).toBeTruthy(); + expect( + [...GENETIC_CODE.entries()] + .filter(([, a]) => a === '*') + .map(([c]) => c) + .sort() + ).toEqual(['TAA', 'TAG', 'TGA']); + }); + + it('uses "*" for a stop, so it lands on a real AA token rather than unknown', () => { + // The handoff's driver writes stops as '_' while its AA_LIST contains '*', so stops fall + // through to 22 (unknown) there. Here they translate to 20. + expect(AA_TO_IDX.get('*')).toBe(20); + expect(aaToken('TAA')).toBe(20); + expect(aaToken('TAA')).not.toBe(AA_UNKNOWN); + }); +}); + +describe('codonToken', () => { + it('is case insensitive', () => { + expect(codonToken('atg')).toBe(codonToken('ATG')); + }); + + it('treats a gap ANYWHERE as a gap, not as unknown', () => { + // `'-' in codon` is a substring test in the reference, so a partial gap counts. + expect(codonToken('---')).toBe(CODON_GAP); + expect(codonToken('A-T')).toBe(CODON_GAP); + expect(codonToken('AT-')).toBe(CODON_GAP); + // And a gap outranks the length and N tests: this is length 2 AND gapped. + expect(codonToken('A-')).toBe(CODON_GAP); + }); + + it('maps ambiguity and wrong lengths to unknown', () => { + expect(codonToken('ANT')).toBe(CODON_UNKNOWN); + expect(codonToken('NNN')).toBe(CODON_UNKNOWN); + expect(codonToken('AT')).toBe(CODON_UNKNOWN); + expect(codonToken('ATGC')).toBe(CODON_UNKNOWN); + expect(codonToken('')).toBe(CODON_UNKNOWN); + expect(codonToken('XYZ')).toBe(CODON_UNKNOWN); + }); +}); + +describe('aaToken', () => { + it('translates, and separates gap from unknown', () => { + expect(aaToken('ATG')).toBe(AA_TO_IDX.get('M')); + expect(aaToken('---')).toBe(AA_GAP); + expect(aaToken('A-T')).toBe(AA_GAP); + expect(aaToken('ANT')).toBe(AA_UNKNOWN); + expect(aaToken('AT')).toBe(AA_UNKNOWN); + expect(AA_GAP).not.toBe(AA_UNKNOWN); + }); + + it('rejects "?" where codonToken does not — the reference asymmetry, same answer either way', () => { + expect(aaToken('?A?')).toBe(AA_UNKNOWN); + // codonToken has no '?' test, but a '?' codon is not in the map, so it lands on unknown too. + expect(codonToken('?A?')).toBe(CODON_UNKNOWN); + }); +}); + +describe('tokenizeSequence', () => { + it('tokenises in frame and drops a trailing partial codon', () => { + // 7 nt -> 2 whole codons, matching len // 3. + const { codons, aas } = tokenizeSequence('ATGTTTAA'); + expect(codons).toHaveLength(2); + expect(Array.from(codons)).toEqual([codonToken('ATG'), codonToken('TTT')]); + expect(Array.from(aas)).toEqual([AA_TO_IDX.get('M'), AA_TO_IDX.get('F')]); + }); + + it('handles an empty and a sub-codon sequence', () => { + expect(tokenizeSequence('').codons).toHaveLength(0); + expect(tokenizeSequence('AT').codons).toHaveLength(0); + }); + + it('carries gaps through as gap tokens', () => { + const { codons, aas } = tokenizeSequence('ATG---'); + expect(Array.from(codons)).toEqual([codonToken('ATG'), CODON_GAP]); + expect(Array.from(aas)).toEqual([AA_TO_IDX.get('M'), AA_GAP]); + }); +}); + +describe('WE USE THE TRAINING TOKENIZER, NOT THE INFERENCE DRIVER"S', () => { + /** + * Measured over all 64 codons: predict_regression_nexus.py builds a 60-codon ALPHABETICAL + * vocabulary (lines 49-55), then redefines get_codon_token (line 74) WITHOUT redefining + * CODON_TO_IDX, and imports only three non-tokenizer names from the training module. So the + * driver disagrees with training on 63 of 64 codons, and drops TTA plus all three stops entirely. + * + * A model trained on TCAG-64 must be served TCAG-64. These are the exact values that distinguish + * the two, so this block fails whichever way someone drifts. + */ + const TRAINING = { TTT: 0, TTA: 2, TAA: 10, ATG: 35, AAA: 42, GGG: 63 }; + const DRIVER = { TTT: 59, TTA: 65, TAA: 65, ATG: 14, AAA: 0, GGG: 42 }; + + it('matches the training vocabulary exactly', () => { + for (const [codon, token] of Object.entries(TRAINING)) { + expect(codonToken(codon), codon).toBe(token); + } + }); + + it('does NOT match the driver vocabulary', () => { + for (const [codon, driverToken] of Object.entries(DRIVER)) { + expect(codonToken(codon), `${codon} matched the driver's token`).not.toBe(driverToken); + } + }); + + it('keeps TTA and the stops as real codons, which the driver discards', () => { + // The driver's CODON_LIST omits TTA (Leucine — almost certainly a slip when the stops were + // removed) along with TAA/TAG/TGA, so all four collapse to "unknown" there and real leucine + // data is thrown away. + for (const c of ['TTA', 'TAA', 'TAG', 'TGA']) { + expect(codonToken(c), c).toBeLessThan(64); + } + }); +}); diff --git a/static/models/axomeme/axomeme_2.0_viral_finetuned.onnx b/static/models/axomeme/axomeme_2.0_viral_finetuned.onnx new file mode 100644 index 0000000000000000000000000000000000000000..c3f554ab2f11f77d06ee310050149aeec71b703d GIT binary patch literal 3782234 zcmb?^d4L^7wRa|aZdk%VLKyZTYymQJ_j^O8I}rAru*e>U%p@7IOvp?i0hF*W0o2I$ z*kzGT1X)Fx+X0j(B0fcNMG<5XL>{QX6F%gtzTH*7bLv)A_hj@B%=A4~b?WS=`qb&M zW1Gh;UD@BeZ0>@V(Ya~a{IvGzwfEj?%2YBN^e$PlV*2zwy1M3MEA7>0`ggUIYgupq zuI?rMJ^d@^6mr?tvGT9Pk?m|`F8#=Cg^kP)Vq}SpETkS;DHqtt;vhzH3YF52%x2k| zWioQtOlEOk=bX8{UA;^Cnm24aVnOH9?m4~7`Em>VPlWQ!tEE(M8-Mm&YeroeMAA=EL4uslT+lQ*y5wpY*TSZieX zXX^;@pG-N!e{0^LZB}O=(Y;aizgtI!Pp6K{4DauqJSNnJ85s>y=u>?p5tlLXlg81r zsS0EoMuM_Lh!kb>4U`=oVsvO`OlWC3JNsM{f_-3B!8{?;x)R7o6kga!3#(=lY2FvC z!hR^LFu9=rLEU|QSZq}N@79swQ*MIF03sL(+iSsSBEjfDU<86tj4-+2YaJuT*XT)% zRVWG!X+eK&h>*ZNmUIE(k>Q`MBgB9Bo+n=lgDwP?CdH<2G9cKLwboKj12#59U=ZBWS?(G{7FHDmkT#b2sS!;VAycg 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    Jg%ICmWIjjE!~1VUt)r_7Rx}r|1G7{ z1iSL>u2M%DjAL)*L#nY5((Wb*85f6HTT)QZyN;Mjq;Ta0YhmsAqg2h>f^+EkP1kJR z#J!XG+ZovTn4fqDXq@dv>?!F)pX({$;BpEdK4_&u0Vil^|PT`OT z8+EFGQfsGJ{)Fs zmr^pxs)N?{mO!Sv2Tltv!smBH;I$1?_`cUdFS~!V_I)^PVk0h_O*(Y(PD3>J&4$<0 zd&tkrBeYV~1!smd!55|ZoZjT~BvhsgtUX(}&_83zudooNkDE&?6W-BzY|UTyH-dx>Ho{L_1E9P3CNy+xT6RT+!rCKRxpx z9m=lm!}iWS^c^Yb^;W$4DDPp=t9(`nHT%zL
    -

    - Introducing PRIME -

    -

    - Property-Informed Models of Evolution -

    +

    Introducing PRIME

    +

    Property-Informed Models of Evolution

    - Characterize physicochemical selection in protein evolution. PRIME incorporates amino acid properties like molecular volume, hydropathy, and structural propensities to resolve the biophysical basis of selective constraint. + Characterize physicochemical selection in protein evolution. PRIME incorporates + amino acid properties like molecular volume, hydropathy, and structural propensities + to resolve the biophysical basis of selective constraint.

    Read the preprint -
    +
    {/if} - + {#if activeTab === 'data'} From 65f6d870f1ecdfbfcaf784aecd11f6e2bc936649 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 10:37:45 -0700 Subject: [PATCH 06/15] Add the AxoMEME results view, and drop controls the model cannot honour MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two UI affordances implied capabilities AxoMEME does not have, and both are now driven off a `browserOnly` flag on the method rather than a name check: - EXECUTION MODE offered "Backend Server". There is no server-side AxoMEME; the dispatch ignores the toggle and always runs in-browser, so choosing the server silently did the local thing. Replaced with a sentence saying it runs in your browser and there is nothing to choose. - GENETIC CODE was selectable but unreachable. The model's tokenizer bakes in the universal table, so a user's choice could not affect the result. It defaulted to Universal, which is right, which is what made it dangerous -- changing it would have done nothing and said nothing. The results view leads with the framing rather than burying it. AxoMEME estimates what MEME would report; a researcher reading "Tier 1 (High)" next to a site is one step from writing it up, so the heading says "predictions" and the first sentence says MEME was not run. Invariant sites render "not scored — no amino-acid variation" instead of 0.000. The reference zeroes those before consulting the model, so printing zeros beside real zeros would conflate "the model said nothing" with "we never asked". A warnings block reports what the model was actually given -- sequences dropped for not being in the tree, taxa cut by the 512 cap, repeated taxon slots, and negative branch lengths. None of that is visible in the numbers themselves and all of it changes them. Rows are capped at 500 with an explicit control to show more, because a 12,000-codon alignment is ~100k DOM nodes. e2e covers the render and the suppressed controls, and checks that the controls come BACK for a method that has them, so the flag cannot silently disable them everywhere. --- e2e/19-axomeme.spec.js | 49 +++++++ src/lib/AnalysisResultViewer.svelte | 23 +++- src/lib/AxomemeResults.svelte | 191 ++++++++++++++++++++++++++++ src/lib/MethodSelector.svelte | 59 +++++++-- src/routes/+page.svelte | 4 + 5 files changed, 311 insertions(+), 15 deletions(-) create mode 100644 src/lib/AxomemeResults.svelte diff --git a/e2e/19-axomeme.spec.js b/e2e/19-axomeme.spec.js index abff306..f0b0194 100644 --- a/e2e/19-axomeme.spec.js +++ b/e2e/19-axomeme.spec.js @@ -114,6 +114,55 @@ test.describe('AxoMEME', () => { // The analysis must reach a terminal state, and it must be success. An error toast here is the // signal that the runtime or the model failed to load. await expect(page.getByText(/AxoMEME prediction complete/i)).toBeVisible({ timeout: 120000 }); + + // And the results must actually render. A completed analysis with nothing to show is the + // failure mode this test exists to catch. + await page + .getByRole('button', { name: /results/i }) + .first() + .click(); + // The Results tab lists every analysis and defaults the detail pane to the FIRST one, which is + // the datareader job every upload creates. Selecting the AxoMEME run explicitly is required; + // without it this test passes or fails on whatever happens to be first. + await page + .locator('.analysis-card') + .filter({ hasText: /AXOMEME Analysis/i }) + .getByRole('button', { name: 'View' }) + .first() + .click(); + await expect(page.getByText(/AxoMEME predictions/i)).toBeVisible({ timeout: 30000 }); + + // The framing is not decoration. A researcher reading a per-site table is one step from + // writing it up, so the page must say MEME was not run. + const body = await page.locator('body').innerText(); + expect(body).toMatch(/MEME was not run/i); + + // The per-site table, with the columns that carry the actual claim. + await expect(page.getByRole('columnheader', { name: 'LRT' })).toBeVisible(); + await expect(page.getByRole('columnheader', { name: /dN/ })).toBeVisible(); + }); + + test('suppresses controls that cannot reach the model', async ({ page }) => { + // Both were live at first and both imply a capability AxoMEME does not have: there is no + // server-side AxoMEME, and the model's tokenizer bakes in the universal genetic code. + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + + await selectMethod(page, 'AxoMEME'); + const body = await page.locator('body').innerText(); + expect(body).not.toMatch(/Backend Server/i); + expect(body).toMatch(/Runs in your browser/i); + expect(body).toMatch(/cannot be changed for this method/i); + + // And the controls must come back for a method that does have them. + await selectMethod(page, 'FEL'); + const felBody = await page.locator('body').innerText(); + expect(felBody).toMatch(/Backend Server/i); + expect(felBody).toMatch(/Genetic Code/i); }); }); diff --git a/src/lib/AnalysisResultViewer.svelte b/src/lib/AnalysisResultViewer.svelte index f7ed60c..56bd17b 100644 --- a/src/lib/AnalysisResultViewer.svelte +++ b/src/lib/AnalysisResultViewer.svelte @@ -4,6 +4,7 @@ import { persistentFileStore } from '../stores/fileInfo'; import ExportPanel from './ExportPanel.svelte'; import FelVisualization from './FelVisualization.svelte'; + import AxomemeResults from './AxomemeResults.svelte'; import AnalysisProgress from './AnalysisProgress.svelte'; import { FINAL_HYPHY_EYE_URL } from './config/env'; import { @@ -119,6 +120,11 @@ case 'fel': case 'contrast-fel': return HyphyScopeFel; + case 'axomeme': + // Not a hyphy-scope visualiser — AxoMEME emits per-site predictions rather than a HyPhy + // results document — but it renders through the same slot, which takes any component + // accepting `data`. + return AxomemeResults; case 'meme': return MemeVisualization; case 'absrel': @@ -184,7 +190,9 @@

    Loading analysis results...

    {:else if error} -
    +
    Debug: aBSREL Data Structure - 
    {JSON.stringify(
+											
    {JSON.stringify(
 													resultData,
 													null,
 													2
@@ -377,13 +386,19 @@
 							{/if}
 
 							{#if !resultData.tested && !resultData.fits}
-								
    {JSON.stringify(resultData, null, 2)}
    
+								
    {JSON.stringify(
+										resultData,
+										null,
+										2
+									)}
    
 							{/if}
 
 							
 							{#if isMethodSupported(analysis.method)}
 								
    
-									
    View results with automatic data sharing:
    
+									
    
+										View results with automatic data sharing:
+									
    
 									
+		{/if}
+
+		
    
+			AxoMEME {data.modelVersion}
+			{#if summary.referenceSequence}
+				· reference sequence {summary.referenceSequence}
+			{/if}
+			{#if data.modelSha256}
+				· model {data.modelSha256.slice(0, 12)}
+			{/if}
+		
    
+	
    
+{/if}
diff --git a/src/lib/MethodSelector.svelte b/src/lib/MethodSelector.svelte
index eff4f0d..6f23a8e 100644
--- a/src/lib/MethodSelector.svelte
+++ b/src/lib/MethodSelector.svelte
@@ -984,6 +984,22 @@
 	// Get current method details
 	$: currentMethod = selectedMethod ? availableMethods.find((m) => m.id === selectedMethod) : null;
 
+	/**
+	 * A method that runs only in this tab, with no HyPhy binary and no server job behind it.
+	 *
+	 * Two controls are suppressed for these, and in both cases the reason is the same: the control
+	 * would imply a capability the method does not have.
+	 *   - EXECUTION MODE. There is no server-side AxoMEME. Offering "Backend Server" would either be
+	 *     silently ignored or would route to a socket with no handler.
+	 *   - GENETIC CODE. The model's tokenizer bakes in the universal table (its GENETIC_CODE is a
+	 *     hard-coded literal), so a user's choice cannot reach it. It currently defaults to Universal,
+	 *     which is right, so the control looks harmless -- but changing it would do nothing and say
+	 *     nothing, which is the worst of the three options.
+	 */
+	$: browserOnly = Boolean(currentMethod?.config?.browserOnly);
+	// Keep the reported mode honest for analytics and for the run-started toast.
+	$: if (browserOnly && executionMode !== 'local') executionMode = 'local';
+
 	// Get current method's advanced options
 	$: currentMethodOptions = selectedMethod
 		? METHOD_ADVANCED_OPTIONS[selectedMethod.toLowerCase()] || {}
@@ -1291,7 +1307,15 @@
 		{/if}
 
 		
-		{#if selectedMethod && currentMethod?.info.supported}
+		{#if selectedMethod && currentMethod?.info.supported && browserOnly}
+			
    
+				
    Execution Mode
    
+				
    
+					Runs in your browser. There is no server-side version of this method, so there is nothing
+					to choose.
+				
    
+			
    
+		{:else if selectedMethod && currentMethod?.info.supported}
 			
    
 				
    Execution Mode
    
 				
    
@@ -1359,16 +1383,22 @@
 						Essential
 					
    
 
-					
    
-						
-					
    
+					{#if browserOnly}
+						
    
+							This model reads the standard genetic code. It cannot be changed for this method.
+						
    
+					{:else}
+						
    
+							
+						
    
+					{/if}
 				
    
 
 				
@@ -1632,6 +1662,13 @@
 		margin-bottom: 12px;
 	}
 
+	.browser-only-note {
+		margin: 0;
+		font-size: 0.8125rem;
+		line-height: 1.4;
+		color: #475569;
+	}
+
 	.method-dropdown {
 		flex: 1;
 		padding: 8px 12px;
diff --git a/src/routes/+page.svelte b/src/routes/+page.svelte
index 3bb6f25..483eca3 100644
--- a/src/routes/+page.svelte
+++ b/src/routes/+page.svelte
@@ -80,6 +80,10 @@
 			url: 'axomeme',
 			args: [],
 			runner: 'axomeme',
+			// Drives the UI: no execution-mode toggle (there is no server-side AxoMEME) and no genetic
+			// code selector (the model's tokenizer bakes in the universal table). A control that cannot
+			// reach the model is worse than no control, because it implies a capability.
+			browserOnly: true,
 			description:
 				'Predicts what MEME would report for each site, in seconds rather than hours. A neural surrogate, not a substitute for the full analysis.'
 		},

From ebb3774814dbb444ce4db1645eb08fa4689acf54 Mon Sep 17 00:00:00 2001
From: Steven Weaver 
Date: Sat, 8 Aug 2026 11:32:05 -0700
Subject: [PATCH 07/15] Clamp negative patristic distances, matching the
 model's training pipeline

AxoMEME failed on the bundled large.nex demo. Its NJ-inferred tree produces a
patristic sum of -1.04e-5 -- zero with rounding error on it -- and the input
check rejected the entire run for it.

Neither obvious response is right. Refusing the tree rejects an ordinary
alignment for a rounding artifact, and DM3's own NJ emits these routinely:
NJ.bf:214-220 computes (d01 + d02 - d12)/2 with no Max(0, ...), so any
triangle-inequality violation yields one. Passing it through unclamped feeds the
model something training never showed it.

The handoff README settles it: "This build's training pipeline clamps distances
>= 0." So clamping at inference matches what the weights were fitted against. It
is the reference's INFERENCE path that omits the clamp, and that omission is why
it throws on 13 of 283 real DM3 trees.

This is the one place in the port that deliberately alters a value rather than
passing it through, so it is not done silently: the count and the most negative
magnitude are recorded, and the results page warns when the worst distance is
below -0.001, which separates a genuinely broken tree from NJ float noise.

e2e now runs AxoMEME on every bundled demo, so a tightened validator cannot
quietly break the datasets a user is most likely to click.
---
 e2e/19-axomeme.spec.js                    | 24 ++++++++++++++
 src/lib/AxomemeResults.svelte             |  7 ++++
 src/lib/services/AxomemeAnalysisRunner.js |  5 +++
 src/lib/services/axomeme/assemble.js      | 32 ++++++++++++++++++-
 src/test/axomeme-assemble.test.js         | 39 +++++++++++++++++++++++
 5 files changed, 106 insertions(+), 1 deletion(-)

diff --git a/e2e/19-axomeme.spec.js b/e2e/19-axomeme.spec.js
index f0b0194..509286d 100644
--- a/e2e/19-axomeme.spec.js
+++ b/e2e/19-axomeme.spec.js
@@ -166,6 +166,30 @@ test.describe('AxoMEME', () => {
 	});
 });
 
+test.describe('AxoMEME on the bundled demos', () => {
+	test.setTimeout(300000);
+
+	// large.nex is the regression that prompted this block. Its tree carries negative branch lengths
+	// from DM3's own NJ inference — a patristic sum of -1.04e-5, which is zero with rounding error on
+	// it — and the input check rejected the whole run for it. Every bundled demo now runs, so a
+	// tightened validator cannot quietly break the ones a user is most likely to click.
+	for (const demo of ['small.nex', 'medium.nex', 'large.nex']) {
+		test(`runs on ${demo}`, async ({ page }) => {
+			await freshStart(page);
+			await loadDemoFile(page, demo);
+			await expect(async () => {
+				await goToAnalyzeTab(page);
+				await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({
+					timeout: 5000
+				});
+			}).toPass({ timeout: 60000 });
+			await selectMethod(page, 'AxoMEME');
+			expect(await clickRunAnalysis(page), 'run button was not clickable').toBe(true);
+			await expect(page.getByText(/AxoMEME prediction complete/i)).toBeVisible({ timeout: 180000 });
+		});
+	}
+});
+
 test.describe('AxoMEME cost', () => {
 	test.setTimeout(180000);
 
diff --git a/src/lib/AxomemeResults.svelte b/src/lib/AxomemeResults.svelte
index b659901..b79a437 100644
--- a/src/lib/AxomemeResults.svelte
+++ b/src/lib/AxomemeResults.svelte
@@ -74,6 +74,13 @@
 								: ''}.

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z23LHe38Q87@xpFO<9f(ACFW2Zx#?BmDHu4o(t^}NGGgx_~>n7*6r+2Zt~I zWb(U}Fu|J5v<;5JB}>K-SGz=RytSoo?|Fes@mKP=CJv%AI_T2(EfDRP0b-1kvo~To zUesb;jt~3b!B`7=^wyKX&4x6mq6=rneB~4zovHQh)2Pwsf(vp4g@gEn`Na7QB=&oF z`0MCwaP-;WyU%{9w)Rq!Ai<5RqFApq0iWw;X(cnV49f`3Lm_SKTa`s-j>TR96o_yNWP-c^{~rS)ofBV?nn~ zM3-Y<&9|f!nu_2bxc^RrBfP~;uNs#&?NplF^sX=)`hI^$+pJ-HxNv%teWiF)(-EHB4LCBs$#*DMX{PN{e@`7QoDGO1~O?<2I8+J*1#dcdLckMYA-sixPzlA5TsN>kp` zwM~j~3c7u!A?QA5x^Dkx2M86K(-gOJt!~F_3Eg{Y({=Ajj6j>Y397|RY|7#n(H*a} zqN!4JHspu0m4(SutQFB}YPD0x1+)Lp_h$8ekDZP_|8eOoHxbPBU8iTGE+XRY?P$Mq zzw>?%Z^^kL^1?!rvqXd>$2&Rt?A#?HDmlr+*WKRR(`Dyw7jF@MVLqG1B0~T17mkkq ze@`bOlbm;Z``EiX`gra3ms~8eP?%3r<-dJ}|N901=ixbfv%}l(<@~>1kMI9^c!D-+ zB9lEGot<}kxUrY?-YY35GD~=(hRArweV)4< n|MMfVBEn8CK92StE^hz*gP!}n|MP?Fe|r9xe`dfhF7ZDAl@+qq literal 0 HcmV?d00001 From 14087418581734d447ce96ac2a7dcfc5af7d773b Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 08:17:16 -0700 Subject: [PATCH 04/15] Assemble alignment and tree into AxoMEME's five input tensors This joins the separately-verified stages -- newick parse, patristic distances, Max-PD selection, tokenisation and MDS -- into the bundle the ONNX graph accepts, which makes the whole path runnable end to end. Two decisions in here are not obvious. MDS runs on the PADDED matrix and is then sliced, while the GRAPH is fed only the real species. Those are different questions with different answers. The padded zeros take part in MDS's double-centring, so coordinates genuinely depend on max_species and computing them on the real N gives different numbers -- that has to be reproduced. But feeding 512 slots to the graph is measurably equivalent to feeding N real species with the remainder masked: worst delta 1.192e-07, one float32 ulp, which is what masking is for. The difference is 462 MB versus 2.3 MB for dist_matrix on a 441-site alignment, because ONNX needs materialised data where torch used an expand view, and attention is quadratic in N on top of that. Species order comes from the TREE, not the alignment, with the reference sequence moved to index 0. Order matters twice: it fixes the distance matrix rows and therefore MDS, and index 0 seeds the Max-PD traversal, so it decides which taxa survive the cap. The reference sequence is chosen by a heuristic that looks for 'hg' / 'hg38' / 'human' before falling back to the first sequence -- a TOGA-mammal artifact, so on viral traffic the fallback is the real behaviour. Sites are batched rather than assembled whole, sized against the dist_matrix budget, because DataMonkey accepts uploads up to 12,000 codons. Verified end to end on the bundled CD2 demo (10 taxa, 17 codons): 726 ms to prepare, 88 ms of inference, 13 of 17 sites variable, and the top site reports dN+ 4.47 against dS 0.025 with p_pos 0.9999 while calling Neutral -- the LRT of 1.86 does not clear the 3.12 gate, which is the right answer at that alignment size. --- src/lib/services/axomeme/assemble.js | 287 +++++++++++++++++++++++++++ src/test/axomeme-assemble.test.js | 230 +++++++++++++++++++++ 2 files changed, 517 insertions(+) create mode 100644 src/lib/services/axomeme/assemble.js create mode 100644 src/test/axomeme-assemble.test.js diff --git a/src/lib/services/axomeme/assemble.js b/src/lib/services/axomeme/assemble.js new file mode 100644 index 0000000..4699593 --- /dev/null +++ b/src/lib/services/axomeme/assemble.js @@ -0,0 +1,287 @@ +/** + * assemble.js — turn an alignment and a tree into the five tensors the AxoMEME graph accepts. + * + * This is the layer that joins everything else: newick parse, patristic distances, Max-PD selection, + * tokenisation and MDS. Two decisions in here are not obvious and both are load-bearing. + * + * --------------------------------------------------------------------------------------------- + * 1. MDS IS COMPUTED ON THE PADDED MATRIX, THEN SLICED. THE MODEL IS FED ONLY THE REAL SPECIES. + * --------------------------------------------------------------------------------------------- + * The reference builds a [max_species, max_species] tensor — 512 x 512 by default — regardless of how + * many taxa the alignment actually has, and runs MDS on that. The padded zeros take part in the + * double-centring, so the coordinates genuinely depend on max_species and computing MDS on the real N + * gives different numbers. That part must be reproduced exactly. + * + * Feeding those 512 slots to the GRAPH, however, is a different question, and the answer is measured + * rather than assumed: feeding N = real species produces the same outputs as feeding N = 512 with the + * remainder masked, to 1.192e-07 — exactly one float32 ulp. That is what masking is for; `forward()` + * excludes padded slots from the validity mask, the distance normalisation, the attention and every + * pooling stream, and softmax's -1e4 fill underflows to zero. + * + * The difference is not academic. A 441-site alignment at N=512 needs + * 441 x 512 x 512 x 4 = 462 MB for dist_matrix alone, because ONNX needs materialised data where + * torch used an `expand` view. At N=36 it is 2.3 MB, and attention is quadratic in N on top of that. + * So: MDS at 512 for fidelity, graph at real N for tractability. + * + * --------------------------------------------------------------------------------------------- + * 2. SPECIES ORDER COMES FROM THE TREE, NOT THE ALIGNMENT, AND THE REFERENCE SEQUENCE LEADS. + * --------------------------------------------------------------------------------------------- + * predict_regression_nexus.py:1069-1082 walks the TREE's leaves in order and keeps the ones present + * in the alignment, then moves the reference sequence to index 0. Order matters twice over: it fixes + * the row order of the distance matrix (and therefore MDS), and index 0 is the seed of the Max-PD + * traversal, so it decides which taxa survive the cap. + * + * The reference sequence itself is picked by a heuristic that is a TOGA-mammal artifact — it looks + * for sequences literally named 'hg', 'hg38' or 'human' before falling back to the first sequence in + * the file. On DataMonkey's viral traffic that fallback is what fires essentially always. + * + * Sources: predict_regression_nexus.py:1049-1082 (reference + matching), 1165-1188 (Max-PD), + * 1213-1275 (tensor allocation, pad values, MDS, the site loop). + */ + +import { parseNewick, leafIndex, normalizeTaxonName } from './newick.js'; +import { patristicMatrix, maxPdSelect } from './patristic.js'; +import { computeMdsCoordinates } from './mds.js'; +import { codonToken, aaToken } from './tokenizer.js'; +import { + MAX_SPECIES_DEFAULT, + WINDOW_SIZE_DEFAULT, + MDS_COMPONENTS, + CODON_UNKNOWN, + AA_UNKNOWN +} from './modelContract.js'; + +/** The reference-sequence heuristic, verbatim from the driver. */ +const REFERENCE_HEURISTICS = ['hg', 'hg38', 'human']; + +/** + * Pick the reference sequence, whose length sets the number of sites and which leads the species + * order. Explicit choice wins; otherwise the driver's heuristic, which falls through to the first + * sequence for anything that is not a TOGA mammal alignment. + * + * @param {string[]} names + * @param {string} [explicit] + * @returns {string} + */ +export function chooseReference(names, explicit) { + if (explicit && names.includes(explicit)) return explicit; + for (const h of REFERENCE_HEURISTICS) if (names.includes(h)) return h; + return names[0]; +} + +/** + * Order the taxa the way the reference does: tree leaf order, filtered to those present in the + * alignment, with the reference sequence moved to the front. + * + * @param {string[]} names alignment sequence names + * @param {any} tree parsed newick, or null + * @param {string} referenceName + * @returns {{order: number[], matchedFromTree: boolean}} indices into `names` + */ +export function orderSpecies(names, tree, referenceName) { + const byNormalised = new Map(); + // Later duplicates overwrite, matching the reference's `name_map[norm] = align_name`. + names.forEach((n, i) => byNormalised.set(normalizeTaxonName(n), i)); + + let order = []; + let matchedFromTree = false; + if (tree) { + const seen = new Set(); + for (const leaf of tree.leaves) { + const nm = normalizeTaxonName(tree.name[leaf]); + if (!nm || seen.has(nm)) continue; + seen.add(nm); + const idx = byNormalised.get(nm); + if (idx !== undefined) order.push(idx); + } + matchedFromTree = order.length > 0; + } + // "Zero matching species between tree and alignment. Falling back to alignment order." + if (order.length === 0) order = names.map((_, i) => i); + + const refIdx = names.indexOf(referenceName); + if (refIdx >= 0) { + const at = order.indexOf(refIdx); + if (at > 0) { + order.splice(at, 1); + order.unshift(refIdx); + } else if (at < 0) { + // The reference is not in the tree. The driver still puts it first, because its sequence + // defines the site count and it seeds Max-PD. + order.unshift(refIdx); + } + } + return { order, matchedFromTree }; +} + +/** + * Prepare everything that is per-alignment rather than per-site. + * + * Returns a handle whose `batch()` materialises tensors for a range of sites. Sites are chunked + * rather than assembled in one go because the tensors are O(sites x N^2): a 12,000-codon upload at + * even 36 species is 62 MB for dist_matrix, and DataMonkey accepts uploads that large. + * + * @param {{names: string[], sequences: string[], tree?: any, treeText?: string, + * maxSpecies?: number, windowSize?: number, referenceName?: string}} input + */ +export function prepareAlignment(input) { + const { + names, + sequences, + treeText, + maxSpecies = MAX_SPECIES_DEFAULT, + windowSize = WINDOW_SIZE_DEFAULT, + referenceName + } = input; + + if (!Array.isArray(names) || !Array.isArray(sequences) || names.length !== sequences.length) { + throw new Error('prepareAlignment: names and sequences must be parallel arrays'); + } + if (names.length === 0) throw new Error('prepareAlignment: no sequences'); + + const tree = input.tree ?? (treeText ? parseNewick(treeText) : null); + const refName = chooseReference(names, referenceName); + const { order, matchedFromTree } = orderSpecies(names, tree, refName); + + // --- taxon selection ------------------------------------------------------------------------- + let selected = order; + let duplicates = 0; + if (order.length > maxSpecies) { + if (tree) { + const { index } = leafIndex(tree); + const nodes = order.map((i) => index.get(normalizeTaxonName(names[i]))); + if (nodes.every((n) => n !== undefined)) { + const pd = maxPdSelect(tree, nodes, maxSpecies); + duplicates = pd.duplicates; + selected = pd.selected.map((k) => order[k]); + } else { + selected = order.slice(0, maxSpecies); + } + } else { + // `selected_species = matching_species[:args.max_species]` + selected = order.slice(0, maxSpecies); + } + } + const n = selected.length; + + // --- distances, at the REAL species count ---------------------------------------------------- + const dist = new Float32Array(n * n); + if (tree) { + const { index } = leafIndex(tree); + const nodes = selected.map((i) => index.get(normalizeTaxonName(names[i]))); + if (nodes.every((v) => v !== undefined)) { + const full = patristicMatrix(tree, nodes); + // float32 because that is what dist_tensor is, and MDS is sensitive to the rounding — see + // mds.js. Doing it here rather than there keeps a single source of the rounded values. + for (let k = 0; k < n * n; k++) dist[k] = full[k]; + } + } + // No tree, or names that do not resolve: the reference falls back to an all-zero matrix rather + // than failing, and MDS on all zeros yields all-zero coordinates. + + // --- MDS on the PADDED matrix, then sliced back ---------------------------------------------- + // This is the part that must not be "simplified" to run on `dist` directly. See the header. + const cap = Math.max(maxSpecies, n); + const padded = new Float64Array(cap * cap); + for (let i = 0; i < n; i++) for (let j = 0; j < n; j++) padded[i * cap + j] = dist[i * n + j]; + const paddedMds = computeMdsCoordinates(padded, cap, MDS_COMPONENTS); + const mds = new Float32Array(n * MDS_COMPONENTS); + for (let i = 0; i < n; i++) { + for (let c = 0; c < MDS_COMPONENTS; c++) + mds[i * MDS_COMPONENTS + c] = paddedMds[i * MDS_COMPONENTS + c]; + } + + // --- tokens ---------------------------------------------------------------------------------- + const refSeq = sequences[names.indexOf(refName)] ?? sequences[0]; + const totalCodons = Math.floor(refSeq.length / 3); + const halfWindow = Math.floor(windowSize / 2); + + // Pre-filled with the pad values, so a species whose sequence is shorter than the reference simply + // keeps them — matching `torch.ones(...) * 65` and `* AA_TO_IDX['?']`. + const codonTokens = new BigInt64Array(totalCodons * n * windowSize).fill(BigInt(CODON_UNKNOWN)); + const aaTokens = new BigInt64Array(totalCodons * n * windowSize).fill(BigInt(AA_UNKNOWN)); + + for (let s = 0; s < n; s++) { + const seq = sequences[selected[s]] ?? ''; + const seqCodons = Math.floor(seq.length / 3); + for (let site = 0; site < totalCodons; site++) { + for (let w = 0; w < windowSize; w++) { + // `codon_pos_1based = site_idx - half_win + w_idx`, guarded to the sequence's own length. + const pos = site + 1 - halfWindow + w; + if (pos < 1 || pos > seqCodons) continue; + const codon = seq.slice((pos - 1) * 3, (pos - 1) * 3 + 3); + const at = (site * n + s) * windowSize + w; + codonTokens[at] = BigInt(codonToken(codon)); + aaTokens[at] = BigInt(aaToken(codon)); + } + } + } + + // Every selected slot is a real species, so nothing is padded. The tensor is still required by the + // graph, and is what would carry padding if a caller ever fed padded slots. + const paddingMask = new Uint8Array(n); + + return { + speciesCount: n, + totalCodons, + windowSize, + referenceName: refName, + selectedNames: selected.map((i) => names[i]), + matchedFromTree, + duplicateSelections: duplicates, + dist, + mds, + paddingMask, + codonTokens, + aaTokens, + + /** + * Materialise the five tensors for sites [start, start + count). + * + * @param {number} start + * @param {number} [count] + * @returns {Record} + */ + batch(start, count = totalCodons - start) { + const b = Math.max(0, Math.min(count, totalCodons - start)); + const perSiteTokens = n * windowSize; + const distData = new Float32Array(b * n * n); + const mdsData = new Float32Array(b * n * MDS_COMPONENTS); + const maskData = new Uint8Array(b * n); + for (let k = 0; k < b; k++) { + distData.set(dist, k * n * n); + mdsData.set(mds, k * n * MDS_COMPONENTS); + maskData.set(paddingMask, k * n); + } + return { + msa_codons: { + data: codonTokens.slice(start * perSiteTokens, (start + b) * perSiteTokens), + dims: [b, n, windowSize] + }, + msa_aas: { + data: aaTokens.slice(start * perSiteTokens, (start + b) * perSiteTokens), + dims: [b, n, windowSize] + }, + dist_matrix: { data: distData, dims: [b, n, n] }, + mds_coords: { data: mdsData, dims: [b, n, MDS_COMPONENTS] }, + padding_mask: { data: maskData, dims: [b, n] } + }; + } + }; +} + +/** + * Site count per batch that keeps the materialised tensors near `budgetBytes`. + * + * dist_matrix dominates at 4 * N^2 bytes per site, so this is a one-term estimate rather than an + * accounting of every tensor. At least one site is always returned — a single site of a 512-taxon + * alignment is 1 MB and has to go through whatever the budget says. + * + * @param {number} speciesCount + * @param {number} [budgetBytes] default 64 MB + * @returns {number} + */ +export function batchSizeFor(speciesCount, budgetBytes = 64 * 1024 * 1024) { + const perSite = 4 * speciesCount * speciesCount; + return Math.max(1, Math.floor(budgetBytes / Math.max(perSite, 1))); +} diff --git a/src/test/axomeme-assemble.test.js b/src/test/axomeme-assemble.test.js new file mode 100644 index 0000000..baaff2a --- /dev/null +++ b/src/test/axomeme-assemble.test.js @@ -0,0 +1,230 @@ +/** + * Tests for AxoMEME tensor assembly. + * + * Assembly is where the separately-verified stages get joined, so the failures available here are + * joining failures: right values in the wrong order, right shapes with the wrong species, MDS + * computed on the wrong matrix. None of them crash. All of them produce a well-formed bundle that + * means something the model was not trained on, which is why every bundle below is also run through + * validateInputBundle. + */ +import { describe, it, expect } from 'vitest'; +import { + prepareAlignment, + chooseReference, + orderSpecies, + batchSizeFor +} from '../lib/services/axomeme/assemble.js'; +import { parseNewick } from '../lib/services/axomeme/newick.js'; +import { computeMdsCoordinates } from '../lib/services/axomeme/mds.js'; +import { codonToken, aaToken } from '../lib/services/axomeme/tokenizer.js'; +import { + validateInputBundle, + CODON_UNKNOWN, + AA_UNKNOWN, + MDS_COMPONENTS +} from '../lib/services/axomeme/modelContract.js'; + +/** Four taxa, distinct codons, 3 sites each. Tree order is deliberately NOT alignment order. */ +const NAMES = ['alpha', 'beta', 'gamma', 'delta']; +const SEQS = ['ATGTTATCA', 'ATGCTATCA', 'ATGTTAAGC', 'ATGGGGTCA']; +const TREE = '((gamma:0.1,delta:0.2):0.05,(beta:0.3,alpha:0.15):0.02);'; + +const prep = (over = {}) => + prepareAlignment({ names: NAMES, sequences: SEQS, treeText: TREE, maxSpecies: 8, ...over }); + +describe('chooseReference', () => { + it('honours an explicit choice', () => { + expect(chooseReference(NAMES, 'gamma')).toBe('gamma'); + }); + + it('falls back to the first sequence, which is what fires on viral data', () => { + // The heuristic looks for 'hg' / 'hg38' / 'human' — a TOGA-mammal artifact. DataMonkey traffic + // is viral, so the fallback is the real behaviour. + expect(chooseReference(NAMES)).toBe('alpha'); + expect(chooseReference(['x', 'human', 'y'])).toBe('human'); + expect(chooseReference(['x', 'hg38'])).toBe('hg38'); + }); + + it('ignores an explicit name that is not in the alignment', () => { + expect(chooseReference(NAMES, 'nope')).toBe('alpha'); + }); +}); + +describe('orderSpecies', () => { + it('takes TREE order, not alignment order', () => { + // Order fixes the distance matrix rows and therefore MDS, and index 0 seeds Max-PD. + const tree = parseNewick(TREE); + const { order, matchedFromTree } = orderSpecies(NAMES, tree, 'gamma'); + expect(matchedFromTree).toBe(true); + expect(order.map((i) => NAMES[i])).toEqual(['gamma', 'delta', 'beta', 'alpha']); + }); + + it('moves the reference sequence to the front', () => { + const tree = parseNewick(TREE); + const { order } = orderSpecies(NAMES, tree, 'alpha'); + expect(order.map((i) => NAMES[i])[0]).toBe('alpha'); + // and everything else keeps tree order behind it + expect(order.map((i) => NAMES[i])).toEqual(['alpha', 'gamma', 'delta', 'beta']); + }); + + it('falls back to alignment order when nothing matches the tree', () => { + const tree = parseNewick('((zzz:0.1,yyy:0.2):0.05);'); + const { order, matchedFromTree } = orderSpecies(NAMES, tree, 'alpha'); + expect(matchedFromTree).toBe(false); + expect(order.map((i) => NAMES[i])).toEqual(NAMES); + }); + + it('drops alignment sequences that are absent from the tree', () => { + const tree = parseNewick('((gamma:0.1,delta:0.2):0.05);'); + const { order } = orderSpecies(NAMES, tree, 'gamma'); + expect(order.map((i) => NAMES[i])).toEqual(['gamma', 'delta']); + }); +}); + +describe('prepareAlignment', () => { + it('produces a bundle that satisfies the contract', () => { + const p = prep(); + const bundle = p.batch(0); + const v = validateInputBundle(bundle, { + batch: p.totalCodons, + numSpecies: p.speciesCount, + windowSize: p.windowSize + }); + expect(v.errors).toEqual([]); + }); + + it('derives the site count from the reference sequence', () => { + expect(prep().totalCodons).toBe(3); // 9 nt / 3 + }); + + it('feeds the graph only the real species, with nothing padded', () => { + // Measured equivalent to feeding max_species with the remainder masked (1 float32 ulp), and + // the difference is 462 MB vs 2.3 MB on a 441-site alignment. + const p = prep(); + expect(p.speciesCount).toBe(4); + expect(Array.from(p.paddingMask)).toEqual([0, 0, 0, 0]); + expect(p.batch(0).dist_matrix.dims).toEqual([3, 4, 4]); + }); + + it('computes MDS on the PADDED matrix and slices, not on the real N', () => { + // The single most silently-wrong thing available in this file. Coordinates depend on + // max_species because the padded zeros take part in the double-centring. + const p = prepareAlignment({ + names: NAMES, + sequences: SEQS, + treeText: TREE, + maxSpecies: 16 + }); + const cap = 16; + const padded = new Float64Array(cap * cap); + const n = p.speciesCount; + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) padded[i * cap + j] = p.dist[i * n + j]; + } + const expected = computeMdsCoordinates(padded, cap, MDS_COMPONENTS); + for (let i = 0; i < n * MDS_COMPONENTS; i++) { + expect(p.mds[i]).toBeCloseTo(expected[i], 6); + } + // And it is genuinely different from the unpadded answer, so the test above has teeth. + const unpadded = computeMdsCoordinates(Float64Array.from(p.dist), n, MDS_COMPONENTS); + expect(Array.from(p.mds)).not.toEqual(Array.from(unpadded)); + }); + + it('orders the distance matrix rows to match the selected species', () => { + const p = prep({ referenceName: 'gamma' }); + expect(p.selectedNames).toEqual(['gamma', 'delta', 'beta', 'alpha']); + const n = p.speciesCount; + // gamma-delta share a parent: 0.1 + 0.2 = 0.3. gamma-beta crosses the root: 0.1+0.05+0.02+0.3. + expect(p.dist[0 * n + 1]).toBeCloseTo(0.3, 5); + expect(p.dist[0 * n + 2]).toBeCloseTo(0.47, 5); + expect(p.dist[0 * n + 0]).toBe(0); + }); + + it('tokenises each species at each site, in selected order', () => { + const p = prep({ referenceName: 'alpha' }); + // selected: alpha, gamma, delta, beta -> site 1 (2nd codon) is TTA, TTA, GGG, CTA + const n = p.speciesCount; + const at = (site, s) => Number(p.codonTokens[(site * n + s) * p.windowSize]); + expect(at(1, 0)).toBe(codonToken('TTA')); + expect(at(1, 1)).toBe(codonToken('TTA')); + expect(at(1, 2)).toBe(codonToken('GGG')); + expect(at(1, 3)).toBe(codonToken('CTA')); + const aaAt = (site, s) => Number(p.aaTokens[(site * n + s) * p.windowSize]); + expect(aaAt(1, 2)).toBe(aaToken('GGG')); + }); + + it('leaves pad values where a sequence is shorter than the reference', () => { + // `torch.ones(...) * 65` is never overwritten past a short sequence's end. + const p = prepareAlignment({ + names: ['a', 'b'], + sequences: ['ATGTTATCA', 'ATG'], + treeText: '(a:0.1,b:0.2);', + maxSpecies: 8 + }); + const n = p.speciesCount; + const idx = (site, s) => (site * n + s) * p.windowSize; + expect(Number(p.codonTokens[idx(0, 1)])).toBe(codonToken('ATG')); + expect(Number(p.codonTokens[idx(1, 1)])).toBe(CODON_UNKNOWN); + expect(Number(p.aaTokens[idx(1, 1)])).toBe(AA_UNKNOWN); + }); + + it('applies Max-PD when over the cap, seeded at the reference', () => { + const names = ['r', 'near', 'far', 'mid']; + const seqs = ['ATG', 'ATG', 'ATG', 'ATG']; + const p = prepareAlignment({ + names, + sequences: seqs, + treeText: '((r:0.01,near:0.01):0.05,(far:2.0,mid:0.5):0.5);', + maxSpecies: 2, + referenceName: 'r' + }); + expect(p.speciesCount).toBe(2); + expect(p.selectedNames[0]).toBe('r'); // the seed + expect(p.selectedNames[1]).toBe('far'); // farthest from it + }); + + it('falls back to an all-zero distance matrix without a tree', () => { + const p = prepareAlignment({ names: NAMES, sequences: SEQS, maxSpecies: 8 }); + expect(Array.from(p.dist).every((v) => v === 0)).toBe(true); + expect(p.matchedFromTree).toBe(false); + // MDS of an all-zero matrix is all zeros, not NaN. + expect(Array.from(p.mds).every((v) => v === 0)).toBe(true); + }); + + it('rejects mismatched or empty input rather than producing a bundle', () => { + expect(() => prepareAlignment({ names: ['a'], sequences: [] })).toThrow(/parallel/); + expect(() => prepareAlignment({ names: [], sequences: [] })).toThrow(/no sequences/); + }); +}); + +describe('batching', () => { + it('slices sites without disturbing the per-alignment tensors', () => { + const p = prep(); + const all = p.batch(0); + const tail = p.batch(1, 2); + expect(tail.msa_codons.dims).toEqual([2, 4, 1]); + // site 1 of the full batch is site 0 of this one + const n = p.speciesCount; + for (let s = 0; s < n; s++) { + expect(tail.msa_codons.data[s]).toBe(all.msa_codons.data[n + s]); + } + // and the invariant tensors are repeated per site, identically + for (let k = 0; k < n * n; k++) { + expect(tail.dist_matrix.data[n * n + k]).toBe(tail.dist_matrix.data[k]); + } + }); + + it('clamps a range that runs past the end', () => { + const p = prep(); + expect(p.batch(2, 99).msa_codons.dims[0]).toBe(1); + expect(p.batch(3, 5).msa_codons.dims[0]).toBe(0); + }); + + it('sizes batches against the dist_matrix budget', () => { + // 4 * N^2 bytes per site is the dominant term. + expect(batchSizeFor(512, 64 * 1024 * 1024)).toBe(64); + expect(batchSizeFor(36, 64 * 1024 * 1024)).toBeGreaterThan(1000); + // Never zero: one site of a huge alignment still has to go through. + expect(batchSizeFor(4096, 1024)).toBe(1); + }); +}); From 885e6e52239d97de664628c189d4d9be42c6931f Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 09:58:00 -0700 Subject: [PATCH 05/15] Wire AxoMEME into the analysis UI as a browser-only method Registers AxoMEME in the method registry and dispatches it to a third runner. The project's checklist for adding an analysis assumes a HyPhy method at every step -- a hyphy command, an outputSuffix naming the HyPhy JSON, CLI argument mapping, a backend socket event, a hyphy-eye visualiser keyed to that JSON -- and AxoMEME has none of them. So methodConfig carries `command: null` and `outputSuffix: null` rather than invented values, and anything dispatching on them fails loudly instead of running the wrong binary. AxomemeAnalysisRunner implements the BaseAnalysisRunner lifecycle and nothing else, and is dispatched in AnalyzeTab BEFORE the backend/WASM split because it belongs to neither. The executionMode toggle is ignored rather than routing a browser-only method through a socket with no handler for it. Two runtime-loading bugs, both found by running it in a browser and neither visible to a unit test: onnxruntime-web does not bundle its WASM binary. With no wasmPaths it resolves to a jsDelivr CDN, which breaks the project's core constraint that the site be servable with no other domains involved, and fails outright offline. The runtime reports "no available backend found", which reads like a broken model rather than a missing asset. scripts/copy-ort-wasm.mjs now vendors the binary into static/ort/ at build time -- copied rather than committed, because it is 12.9 MB of third-party output already pinned by package.json, and a committed copy would go stale on upgrade with nothing noticing. The DEFAULT package entry then asks for the JSEP (WebGPU) binary, a different 26.8 MB file, and fails identically even with the CPU binary present and served correctly. Importing onnxruntime-web/wasm fixes it and drops WebGPU/WebNN backends this feature never uses. e2e/19-axomeme.spec.js asserts WHICH URLS THE PAGE REQUESTS, not just that a number came out, since both bugs were in the fetch rather than in any computation. It also re-asserts the cost invariant from the other direction: a non-AxoMEME method must download none of the 17 MB. Advanced options are deliberately near-empty. There are no branch sets to select (the model consumes the whole tree as a distance matrix), no rate variation switch, and no genetic code choice -- the code table is baked into the model's tokenizer. Calling mode is the one real control, because it is a threshold applied after inference. Verified end to end in Chrome on the CD2 demo: method appears, runs, and reports completion. --- .gitignore | 4 + e2e/19-axomeme.spec.js | 141 +++++++++++++++++++ package.json | 8 +- scripts/copy-ort-wasm.mjs | 58 ++++++++ src/lib/AnalyzeTab.svelte | 55 +++++++- src/lib/MethodSelector.svelte | 120 ++++++++++------ src/lib/config/methodOptions.toml | 35 +++++ src/lib/services/AxomemeAnalysisRunner.js | 162 ++++++++++++++++++++++ src/lib/services/axomeme/session.js | 24 +++- src/routes/+page.svelte | 115 ++++++++++----- 10 files changed, 636 insertions(+), 86 deletions(-) create mode 100644 e2e/19-axomeme.spec.js create mode 100644 scripts/copy-ort-wasm.mjs create mode 100644 src/lib/services/AxomemeAnalysisRunner.js diff --git a/.gitignore b/.gitignore index 3d702c1..a7ccfab 100644 --- a/.gitignore +++ b/.gitignore @@ -74,3 +74,7 @@ coverage-e2e /benchmark-results/ /hyphy-wasm-artifact/ /src/benchmark/test-alignments/ + +# onnxruntime-web WASM runtime, copied from node_modules at build time by +# scripts/copy-ort-wasm.mjs. Pinned by package.json, not by git. +static/ort/ diff --git a/e2e/19-axomeme.spec.js b/e2e/19-axomeme.spec.js new file mode 100644 index 0000000..abff306 --- /dev/null +++ b/e2e/19-axomeme.spec.js @@ -0,0 +1,141 @@ +/** + * E2E tests for AxoMEME, the in-browser neural surrogate for MEME. + * + * These exist because AxoMEME's failure modes are ones no unit test can see. The model and the + * runtime are fetched at run time from static assets, and every bug found while wiring this up was + * in that fetch rather than in any computation: + * + * 1. onnxruntime-web does not bundle its WASM binary. With no `wasmPaths` it resolves to a jsDelivr + * CDN — which violates this project's core constraint that the site be servable with no other + * domains involved, and which simply fails offline. The runtime reports "no available backend + * found", which reads like a broken model rather than a missing asset. + * 2. The DEFAULT package entry wants the JSEP (WebGPU) binary, a different 26.8 MB file from the + * CPU one. Importing `onnxruntime-web` instead of `onnxruntime-web/wasm` fails with the same + * opaque message even when the CPU binary is present and served correctly. + * + * Both were found by hand and neither would have failed a unit test. So what these tests assert is + * WHICH URLS THE PAGE ACTUALLY REQUESTS, not just that a number came out. + */ + +import { test, expect } from './fixtures/coverage.js'; +import { + freshStart, + loadDemoFile, + goToAnalyzeTab, + selectMethod, + clickRunAnalysis +} from './fixtures/helpers.js'; + +/** Anything served from another origin. The project must never need one. */ +const isThirdParty = (url) => !/^https?:\/\/(localhost|127\.0\.0\.1)/.test(url); + +/** The CPU runtime this feature is built against. */ +const CPU_WASM = /ort-wasm-simd-threaded\.(wasm|mjs)$/; +/** The WebGPU / asyncify / JSPI variants, none of which should ever be requested. */ +const OTHER_VARIANTS = /ort-wasm-simd-threaded\.(jsep|asyncify|jspi)\./; + +function trackRequests(page) { + const urls = []; + page.on('request', (r) => urls.push(r.url())); + return () => urls; +} + +test.describe('AxoMEME', () => { + test.setTimeout(180000); + + test('appears in the method list and describes itself as a prediction', async ({ page }) => { + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + + await selectMethod(page, 'AxoMEME'); + + // The copy has to say PREDICT. AxoMEME estimates what MEME would report; presenting it as a + // completed selection analysis is the one framing error that matters here, and the method + // description is where a user forms that expectation. + const body = await page.locator('body').innerText(); + expect(body).toMatch(/predict/i); + }); + + test('fetches its runtime and model from THIS origin, never a CDN', async ({ page }) => { + // The project constraint, asserted rather than trusted: "The website needs to be entirely + // self-contained so that it can be served locally. No pulling down from other domains." + const requests = trackRequests(page); + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + await selectMethod(page, 'AxoMEME'); + + expect(await clickRunAnalysis(page), 'run button was not clickable').toBe(true); + + // Give the runtime time to fetch its binary and score 17 sites. + await page.waitForTimeout(45000); + + const all = requests(); + // SCOPED TO AXOMEME'S OWN ASSETS, deliberately. A broader filter here fails, and not because of + // anything in this feature: the app already fetches katex from cdn.jsdelivr.net and aioli from + // biowasm.com, which are real violations of the same project constraint and predate this work. + // Widening this assertion would make an AxoMEME test fail for someone else's reason, which is + // how a guard gets deleted. They are logged separately instead. + const offOrigin = all.filter(isThirdParty).filter((u) => /onnx|ort-wasm/i.test(u)); + expect(offOrigin, `AxoMEME assets fetched off-origin: ${offOrigin.join(', ')}`).toEqual([]); + + // And it must have asked for the CPU binary specifically. + const wasmRequests = all.filter((u) => CPU_WASM.test(u)); + expect(wasmRequests.length, 'the CPU WASM runtime was never requested').toBeGreaterThan(0); + for (const u of wasmRequests) expect(u).toMatch(/\/ort\//); + + // The JSEP / asyncify / JSPI builds are 15-27 MB each and this feature uses none of them. + // Requesting one means the default package entry crept back in. + const wrongVariant = all.filter((u) => OTHER_VARIANTS.test(u)); + expect( + wrongVariant, + `requested a runtime variant we do not ship: ${wrongVariant.join(', ')}` + ).toEqual([]); + }); + + test('produces a per-site result table', async ({ page }) => { + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + await selectMethod(page, 'AxoMEME'); + + expect(await clickRunAnalysis(page), 'run button was not clickable').toBe(true); + + // The analysis must reach a terminal state, and it must be success. An error toast here is the + // signal that the runtime or the model failed to load. + await expect(page.getByText(/AxoMEME prediction complete/i)).toBeVisible({ timeout: 120000 }); + }); +}); + +test.describe('AxoMEME cost', () => { + test.setTimeout(180000); + + test('a non-AxoMEME method downloads none of the runtime or the model', async ({ page }) => { + // The invariant the whole session.js design exists for: AxoMEME is one method of fifteen, and + // the other fourteen must not pay 17 MB for it. This is the same guard the MEME hit-likelihood + // suite applies to its own model, and it matters more here because the payload is larger. + const requests = trackRequests(page); + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + + await selectMethod(page, 'FEL'); + await page.waitForTimeout(3000); + + const offending = requests().filter((u) => /ort-wasm|onnxruntime|axomeme.*\.onnx/i.test(u)); + expect(offending, `FEL pulled AxoMEME assets: ${offending.join(', ')}`).toEqual([]); + }); +}); diff --git a/package.json b/package.json index 780e7b8..dcb339b 100644 --- a/package.json +++ b/package.json @@ -9,8 +9,8 @@ }, "scripts": { "dev": "vite dev", - "prebuild": "svelte-kit sync", - "build": "svelte-kit sync && vite build", + "prebuild": "node scripts/copy-ort-wasm.mjs && svelte-kit sync", + "build": "node scripts/copy-ort-wasm.mjs && svelte-kit sync && vite build", "preview": "vite preview", "prepare": "svelte-kit sync || echo ''", "check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json", @@ -49,7 +49,9 @@ "deploy:cloudflare": "npx wrangler pages deploy .svelte-kit/cloudflare", "deploy:cloudflare:storybook": "npm run build-storybook && npx wrangler pages deploy storybook-static --project-name datamonkey3-storybook", "deploy:cloudflare:all": "./scripts/deploy-with-storybook.sh", - "dev:cloudflare": "npx wrangler pages dev .svelte-kit/cloudflare" + "dev:cloudflare": "npx wrangler pages dev .svelte-kit/cloudflare", + "ort:wasm": "node scripts/copy-ort-wasm.mjs", + "predev": "node scripts/copy-ort-wasm.mjs" }, "devDependencies": { "@chromatic-com/storybook": "^4.1.1", diff --git a/scripts/copy-ort-wasm.mjs b/scripts/copy-ort-wasm.mjs new file mode 100644 index 0000000..df1a3aa --- /dev/null +++ b/scripts/copy-ort-wasm.mjs @@ -0,0 +1,58 @@ +/** + * Copy onnxruntime-web's WASM runtime into static/ so DataMonkey serves it itself. + * + * WHY THIS EXISTS. onnxruntime-web does not bundle its WASM binary; it fetches it at runtime, and + * with no configuration it resolves to a jsDelivr CDN URL. That breaks two things at once: + * + * 1. THE PROJECT'S CORE CONSTRAINT. CLAUDE.md: "The website needs to be entirely self-contained so + * that it can be served locally. No pulling down from other domains." A CDN fetch is exactly + * that, and it fails closed on an air-gapped or offline install rather than degrading. + * 2. It fails in dev anyway, which is how this was found — the runtime aborts with "both async and + * sync fetching of the wasm failed" and onnxruntime reports "no available backend found", + * which reads like a broken model rather than a missing asset. + * + * WHY COPY RATHER THAN COMMIT. The file is 12.9 MB of third-party build output that is already + * pinned by package.json and present in node_modules. Committing it would put a binary in git + * history that goes stale the moment onnxruntime-web is upgraded, and nothing would notice. Copying + * at build time means the served asset always matches the installed package. + * + * ONLY THE BASE VARIANT IS COPIED. onnxruntime-web ships ~128 MB of dist covering four builds — + * jsep (WebGPU), asyncify, jspi and the plain SIMD+threads one. AxoMEME runs on CPU with a single + * thread, so it needs the last of those and nothing else. Copying all of them would multiply the + * deploy size for capabilities this feature does not use. + */ + +import { copyFileSync, mkdirSync, existsSync, statSync } from 'node:fs'; +import { dirname, join } from 'node:path'; +import { fileURLToPath } from 'node:url'; + +const repo = join(dirname(fileURLToPath(import.meta.url)), '..'); +const from = join(repo, 'node_modules', 'onnxruntime-web', 'dist'); +const to = join(repo, 'static', 'ort'); + +/** The plain SIMD+threads build and its loader. Nothing else is used at runtime. */ +const ASSETS = ['ort-wasm-simd-threaded.wasm', 'ort-wasm-simd-threaded.mjs']; + +if (!existsSync(from)) { + // Not fatal: `npm run build` in an environment without the optional dependency should say so + // clearly rather than emit a site whose AxoMEME path fails at the last moment. + console.warn('[ort-wasm] onnxruntime-web is not installed; skipping. AxoMEME will not run.'); + process.exit(0); +} + +mkdirSync(to, { recursive: true }); +let total = 0; +for (const name of ASSETS) { + const src = join(from, name); + if (!existsSync(src)) { + console.error( + `[ort-wasm] expected ${name} in onnxruntime-web/dist — has the package layout changed?` + ); + process.exit(1); + } + copyFileSync(src, join(to, name)); + total += statSync(src).size; +} +console.log( + `[ort-wasm] copied ${ASSETS.length} files (${(total / 1024 / 1024).toFixed(1)} MB) to static/ort/` +); diff --git a/src/lib/AnalyzeTab.svelte b/src/lib/AnalyzeTab.svelte index 0d2ffd2..817de46 100644 --- a/src/lib/AnalyzeTab.svelte +++ b/src/lib/AnalyzeTab.svelte @@ -131,6 +131,57 @@ }); try { + // AxoMEME is dispatched BEFORE the backend/WASM split, because it belongs to neither. It is + // not a HyPhy analysis: no hyphy.wasm invocation, no server job, no HyPhy JSON. It runs an + // ONNX graph in this tab and returns in seconds. The executionMode toggle does not apply — + // there is no server-side AxoMEME yet — so it is deliberately ignored rather than silently + // routing a browser-only method through a socket that has no handler for it. + if (method.toLowerCase() === 'axomeme') { + if (!$currentFile || !$currentFile.id) { + throw new Error('No file selected for analysis'); + } + + // Same source-of-truth order the other two branches use: prefer the canonical FASTA + // datareader.bf emits on upload, which has already collapsed PHYLIP / MEGA / CLUSTAL / + // NEXUS / FASTA to one shape, and only strip embedded trees from a raw user blob. + // AxoMEME cares about this more than most: a trailing newick line left in the FASTA + // would be appended to the last sequence and quietly corrupt that taxon's codons. + let alignment = $fileMetricsStore?.canonicalFasta; + if (!alignment) { + const fullFileData = await persistentFileStore.getFile($currentFile.id); + if (!fullFileData) { + throw new Error('Unable to load file data'); + } + alignment = await fullFileData.text(); + if (!alignment || !alignment.trim()) { + throw new Error('No sequence data available in selected file'); + } + alignment = stripEmbeddedTrees(alignment); + } + + // A tree with BRANCH LENGTHS is not optional here, unlike for some HyPhy methods. The + // model reads a patristic distance matrix; a topology-only tree yields all-zero + // distances and an all-zero embedding, and the model would return confident numbers + // computed from nothing. + const tree = getSelectedTreeData(); + if (!tree) { + throw new Error( + 'AxoMEME needs a phylogenetic tree. Infer one or upload your own before running it.' + ); + } + + const { axomemeAnalysisRunner } = await import('./services/AxomemeAnalysisRunner.js'); + const result = await axomemeAnalysisRunner.runAnalysis( + method, + config, + alignment, + tree, + $currentFile.id + ); + toastStore.success('AxoMEME prediction complete', { duration: 3000 }); + return result; + } + if (config.executionMode === 'backend') { // Backend execution if (!$backendConnectivity.isConnected) { @@ -389,7 +440,9 @@ {/if} diff --git a/src/lib/MethodSelector.svelte b/src/lib/MethodSelector.svelte index 329059e..eff4f0d 100644 --- a/src/lib/MethodSelector.svelte +++ b/src/lib/MethodSelector.svelte @@ -9,10 +9,7 @@ import { treeHasBranchLengths } from './services/prescreen/scope.js'; import { AlertTriangle, Play, Loader2 } from 'lucide-svelte'; import { trackEvent } from './utils/analytics.js'; - import { - countBranchGroups, - contrastFelHasEnoughGroups - } from './utils/branchGroupValidation.js'; + import { countBranchGroups, contrastFelHasEnoughGroups } from './utils/branchGroupValidation.js'; export let methodConfig; export let runMethod = null; @@ -22,6 +19,7 @@ // Supported methods - easy to update when methods are implemented const SUPPORTED_METHODS = [ + 'axomeme', 'b-still', 'fel', 'slac', @@ -40,10 +38,19 @@ // Method info with simplified descriptions and runtime estimates const METHOD_INFO = { + axomeme: { + name: 'AxoMEME', + fullName: 'AxoMEME 2.0 — neural surrogate for MEME', + // Deliberately says PREDICTS. This is a fitted model estimating what MEME would report, + // not MEME, and the dropdown is the first place a user forms that expectation. + shortDescription: 'Predict MEME per-site selection in seconds, in your browser', + supported: true + }, 'b-still': { name: 'B-STILL', fullName: 'Bayesian Significance Test of Invariant Low Likelihoods', - shortDescription: 'Detect invariant sites via posterior probabilities and Empirical Bayes Factors', + shortDescription: + 'Detect invariant sites via posterior probabilities and Empirical Bayes Factors', supported: true }, fel: { @@ -161,6 +168,22 @@ // Method-specific advanced options configurations const METHOD_ADVANCED_OPTIONS = { + // AxoMEME exposes almost nothing on purpose. It is a fitted model with one set of weights: + // there are no branch sets to select (it consumes the whole tree as a distance matrix), no + // rate-variation switch, and no genetic code choice — the code table is baked into the + // model's tokenizer as the universal one. Offering knobs that do not reach the model would be + // worse than offering none. Calling mode is the one real choice, because it is a threshold + // applied AFTER inference and genuinely changes what gets reported. + axomeme: { + callMode: { + type: 'select', + label: 'How to call sites', + default: 'pvalue', + options: ['pvalue', 'zscore', 'percentile'], + description: + 'pvalue uses fixed LRT gates (4.45 / 3.12). zscore and percentile rank sites within this alignment, so they always return some hits.' + } + }, fel: { // Branch selection options branchesToTest: { @@ -815,7 +838,8 @@ label: 'Amino Acid Property Set', default: '5PROP', options: ['5PROP', '4PROP', '3PROP', '2PROP', 'Atchley', 'LCAP'], - description: 'Set of amino acid properties to model (5PROP: hydrophobicity, polarity, volume, charge, iso-electric point)' + description: + 'Set of amino acid properties to model (5PROP: hydrophobicity, polarity, volume, charge, iso-electric point)' }, // P-value threshold pValueThreshold: { @@ -978,9 +1002,11 @@ } // Auto-detect data type from uploaded file for GARD - $: if ($fileMetricsStore?.FILE_INFO?.gencodeid !== undefined && + $: if ( + $fileMetricsStore?.FILE_INFO?.gencodeid !== undefined && selectedMethod?.toLowerCase() === 'gard' && - methodOptions[selectedMethod]) { + methodOptions[selectedMethod] + ) { const gencodeid = $fileMetricsStore.FILE_INFO.gencodeid; const detectedType = gencodeid === -2 ? 'protein' : gencodeid === -1 ? 'nucleotide' : 'codon'; if (methodOptions[selectedMethod].datatype !== detectedType) { @@ -1002,30 +1028,30 @@ } // Pre-computed renderable options array (excludes interactive-tree) - $: renderableAdvancedOptions = (selectedMethod && methodOptions[selectedMethod]) - ? Object.entries(currentMethodOptions) - .filter(([_, c]) => c.type !== 'interactive-tree') - .map(([key, config]) => { - const isEnabled = !config.dependsOn || - (methodOptions[selectedMethod] && - config.enabledWhen && - config.enabledWhen.includes( - methodOptions[selectedMethod][config.dependsOn] - )); - - // Resolve filtered options for selects constrained by another option - let effectiveConfig = config; - if (config.filteredOptionsBy && config.filteredOptions) { - const controllerValue = methodOptions[selectedMethod][config.filteredOptionsBy]; - const filtered = config.filteredOptions[controllerValue]; - if (filtered) { - effectiveConfig = { ...config, options: filtered }; - } - } - - return { key, config: effectiveConfig, isEnabled }; - }) - : []; + $: renderableAdvancedOptions = + selectedMethod && methodOptions[selectedMethod] + ? Object.entries(currentMethodOptions) + .filter(([_, c]) => c.type !== 'interactive-tree') + .map(([key, config]) => { + const isEnabled = + !config.dependsOn || + (methodOptions[selectedMethod] && + config.enabledWhen && + config.enabledWhen.includes(methodOptions[selectedMethod][config.dependsOn])); + + // Resolve filtered options for selects constrained by another option + let effectiveConfig = config; + if (config.filteredOptionsBy && config.filteredOptions) { + const controllerValue = methodOptions[selectedMethod][config.filteredOptionsBy]; + const filtered = config.filteredOptions[controllerValue]; + if (filtered) { + effectiveConfig = { ...config, options: filtered }; + } + } + + return { key, config: effectiveConfig, isEnabled }; + }) + : []; // Update genetic code ID when name changes $: { @@ -1047,12 +1073,15 @@ } // RELAX branch validation - requires TEST and REFERENCE branches to be tagged - $: relaxHasTestBranches = selectedMethod?.toLowerCase() === 'relax' && + $: relaxHasTestBranches = + selectedMethod?.toLowerCase() === 'relax' && methodOptions?.relax?.interactiveTree?.includes('{TEST}'); - $: relaxHasReferenceBranches = selectedMethod?.toLowerCase() === 'relax' && + $: relaxHasReferenceBranches = + selectedMethod?.toLowerCase() === 'relax' && (methodOptions?.relax?.interactiveTree?.includes('{REFERENCE}') || - methodOptions?.relax?.referenceBranches === 'All'); - $: relaxBranchesValid = selectedMethod?.toLowerCase() !== 'relax' || + methodOptions?.relax?.referenceBranches === 'All'); + $: relaxBranchesValid = + selectedMethod?.toLowerCase() !== 'relax' || (relaxHasTestBranches && relaxHasReferenceBranches); // Contrast-FEL branch validation - compares two or more groups, so at least two @@ -1361,7 +1390,7 @@ updateMethodOption(opt.key, +e.target.value)} + on:input={(e) => updateMethodOption(opt.key, +e.target.value)} min={opt.config.min || 0} max={opt.config.max || 1000} step={opt.config.step || 1} @@ -1374,7 +1403,7 @@ updateMethodOption(opt.key, e.target.checked)} + on:change={(e) => updateMethodOption(opt.key, e.target.checked)} disabled={!opt.isEnabled} /> {opt.config.label} @@ -1384,13 +1413,15 @@ {opt.config.label}: updateMethodOption(opt.key, e.target.value)} + on:input={(e) => updateMethodOption(opt.key, e.target.value)} placeholder={opt.config.placeholder || ''} class="option-input" disabled={!opt.isEnabled} @@ -1526,11 +1557,10 @@ {#if methodOptions?.[selectedMethod]?.branchesToTest === 'Custom'} - Contrast-FEL compares two or more groups — please define at least two branch - sets. + Contrast-FEL compares two or more groups — please define at least two branch sets. {:else} - Contrast-FEL compares two or more groups — please tag at least two branch - groups on the tree. + Contrast-FEL compares two or more groups — please tag at least two branch groups on + the tree. {/if} diff --git a/src/lib/config/methodOptions.toml b/src/lib/config/methodOptions.toml index a22eb5c..1f2471d 100644 --- a/src/lib/config/methodOptions.toml +++ b/src/lib/config/methodOptions.toml @@ -907,3 +907,38 @@ takes_value = true type = "string" default = "" choices = [] + +# AxoMEME is not a HyPhy method and has no CLI. This section exists because the method registry +# reads it, not because there are command-line options to describe: the model is a fixed set of +# weights evaluated in the browser, and the only genuine choice is a threshold applied after +# inference. Notably absent, and absent on purpose: +# - no `code` option. The genetic code is baked into the model's tokenizer as the universal table. +# Offering a choice that cannot reach the model would be worse than offering none. +# - no branch selection. AxoMEME consumes the whole tree as a distance matrix; there is no +# foreground set to tag. +[axomeme] +description = "AxoMEME 2.0 predicts MEME's per-site selection statistics from a codon alignment and tree using a neural model, in seconds instead of hours. It is a surrogate for MEME, not a replacement." + +[[axomeme.options]] +name = "alignment" +description = "An in-frame codon alignment in one of the formats supported by DataMonkey" +takes_value = true +type = "string" +default = "" +choices = [] + +[[axomeme.options]] +name = "tree" +description = "A phylogenetic tree with branch lengths. Branch lengths are required: they become the patristic distance matrix the model reads." +takes_value = true +type = "string" +default = "" +choices = [] + +[[axomeme.options]] +name = "call-mode" +description = "How to call sites. 'pvalue' uses fixed LRT gates; 'zscore' and 'percentile' rank sites within this alignment and therefore always return some hits." +takes_value = true +type = "string" +default = "pvalue" +choices = ["pvalue", "zscore", "percentile"] diff --git a/src/lib/services/AxomemeAnalysisRunner.js b/src/lib/services/AxomemeAnalysisRunner.js new file mode 100644 index 0000000..e74d989 --- /dev/null +++ b/src/lib/services/AxomemeAnalysisRunner.js @@ -0,0 +1,162 @@ +/** + * AxomemeAnalysisRunner — runs AxoMEME 2.0 entirely in the browser. + * + * WHY THIS IS A THIRD RUNNER RATHER THAN A METHOD IN THE OTHER TWO. The project's checklist for + * adding an analysis is written for HyPhy methods, and every step of it assumes one: a `command` to + * pass to hyphy.wasm, an `outputSuffix` naming the HyPhy JSON it writes, CLI argument mapping in + * WasmAnalysisRunner, a socket event in BackendAnalysisRunner, and a hyphy-eye visualiser keyed on + * the shape of that JSON. AxoMEME has none of those. It is a 3.78 MB neural network evaluated by + * onnxruntime-web against tensors this codebase builds itself, and it emits per-site predictions + * rather than a HyPhy results document. Bending it into WasmAnalysisRunner would mean threading a + * non-HyPhy path through every one of those assumptions. + * + * So it implements the same BaseAnalysisRunner lifecycle — createAnalysis, startAnalysisTracking, + * updateProgress, completeAnalysis — which is the part that actually matters for the UI, and shares + * nothing else. + * + * WHAT IT IS NOT. This is a SURROGATE for MEME, not MEME. It predicts what MEME's per-site + * statistics would be, in seconds instead of hours, and it is wrong sometimes in ways a fitted model + * is not. Nothing in this file should present its output as a completed selection analysis; that + * framing belongs in the UI copy and is why `isSurrogate` is on the result rather than implied. + * + * MAIN-THREAD COST, stated because it is the known weak point: the MDS eigendecomposition runs on the + * padded 512 x 512 matrix regardless of taxon count and takes ~600 ms for a small alignment, ~1.9 s + * at 512 taxa. That is synchronous and blocks rendering. It is tolerable against the hours a real + * MEME run takes, and the loop below yields between phases so progress paints, but a Web Worker is + * the right home for it and is the first thing to do if this feels slow. + */ + +import { BaseAnalysisRunner } from './BaseAnalysisRunner.js'; +import { parseAlignment } from '../utils/fastaValidation.js'; +import { prepareAlignment, batchSizeFor } from './axomeme/assemble.js'; +import { loadSession, runSites, isSessionLoaded } from './axomeme/session.js'; +import { buildPredictions, siteVariability } from './axomeme/postprocess.js'; +import { validateInputBundle, VERIFIED_MODEL_SHA256 } from './axomeme/modelContract.js'; +import { inspectBranchLengths } from '../utils/treeSanitation.js'; + +/** Let the browser paint between phases; the heavy stages are synchronous. */ +const yieldToBrowser = () => new Promise((resolve) => setTimeout(resolve, 0)); + +export class AxomemeAnalysisRunner extends BaseAnalysisRunner { + /** + * @param {string} method ignored — this runner serves one method + * @param {object} config + * @param {string} fastaData + * @param {string} treeData + * @param {string|null} fileId + */ + async runAnalysis(method, config, fastaData, treeData, fileId = null) { + const analysisId = await this.createAnalysis(fileId, 'AXOMEME'); + this.startAnalysisTracking(analysisId, 'AXOMEME', 'browser', 'Starting AxoMEME prediction...'); + + try { + this.updateProgress(analysisId, 'parsing', 5, 'Reading alignment...'); + const parsed = parseAlignment(fastaData); + const names = parsed.sequences.map((s) => s.header); + const sequences = parsed.sequences.map((s) => s.sequence); + if (names.length === 0) throw new Error('No sequences found in the alignment'); + await yieldToBrowser(); + + // A tree is not strictly required — the reference falls back to an all-zero distance + // matrix — but the result is close to meaningless, so it is recorded rather than hidden. + const treeReport = treeData ? inspectBranchLengths(treeData) : null; + + this.updateProgress(analysisId, 'preparing', 15, 'Computing tree distances and embedding...'); + await yieldToBrowser(); + const prepared = prepareAlignment({ + names, + sequences, + treeText: treeData || undefined, + maxSpecies: config?.maxSpecies, + referenceName: config?.referenceSequence + }); + if (prepared.totalCodons === 0) { + throw new Error('The reference sequence is shorter than one codon'); + } + + // Loading is 3.78 MB of model plus ~13 MB of runtime on the FIRST run of a session, and + // nothing at all afterwards. Saying which is happening matters — a user who has already run + // one alignment should not be told to wait for a download that will not occur. + const firstLoad = !isSessionLoaded(); + this.updateProgress( + analysisId, + 'loading', + 30, + firstLoad ? 'Downloading the AxoMEME model (first run only)...' : 'Preparing the model...' + ); + const { session, sha256 } = await loadSession(); + // Same CPU-only subpath the session uses. Importing the default entry here instead would + // pull a second, WebGPU-enabled copy of the runtime into the graph. + const ort = await import('onnxruntime-web/wasm'); + + const batch = batchSizeFor(prepared.speciesCount); + const accumulated = { lrt: [], alpha: [], beta_neg: [], beta_pos: [], p_neg: [] }; + for (let start = 0; start < prepared.totalCodons; start += batch) { + const bundle = prepared.batch(start, batch); + // The contract check is cheap next to inference and catches the whole class of errors + // that produce a well-formed tensor meaning something the model never saw. + const check = validateInputBundle(bundle, { + batch: bundle.msa_codons.dims[0], + numSpecies: prepared.speciesCount, + windowSize: prepared.windowSize + }); + if (!check.ok) { + throw new Error(`AxoMEME input check failed: ${check.errors.slice(0, 3).join('; ')}`); + } + const out = await runSites(session, bundle, ort); + for (const key of Object.keys(accumulated)) accumulated[key].push(...out[key]); + + const done = Math.min(start + batch, prepared.totalCodons); + this.updateProgress( + analysisId, + 'running', + 40 + Math.round((done / prepared.totalCodons) * 50), + `Scoring site ${done} of ${prepared.totalCodons}...` + ); + await yieldToBrowser(); + } + + this.updateProgress(analysisId, 'processing', 95, 'Building per-site results...'); + // Variability is judged over the SELECTED species, which is what the model saw — not over + // every sequence in the file, which may include taxa the tree did not contain. + const selectedSeqs = prepared.selectedNames.map((n) => sequences[names.indexOf(n)] ?? ''); + const variable = siteVariability(selectedSeqs, prepared.totalCodons); + const refSeq = sequences[names.indexOf(prepared.referenceName)] ?? sequences[0]; + const refCodons = Array.from({ length: prepared.totalCodons }, (_, i) => + refSeq.slice(i * 3, i * 3 + 3) + ); + const sites = buildPredictions(accumulated, { refCodons, variable }, config?.calling); + + const result = { + method: 'AxoMEME', + modelVersion: '2.0-viral-finetuned', + modelSha256: sha256 ?? VERIFIED_MODEL_SHA256, + // Load-bearing for the UI: these are PREDICTIONS of what MEME would report, not MEME. + isSurrogate: true, + surrogateFor: 'MEME', + sites, + summary: { + totalSites: prepared.totalCodons, + variableSites: sites.filter((s) => s.isVariable).length, + calledSites: sites.filter((s) => s.call !== 'Neutral').length, + speciesUsed: prepared.speciesCount, + speciesInAlignment: names.length, + referenceSequence: prepared.referenceName, + matchedFromTree: prepared.matchedFromTree, + duplicateSelections: prepared.duplicateSelections, + treeWarnings: treeReport && !treeReport.ok ? treeReport.reasons : [] + } + }; + + await this.completeAnalysis(analysisId, true, result); + return { analysisId, result }; + } catch (error) { + await this.completeAnalysis(analysisId, false, null, error.message); + throw error; + } + } +} + +const axomemeAnalysisRunner = new AxomemeAnalysisRunner(); +export default axomemeAnalysisRunner; +export { axomemeAnalysisRunner }; diff --git a/src/lib/services/axomeme/session.js b/src/lib/services/axomeme/session.js index 4ae7a5e..8221c6f 100644 --- a/src/lib/services/axomeme/session.js +++ b/src/lib/services/axomeme/session.js @@ -68,7 +68,29 @@ export function loadSession(options = {}) { const doFetch = options.fetchImpl ?? globalThis.fetch; // The dynamic import is the whole point — see the header. Do not hoist it. - const ort = options.ort ?? (await import('onnxruntime-web')); + // + // NOTE THE SUBPATH: 'onnxruntime-web/wasm', not 'onnxruntime-web'. The default entry is the + // full build, which resolves its binary to the JSEP variant — the WebGPU-enabled one, a 26.8 MB + // wasm this feature has no use for. Importing the CPU-only build makes it load the plain + // SIMD+threads binary (12.9 MB) that scripts/copy-ort-wasm.mjs vendors. Found the hard way: + // with the default entry the runtime asks for ort-wasm-simd-threaded.jsep.mjs and aborts with + // "no available backend found", which reads like a broken model rather than a wrong build. + const ort = options.ort ?? (await import('onnxruntime-web/wasm')); + + // SERVE THE RUNTIME OURSELVES. onnxruntime-web does not bundle its WASM binary; it fetches it + // at run time, and with no wasmPaths set it resolves to a jsDelivr CDN. That violates this + // project's core constraint — the site must be servable with no other domains involved — and + // it also simply fails, with "no available backend found", which reads like a broken model + // rather than a missing asset. scripts/copy-ort-wasm.mjs puts the binary in static/ort/ at + // build time, pinned by package.json rather than committed. + // + // numThreads 1 because multi-threading needs SharedArrayBuffer, which needs COOP/COEP headers + // this app does not set. Asking for threads without them makes the runtime fall back anyway, + // and the fallback path is slower than starting single-threaded. + if (ort.env?.wasm) { + ort.env.wasm.wasmPaths = options.wasmPaths ?? '/ort/'; + ort.env.wasm.numThreads = 1; + } const response = await doFetch(url); if (!response.ok) { diff --git a/src/routes/+page.svelte b/src/routes/+page.svelte index 92da3cb..3bb6f25 100644 --- a/src/routes/+page.svelte +++ b/src/routes/+page.svelte @@ -9,7 +9,12 @@ persistentFileStore, currentFile } from '../stores/fileInfo'; - import { analysisStore, currentAnalysis, activeAnalysisProgress, activeAnalyses } from '../stores/analyses'; + import { + analysisStore, + currentAnalysis, + activeAnalysisProgress, + activeAnalyses + } from '../stores/analyses'; import { backendAnalysisRunner } from '../lib/services/BackendAnalysisRunner.js'; import { treeStore, addTree, updateTaggedTree } from '../stores/tree'; import { trackEvent } from '../lib/utils/analytics.js'; @@ -65,6 +70,19 @@ // Consolidating methods and hyphyCommands with descriptions const methodConfig = { + // AxoMEME is NOT a HyPhy method, and the fields below are shaped for one. It has no hyphy + // command and writes no HyPhy JSON, so `command` and `outputSuffix` are null rather than + // invented — anything that dispatches on them will fail loudly instead of running the wrong + // binary. It is served by AxomemeAnalysisRunner, which evaluates an ONNX graph in the browser. + AxoMEME: { + command: null, + outputSuffix: null, + url: 'axomeme', + args: [], + runner: 'axomeme', + description: + 'Predicts what MEME would report for each site, in seconds rather than hours. A neural surrogate, not a substitute for the full analysis.' + }, aBSREL: { command: 'absrel', outputSuffix: 'ABSREL.json', @@ -754,23 +772,23 @@ // Extract meaningful error from HyPhy stdout for better error reporting let hyphyError = null; if (hyphyOut) { - const lines = hyphyOut.split('\n').filter(l => l.trim()); + const lines = hyphyOut.split('\n').filter((l) => l.trim()); const errorPatterns = [ /Expected \d+ sites?, but found \d+/, /stop codons? found/i, /not a valid/i, /divisible by 3/i, /at least 3 unique sequences/i, - /too large/i, + /too large/i ]; for (const line of lines) { - if (errorPatterns.some(p => p.test(line))) { + if (errorPatterns.some((p) => p.test(line))) { hyphyError = line.trim(); break; } } if (!hyphyError) { - const errorLine = lines.find(l => /^Error:/i.test(l.trim())); + const errorLine = lines.find((l) => /^Error:/i.test(l.trim())); if (errorLine) { hyphyError = errorLine.trim().replace(/^Error:\s*/i, ''); } @@ -778,7 +796,8 @@ } // Check for tree-related messages in datareader output (not an error, just informational) - const hasTreeParsingMessage = hyphyOut.includes('Illegal right hand side in call to Topology') || + const hasTreeParsingMessage = + hyphyOut.includes('Illegal right hand side in call to Topology') || hyphyOut.includes('tree string is invalid') || hyphyOut.includes('Newick tree spec'); @@ -788,9 +807,9 @@ // Log any error-like messages for debugging (but don't fail) if (hyphyOut.includes('Error') || hyphyOut.includes('error')) { - const errorLines = hyphyOut.split('\n').filter(line => - line.toLowerCase().includes('error') - ); + const errorLines = hyphyOut + .split('\n') + .filter((line) => line.toLowerCase().includes('error')); if (errorLines.length > 0) { console.warn('HyPhy datareader messages:', errorLines); } @@ -810,7 +829,10 @@ jsonBlob = await cliObj.download('/shared/data/results.json'); } catch (downloadError) { console.error('Failed to download results.json:', downloadError); - throw new Error(hyphyError || 'File analysis failed. The file may be in an unsupported format or contain invalid data.'); + throw new Error( + hyphyError || + 'File analysis failed. The file may be in an unsupported format or contain invalid data.' + ); } const response = await fetch(jsonBlob); const blob = await response.blob(); @@ -819,15 +841,26 @@ // Validate that we got JSON, not an error page if (jsonOut.trim().startsWith('= 0 ? 'codon' : fileMetricsJSON.FILE_INFO?.gencodeid === -1 ? 'nucleotide' : 'protein', + format: + fileMetricsJSON.FILE_INFO?.gencodeid >= 0 + ? 'codon' + : fileMetricsJSON.FILE_INFO?.gencodeid === -1 + ? 'nucleotide' + : 'protein', sequenceCount: fileMetricsJSON.FILE_INFO?.sequences || 0, siteCount: fileMetricsJSON.FILE_INFO?.sites || 0 }); @@ -936,15 +974,25 @@ const errorMsg = error.message || ''; const isTreeError = /Tree|tree|Topology|Newick/.test(errorMsg); - const errorType = isTreeError ? 'invalid-tree' - : errorMsg.includes('divisible by 3') ? 'not-codon-aligned' - : errorMsg.includes('stop codon') ? 'stop-codons' - : /nucleotide alignment|protein alignment|character states/.test(errorMsg) ? 'wrong-alphabet' - : /too large|must include prebuilt/.test(errorMsg) ? 'dataset-too-large' - : /at least \d+ unique sequences|No sequences found|No MATRIX block/.test(errorMsg) ? 'insufficient-sequences' - : /Aioli|not a valid value for parameter|Invalid parameter choice/.test(errorMsg) ? 'runtime-error' - : /format|valid alignment|valid FASTA|valid NEXUS|valid sequence|FASTA data is empty|partition specification|Sequence data found before header|File is empty/i.test(errorMsg) ? 'invalid-format' - : 'unknown'; + const errorType = isTreeError + ? 'invalid-tree' + : errorMsg.includes('divisible by 3') + ? 'not-codon-aligned' + : errorMsg.includes('stop codon') + ? 'stop-codons' + : /nucleotide alignment|protein alignment|character states/.test(errorMsg) + ? 'wrong-alphabet' + : /too large|must include prebuilt/.test(errorMsg) + ? 'dataset-too-large' + : /at least \d+ unique sequences|No sequences found|No MATRIX block/.test(errorMsg) + ? 'insufficient-sequences' + : /Aioli|not a valid value for parameter|Invalid parameter choice/.test(errorMsg) + ? 'runtime-error' + : /format|valid alignment|valid FASTA|valid NEXUS|valid sequence|FASTA data is empty|partition specification|Sequence data found before header|File is empty/i.test( + errorMsg + ) + ? 'invalid-format' + : 'unknown'; const eventPayload = { errorType, stage: currentStage, source }; if (errorType === 'unknown' || errorType === 'invalid-format') { // Defensive fallback: 338/342 unknown events had no message field in @@ -1145,7 +1193,7 @@

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    • {/if} + {#if summary.mostNegativeDistance < -0.001} +
  • + The tree contains negative branch lengths — the most negative pairwise distance was + {summary.mostNegativeDistance.toFixed(4)}. Those distances were treated as zero, which + is what the model was trained on, but a tree this far negative is worth checking. +
    • + {/if} {#if summary.duplicateSelections > 0}
  • {summary.duplicateSelections} taxon slots repeated the same sequence, which happens when diff --git a/src/lib/services/AxomemeAnalysisRunner.js b/src/lib/services/AxomemeAnalysisRunner.js index e74d989..0c1eb73 100644 --- a/src/lib/services/AxomemeAnalysisRunner.js +++ b/src/lib/services/AxomemeAnalysisRunner.js @@ -144,6 +144,11 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { referenceSequence: prepared.referenceName, matchedFromTree: prepared.matchedFromTree, duplicateSelections: prepared.duplicateSelections, + // Reported, not hidden. Clamping matches the model's training pipeline, so it is not + // an error — but a tree whose distances are meaningfully negative is a different + // situation from one carrying float noise, and only the magnitude distinguishes them. + clampedDistances: prepared.clampedDistances, + mostNegativeDistance: prepared.mostNegativeDistance, treeWarnings: treeReport && !treeReport.ok ? treeReport.reasons : [] } }; diff --git a/src/lib/services/axomeme/assemble.js b/src/lib/services/axomeme/assemble.js index 4699593..c9fbcc4 100644 --- a/src/lib/services/axomeme/assemble.js +++ b/src/lib/services/axomeme/assemble.js @@ -165,7 +165,26 @@ export function prepareAlignment(input) { const n = selected.length; // --- distances, at the REAL species count ---------------------------------------------------- + // + // NEGATIVE DISTANCES ARE CLAMPED TO ZERO, and this is the one place in the port that deliberately + // alters a value rather than passing it through. The reasoning matters: + // + // - DM3's own NJ inference emits negative branch lengths. `NJ.bf:214-220` computes the + // three-taxon closed form (d01 + d02 - d12)/2 with no Max(0, ...), so any triangle-inequality + // violation yields one. Most are float noise -- the demo alignment produces a patristic sum of + // -1.04e-5, which is zero with rounding error on it. + // - THE MODEL WAS TRAINED ON CLAMPED DISTANCES. The handoff README is explicit: "This build's + // training pipeline clamps distances >= 0." So clamping here matches what the weights were + // fitted against. It is the reference's INFERENCE path that omits the clamp, and that omission + // is exactly why it throws on 4.6% of real DM3 trees (findings log §2). + // + // So refusing a -1e-5 distance would reject an ordinary tree for a rounding artifact, while + // passing it through unclamped would feed the model something training never showed it. Clamping + // does neither. What is NOT acceptable is doing it silently, so the magnitude is recorded and a + // meaningfully negative tree still reaches the user. const dist = new Float32Array(n * n); + let clampedDistances = 0; + let mostNegativeDistance = 0; if (tree) { const { index } = leafIndex(tree); const nodes = selected.map((i) => index.get(normalizeTaxonName(names[i]))); @@ -173,7 +192,16 @@ export function prepareAlignment(input) { const full = patristicMatrix(tree, nodes); // float32 because that is what dist_tensor is, and MDS is sensitive to the rounding — see // mds.js. Doing it here rather than there keeps a single source of the rounded values. - for (let k = 0; k < n * n; k++) dist[k] = full[k]; + for (let k = 0; k < n * n; k++) { + const v = full[k]; + if (v < 0) { + clampedDistances++; + if (v < mostNegativeDistance) mostNegativeDistance = v; + dist[k] = 0; + } else { + dist[k] = v; + } + } } } // No tree, or names that do not resolve: the reference falls back to an all-zero matrix rather @@ -229,6 +257,8 @@ export function prepareAlignment(input) { selectedNames: selected.map((i) => names[i]), matchedFromTree, duplicateSelections: duplicates, + clampedDistances, + mostNegativeDistance, dist, mds, paddingMask, diff --git a/src/test/axomeme-assemble.test.js b/src/test/axomeme-assemble.test.js index baaff2a..3e05d29 100644 --- a/src/test/axomeme-assemble.test.js +++ b/src/test/axomeme-assemble.test.js @@ -183,6 +183,45 @@ describe('prepareAlignment', () => { expect(p.selectedNames[1]).toBe('far'); // farthest from it }); + it('clamps a negative distance to zero and REPORTS the magnitude', () => { + // DM3's own NJ emits negative branch lengths; the large.nex demo produces a patristic sum of + // -1.04e-5, which is zero with rounding error on it. The model was trained on clamped + // distances (the handoff README says so), so clamping matches training — but doing it silently + // would hide a genuinely broken tree, which is why the magnitude comes back out. + const p = prepareAlignment({ + names: ['a', 'b', 'c'], + sequences: ['ATG', 'ATG', 'ATG'], + treeText: '((a:-0.5,b:0.2):0.05,c:0.3);', + maxSpecies: 8 + }); + expect(Array.from(p.dist).every((v) => v >= 0)).toBe(true); + expect(p.clampedDistances).toBeGreaterThan(0); + expect(p.mostNegativeDistance).toBeCloseTo(-0.3, 5); // a-b: -0.5 + 0.2 + }); + + it('reports nothing clamped for a clean tree', () => { + const p = prep(); + expect(p.clampedDistances).toBe(0); + expect(p.mostNegativeDistance).toBe(0); + }); + + it('produces a contract-valid bundle from a tree with negative branch lengths', () => { + // The large.nex regression: the bundle used to be REJECTED by validateInputBundle because a + // patristic sum came out at -1e-5. Clamping is what makes an ordinary NJ tree usable. + const p = prepareAlignment({ + names: ['a', 'b', 'c', 'd'], + sequences: ['ATGTTA', 'ATGCTA', 'ATGGGG', 'ATGAAA'], + treeText: '((a:-0.00001,b:0.2):0.05,(c:0.3,d:0.1):0.02);', + maxSpecies: 8 + }); + const v = validateInputBundle(p.batch(0), { + batch: p.totalCodons, + numSpecies: p.speciesCount, + windowSize: p.windowSize + }); + expect(v.errors).toEqual([]); + }); + it('falls back to an all-zero distance matrix without a tree', () => { const p = prepareAlignment({ names: NAMES, sequences: SEQS, maxSpecies: 8 }); expect(Array.from(p.dist).every((v) => v === 0)).toBe(true); From e60c27f65fa82d71eac79f0f77dd1eab5441e3dd Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 12:27:25 -0700 Subject: [PATCH 08/15] Add an end-to-end comparison harness against the reference driver Compares the whole JS pipeline -- parse, patristic distances, Max-PD, tokenisation, MDS, inference, postprocessing -- against predict_regression_nexus.py on real DataMonkey MEME submissions, per site. Result on 12 alignments and 2,195 sites: worst |delta| 5.77e-6, Pearson r = 1.000000, Spearman rho = 1.000000, zero variable-site classification mismatches. Two patches must be applied to the reference before it can serve as ground truth, and both are its own bugs rather than concessions to make the numbers agree: - The codon tokenizer disagrees with the model's training vocabulary on 63 of 64 codons. - model.train() at line 1364 leaves DROPOUT ON, so the script is non-deterministic: two runs of the same input differ by up to 0.41 in predicted_lrt, which is large next to the 3.12 and 4.45 calling gates. Without disabling it the same comparison reports r = 0.925 -- the reference is sampling a different network on every run. Both sides are fed FASTA dumped by the REFERENCE's own parser, so a disagreement is a pipeline disagreement rather than two parsers reading a file differently. That is also necessary here: the corpus alignments are HyPhy-generated NEXUS using NOLABELS, which DM3's own parseNexus cannot read. This measures PORT CORRECTNESS, not model quality. Most of the corpus is AxoMEME fine-tuning data, so accuracy numbers from it would be in-sample. --- scripts/axomeme/compare_to_reference.mjs | 192 +++++++++++++++++++++++ 1 file changed, 192 insertions(+) create mode 100644 scripts/axomeme/compare_to_reference.mjs diff --git a/scripts/axomeme/compare_to_reference.mjs b/scripts/axomeme/compare_to_reference.mjs new file mode 100644 index 0000000..686d7d2 --- /dev/null +++ b/scripts/axomeme/compare_to_reference.mjs @@ -0,0 +1,192 @@ +/** + * Compare the JS AxoMEME pipeline against the reference implementation, per site. + * + * WHAT THIS DOES AND DOES NOT MEASURE. This is a PORT-CORRECTNESS check, not a model-quality one. + * It runs both implementations over the same alignments and compares their per-site output, so a + * disagreement means our JS differs from their Python. It says nothing about whether the model is + * any good — for that you need MEME's own results on data the model never trained on, and most of + * our corpus is AxoMEME fine-tuning data. + * + * The reference CSVs must come from a TOKENIZER-PATCHED copy of predict_regression_nexus.py. Its + * shipped tokenizer disagrees with the model's training vocabulary on 63 of 64 codons, so comparing + * against it unpatched would measure that bug rather than this port. + * + * Usage: + * node scripts/axomeme/compare_to_reference.mjs --refdir --data + */ + +import { readFileSync, readdirSync, existsSync } from 'node:fs'; +import { join } from 'node:path'; +import * as ort from 'onnxruntime-web/wasm'; +import { parseAlignment } from '../../src/lib/utils/fastaValidation.js'; +import { prepareAlignment, batchSizeFor } from '../../src/lib/services/axomeme/assemble.js'; +import { buildPredictions, siteVariability } from '../../src/lib/services/axomeme/postprocess.js'; + +const arg = (f, d) => { + const i = process.argv.indexOf(f); + return i > 0 ? process.argv[i + 1] : d; +}; +const refDir = arg('--refdir'); +const dataDir = arg('--data'); +const fastaDir = arg('--fasta'); +const modelPath = arg('--model'); +if (!refDir || !dataDir || !modelPath || !fastaDir) { + console.error('usage: --refdir --data --fasta --model '); + process.exit(2); +} + +ort.env.wasm.numThreads = 1; +const session = await ort.InferenceSession.create(new Uint8Array(readFileSync(modelPath))); + +/** Pearson correlation. */ +function pearson(a, b) { + const n = a.length; + if (n < 2) return NaN; + const ma = a.reduce((x, y) => x + y, 0) / n; + const mb = b.reduce((x, y) => x + y, 0) / n; + let num = 0, + da = 0, + db = 0; + for (let i = 0; i < n; i++) { + const x = a[i] - ma, + y = b[i] - mb; + num += x * y; + da += x * x; + db += y * y; + } + return da > 0 && db > 0 ? num / Math.sqrt(da * db) : NaN; +} + +/** Spearman: Pearson on average-tied ranks — the metric the ML team reports. */ +function spearman(a, b) { + const rank = (v) => { + const idx = v.map((x, i) => i).sort((i, j) => v[i] - v[j]); + const r = new Array(v.length); + let i = 0; + while (i < v.length) { + let j = i; + while (j + 1 < v.length && v[idx[j + 1]] === v[idx[i]]) j++; + const avg = (i + j) / 2 + 1; + for (let k = i; k <= j; k++) r[idx[k]] = avg; + i = j + 1; + } + return r; + }; + return pearson(rank(a), rank(b)); +} + +const rows = []; +for (const f of readdirSync(refDir) + .filter((x) => x.endsWith('.csv')) + .sort()) { + const id = f.replace(/\.csv$/, ''); + // FASTA dumped by the REFERENCE's own parser, so both sides see byte-identical sequences and a + // disagreement is a pipeline disagreement rather than two parsers reading a file differently. + const fa = join(fastaDir, `${id}.fa`); + const tre = join(dataDir, `${id}.tre`); + if (!existsSync(fa) || !existsSync(tre)) continue; + + // --- reference --- + const csv = readFileSync(join(refDir, f), 'utf8').trim().split(/\r?\n/); + const header = csv[0].split(','); + const cLrt = header.indexOf('predicted_lrt'); + const cVar = header.indexOf('is_variable'); + if (cLrt < 0) { + console.log(`${id}: no predicted_lrt column`); + continue; + } + const refLrt = [], + refVar = []; + for (let i = 1; i < csv.length; i++) { + const p = csv[i].split(','); + refLrt.push(Number(p[cLrt])); + refVar.push(Number(p[cVar])); + } + + // --- ours --- + let mine; + try { + const parsed = parseAlignment(readFileSync(fa, 'utf8')); + const names = parsed.sequences.map((s) => s.header); + const seqs = parsed.sequences.map((s) => s.sequence); + const prep = prepareAlignment({ names, sequences: seqs, treeText: readFileSync(tre, 'utf8') }); + const size = batchSizeFor(prep.speciesCount); + const acc = { lrt: [], alpha: [], beta_neg: [], beta_pos: [], p_neg: [] }; + for (let s = 0; s < prep.totalCodons; s += size) { + const b = prep.batch(s, size); + const out = await session.run({ + msa_codons: new ort.Tensor('int64', b.msa_codons.data, b.msa_codons.dims), + msa_aas: new ort.Tensor('int64', b.msa_aas.data, b.msa_aas.dims), + dist_matrix: new ort.Tensor('float32', b.dist_matrix.data, b.dist_matrix.dims), + mds_coords: new ort.Tensor('float32', b.mds_coords.data, b.mds_coords.dims), + padding_mask: new ort.Tensor('bool', b.padding_mask.data, b.padding_mask.dims) + }); + for (const k of Object.keys(acc)) acc[k].push(...out[k].data); + } + const selected = prep.selectedNames.map((n) => seqs[names.indexOf(n)] ?? ''); + const variable = siteVariability(selected, prep.totalCodons); + const refSeq = seqs[names.indexOf(prep.referenceName)] ?? seqs[0]; + const refCodons = Array.from({ length: prep.totalCodons }, (_, i) => + refSeq.slice(i * 3, i * 3 + 3) + ); + mine = buildPredictions(acc, { refCodons, variable }); + } catch (e) { + console.log(`${id}: JS threw — ${e.message}`); + continue; + } + + const n = Math.min(mine.length, refLrt.length); + if (n === 0 || mine.length !== refLrt.length) { + console.log(`${id}: SITE COUNT MISMATCH js=${mine.length} ref=${refLrt.length}`); + continue; + } + // Compare only sites BOTH sides consider variable; invariant sites are hard zeros on both and + // would inflate every correlation toward 1. + const ja = [], + ra = []; + let worst = 0, + varMismatch = 0; + for (let i = 0; i < n; i++) { + if (Boolean(mine[i].isVariable) !== Boolean(refVar[i])) varMismatch++; + if (!mine[i].isVariable || !refVar[i]) continue; + ja.push(mine[i].lrt); + ra.push(refLrt[i]); + worst = Math.max(worst, Math.abs(mine[i].lrt - refLrt[i])); + } + rows.push({ + id, + sites: n, + compared: ja.length, + varMismatch, + worst, + pearson: pearson(ja, ra), + spearman: spearman(ja, ra) + }); + console.log( + `${id} sites=${String(n).padStart(5)} compared=${String(ja.length).padStart(5)}` + + ` varMismatch=${String(varMismatch).padStart(4)} max|Δ|=${worst.toExponential(2)}` + + ` r=${pearson(ja, ra).toFixed(6)} rho=${spearman(ja, ra).toFixed(6)}` + ); +} + +console.log('\n=== SUMMARY ==='); +console.log(`alignments compared: ${rows.length}`); +if (rows.length) { + const meanBy = (k) => + rows.reduce((s, r) => s + (Number.isFinite(r[k]) ? r[k] : 0), 0) / rows.length; + console.log(`total sites: ${rows.reduce((s, r) => s + r.sites, 0).toLocaleString()}`); + console.log( + `variable-site classification mismatches: ${rows.reduce((s, r) => s + r.varMismatch, 0)}` + ); + console.log( + `worst per-site |Δ| overall: ${Math.max(...rows.map((r) => r.worst)).toExponential(3)}` + ); + console.log(`mean Pearson r: ${meanBy('pearson').toFixed(6)}`); + console.log(`mean Spearman ρ: ${meanBy('spearman').toFixed(6)}`); + const bad = rows.filter((r) => !(r.pearson > 0.99)); + if (bad.length) { + console.log(`\n[!] ${bad.length} alignments with r <= 0.99:`); + for (const b of bad) + console.log(` ${b.id} r=${b.pearson.toFixed(4)} max|Δ|=${b.worst.toExponential(2)}`); + } +} From 72bf13ba1e8c7b515a43e617835b2c2baa86eb7f Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Sat, 8 Aug 2026 12:55:26 -0700 Subject: [PATCH 09/15] Treat AxoMEME as a site RANKER, and default the calling mode to percentile Measured across 12 real DataMonkey MEME submissions and 662 variable sites, the model's predicted LRT does not reach the thresholds the reference driver compares it against: highest predicted LRT anywhere 3.902 sites clearing the Tier 2 gate 3.12 1 of 662 sites clearing the Tier 1 gate 4.45 0 of 662 On an alignment where MEME itself reports 17 sites at p <= 0.05, the model's maximum was 2.484. Under the reference's `pvalue` default this feature ships reporting nothing on real data. That is not a threshold to tune. It reflects what the model is: its authors report SPEARMAN RANK CORRELATION, not calibration, and rank correlation can be good while the absolute scale is off by a factor of two. So the default is now `percentile`, which asks the question the model can answer -- which sites in THIS alignment look most interesting -- rather than one it cannot. The gates themselves are unchanged, so selecting `pvalue` still reproduces the reference exactly. The presentation follows the same decision: - Tier labels state the rule that produced them ("Top 2%", "Z >= 2.5", "LRT >= 4.45") instead of "High" and "Medium", which imply a calibrated confidence the model does not have. - The results table leads with RANK, and the model's output is labelled Score rather than LRT. Calling it an LRT invites a comparison against chi-square thresholds that it clears once in 662 sites. - The header says the score is not a p-value and orders sites within this alignment only. Also: stop warning about float-noise negative branch lengths. inspectBranchLengths reports ANY negative, and DM3's NJ routinely emits values around -1e-5 -- the bundled large.nex has two. Those are clamped to match the model's training pipeline, and warning about them trains users to ignore the warning box. A meaningfully negative tree still surfaces, via the filter and via the most-negative-distance line. --- e2e/19-axomeme.spec.js | 13 +++-- src/lib/AxomemeResults.svelte | 44 ++++++++++----- src/lib/MethodSelector.svelte | 12 +++-- src/lib/config/methodOptions.toml | 8 +-- src/lib/services/AxomemeAnalysisRunner.js | 33 ++++++++++-- src/lib/services/axomeme/postprocess.js | 39 ++++++++++++-- src/test/axomeme-postprocess.test.js | 66 +++++++++++++++++++---- 7 files changed, 175 insertions(+), 40 deletions(-) diff --git a/e2e/19-axomeme.spec.js b/e2e/19-axomeme.spec.js index 509286d..9185a1e 100644 --- a/e2e/19-axomeme.spec.js +++ b/e2e/19-axomeme.spec.js @@ -133,12 +133,17 @@ test.describe('AxoMEME', () => { await expect(page.getByText(/AxoMEME predictions/i)).toBeVisible({ timeout: 30000 }); // The framing is not decoration. A researcher reading a per-site table is one step from - // writing it up, so the page must say MEME was not run. + // writing it up, so the page must say MEME was not run AND that the number is not a p-value. const body = await page.locator('body').innerText(); expect(body).toMatch(/MEME was not run/i); - - // The per-site table, with the columns that carry the actual claim. - await expect(page.getByRole('columnheader', { name: 'LRT' })).toBeVisible(); + expect(body).toMatch(/not a p-value/i); + + // The table leads with RANK, and the model's output is labelled Score rather than LRT. + // Measured across 12 real submissions, its predicted LRT clears the chi-square gates once in + // 662 sites, so presenting it as an LRT invites a comparison it cannot support. + await expect(page.getByRole('columnheader', { name: 'Rank' })).toBeVisible(); + await expect(page.getByRole('columnheader', { name: 'Score' })).toBeVisible(); + await expect(page.getByRole('columnheader', { name: 'LRT' })).toHaveCount(0); await expect(page.getByRole('columnheader', { name: /dN/ })).toBeVisible(); }); diff --git a/src/lib/AxomemeResults.svelte b/src/lib/AxomemeResults.svelte index b79a437..daa0260 100644 --- a/src/lib/AxomemeResults.svelte +++ b/src/lib/AxomemeResults.svelte @@ -52,8 +52,9 @@

    AxoMEME predictions

    - Estimates of what MEME would report for each site, from a neural model. MEME was not run. - Treat these as a fast preview and confirm anything you rely on with the full analysis. + A neural model RANKS the sites of this alignment by how MEME-like their signal looks. MEME + was not run, and the score is not a p-value — it orders sites within this alignment and says + nothing about significance. Use it to decide where to look, then run MEME to find out.

    @@ -97,19 +98,27 @@
    {called.length}
    -
    sites predicted under selection
    +
    + top-ranked {called.length === 1 ? 'site' : 'sites'} +
    {summary.variableSites ?? 0}
    -
    variable sites scored
    +
    + variable {summary.variableSites === 1 ? 'site' : 'sites'} scored +
    {summary.totalSites ?? sites.length}
    -
    codon sites
    +
    + codon {(summary.totalSites ?? sites.length) === 1 ? 'site' : 'sites'} +
    {summary.speciesUsed ?? '—'}
    -
    sequences used
    +
    + {summary.speciesUsed === 1 ? 'sequence' : 'sequences'} used +
    @@ -119,8 +128,8 @@ > - No site cleared the calling threshold. That is a prediction of what MEME would find, not a - result from MEME — a real run can still report sites here. + Nothing to rank — no site in this alignment has amino-acid variation, so the model has + nothing to score. A MEME run would have the same problem. {/if} @@ -128,7 +137,7 @@
    showing {shown.length} of {total} @@ -142,7 +151,15 @@ Site Codon AA - LRT + + Rank + + + Score + dN⁺ dS p⁺ @@ -156,6 +173,9 @@ {row.refCodon} {row.refAa} {#if row.isVariable} + + {row.percentile.toFixed(0)}% + {fmt(row.lrt)} {fmt(row.betaPosDn)} {fmt(row.alphaDs)} @@ -163,7 +183,7 @@ {:else} - + not scored — no amino-acid variation {/if} @@ -186,7 +206,7 @@ {/if}
    - AxoMEME {data.modelVersion} + AxoMEME {data.modelVersion} · ranked by {summary.callMode ?? 'percentile'} {#if summary.referenceSequence} · reference sequence {summary.referenceSequence} {/if} diff --git a/src/lib/MethodSelector.svelte b/src/lib/MethodSelector.svelte index 6f23a8e..ef6b4ae 100644 --- a/src/lib/MethodSelector.svelte +++ b/src/lib/MethodSelector.svelte @@ -177,11 +177,15 @@ axomeme: { callMode: { type: 'select', - label: 'How to call sites', - default: 'pvalue', - options: ['pvalue', 'zscore', 'percentile'], + label: 'How to rank sites', + // percentile, not the reference driver's pvalue default. The model's predicted LRT does + // not reach the chi-square gates pvalue compares against — measured across 12 real + // submissions, one site in 662 cleared 3.12 and none cleared 4.45 — so pvalue makes the + // method silent. See CALL_DEFAULTS in postprocess.js. + default: 'percentile', + options: ['percentile', 'zscore', 'pvalue'], description: - 'pvalue uses fixed LRT gates (4.45 / 3.12). zscore and percentile rank sites within this alignment, so they always return some hits.' + 'percentile and zscore rank sites within this alignment, which is what the model is built to do. pvalue compares against fixed LRT gates (4.45 / 3.12) that its scores rarely reach — it will usually report nothing.' } }, fel: { diff --git a/src/lib/config/methodOptions.toml b/src/lib/config/methodOptions.toml index 1f2471d..dc85e89 100644 --- a/src/lib/config/methodOptions.toml +++ b/src/lib/config/methodOptions.toml @@ -917,7 +917,7 @@ choices = [] # - no branch selection. AxoMEME consumes the whole tree as a distance matrix; there is no # foreground set to tag. [axomeme] -description = "AxoMEME 2.0 predicts MEME's per-site selection statistics from a codon alignment and tree using a neural model, in seconds instead of hours. It is a surrogate for MEME, not a replacement." +description = "AxoMEME 2.0 ranks the sites of a codon alignment by how MEME-like their selection signal looks, using a neural model, in seconds instead of hours. It orders sites within an alignment; it does not produce calibrated significance, and it is not a replacement for MEME." [[axomeme.options]] name = "alignment" @@ -937,8 +937,8 @@ choices = [] [[axomeme.options]] name = "call-mode" -description = "How to call sites. 'pvalue' uses fixed LRT gates; 'zscore' and 'percentile' rank sites within this alignment and therefore always return some hits." +description = "How to rank sites. 'percentile' and 'zscore' order sites within this alignment, which is what the model does well — its authors report Spearman rank correlation, not calibration. 'pvalue' compares the predicted LRT against fixed chi-square gates (4.45 / 3.12) that the model's scores rarely reach, so it will usually report nothing." takes_value = true type = "string" -default = "pvalue" -choices = ["pvalue", "zscore", "percentile"] +default = "percentile" +choices = ["percentile", "zscore", "pvalue"] diff --git a/src/lib/services/AxomemeAnalysisRunner.js b/src/lib/services/AxomemeAnalysisRunner.js index 0c1eb73..642ff76 100644 --- a/src/lib/services/AxomemeAnalysisRunner.js +++ b/src/lib/services/AxomemeAnalysisRunner.js @@ -30,10 +30,16 @@ import { BaseAnalysisRunner } from './BaseAnalysisRunner.js'; import { parseAlignment } from '../utils/fastaValidation.js'; import { prepareAlignment, batchSizeFor } from './axomeme/assemble.js'; import { loadSession, runSites, isSessionLoaded } from './axomeme/session.js'; -import { buildPredictions, siteVariability } from './axomeme/postprocess.js'; +import { buildPredictions, siteVariability, CALL_DEFAULTS } from './axomeme/postprocess.js'; import { validateInputBundle, VERIFIED_MODEL_SHA256 } from './axomeme/modelContract.js'; import { inspectBranchLengths } from '../utils/treeSanitation.js'; +/** + * Below this magnitude a negative branch length is float noise from NJ's unclamped subtraction, not a + * broken tree. Matches the threshold the results page uses for the distance-clamping notice. + */ +const NOISY_NEGATIVE = -0.001; + /** Let the browser paint between phases; the heavy stages are synchronous. */ const yieldToBrowser = () => new Promise((resolve) => setTimeout(resolve, 0)); @@ -125,7 +131,14 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { const refCodons = Array.from({ length: prepared.totalCodons }, (_, i) => refSeq.slice(i * 3, i * 3 + 3) ); - const sites = buildPredictions(accumulated, { refCodons, variable }, config?.calling); + const sites = buildPredictions( + accumulated, + { refCodons, variable }, + { + ...(config?.calling ?? {}), + ...(config?.callMode ? { mode: config.callMode } : {}) + } + ); const result = { method: 'AxoMEME', @@ -142,6 +155,9 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { speciesUsed: prepared.speciesCount, speciesInAlignment: names.length, referenceSequence: prepared.referenceName, + // Named in the footer: it changes what a "call" means, and the default is not the + // reference driver's. + callMode: config?.calling?.mode ?? config?.callMode ?? CALL_DEFAULTS.mode, matchedFromTree: prepared.matchedFromTree, duplicateSelections: prepared.duplicateSelections, // Reported, not hidden. Clamping matches the model's training pipeline, so it is not @@ -149,7 +165,18 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { // situation from one carrying float noise, and only the magnitude distinguishes them. clampedDistances: prepared.clampedDistances, mostNegativeDistance: prepared.mostNegativeDistance, - treeWarnings: treeReport && !treeReport.ok ? treeReport.reasons : [] + // Only surface tree problems worth acting on. inspectBranchLengths reports ANY negative + // branch, and DM3's NJ routinely emits float noise around -1e-5 — the bundled + // large.nex demo has two. Those are clamped (matching the model's training pipeline) + // and warning about them trains users to ignore the warning box, which is worse than + // not having one. A meaningfully negative tree still gets through, via this filter and + // via the mostNegativeDistance line the results page renders. + treeWarnings: + treeReport && !treeReport.ok + ? treeReport.reasons.filter( + (r) => !/negative/.test(r) || (treeReport.min ?? 0) <= NOISY_NEGATIVE + ) + : [] } }; diff --git a/src/lib/services/axomeme/postprocess.js b/src/lib/services/axomeme/postprocess.js index e71e99f..cbf50a5 100644 --- a/src/lib/services/axomeme/postprocess.js +++ b/src/lib/services/axomeme/postprocess.js @@ -24,9 +24,27 @@ import { GENETIC_CODE, aaToken } from './tokenizer.js'; -/** Default tier gates, from the driver's argparse (lines 984-991). */ +/** + * Default tier gates. + * + * THE DEFAULT MODE IS `percentile`, WHICH IS NOT THE REFERENCE'S DEFAULT, and the reason is measured + * rather than preferential. The reference defaults to `pvalue`, which compares the predicted LRT + * against 4.45 and 3.12 — chi-square thresholds for a GENUINE likelihood ratio. The model's output + * does not live on that scale. Across 12 real DataMonkey submissions and 662 variable sites, the + * highest predicted LRT anywhere was 3.902; exactly one site cleared 3.12 and none cleared 4.45. + * On an alignment where MEME itself reports 17 sites at p <= 0.05, the model's maximum was 2.484. + * + * So under `pvalue` this feature ships reporting nothing on real data. That is not a threshold worth + * tuning — it reflects what the model is: a RANKER. The metric its authors report is Spearman rank + * correlation, not calibration, and rank correlation can be good while absolute scale is off. + * `percentile` asks the question the model can answer — which sites in THIS alignment look most + * interesting — instead of one it cannot. + * + * The gates themselves are unchanged from the driver's argparse (lines 984-991), so switching modes + * reproduces the reference exactly. + */ export const CALL_DEFAULTS = Object.freeze({ - mode: 'pvalue', + mode: 'percentile', tier1LrtGate: 4.45, // p <= 0.05 tier2LrtGate: 3.12, // p <= 0.10 tier1Zscore: 2.5, @@ -166,6 +184,16 @@ export function buildPredictions(outputs, sites, callOptions = {}) { rows[rowIdx].percentile = pct[k]; }); + const tierLabels = + cfg.mode === 'zscore' + ? { tier1: `Z \u2265 ${cfg.tier1Zscore}`, tier2: `Z \u2265 ${cfg.tier2Zscore}` } + : cfg.mode === 'pvalue' + ? { tier1: `LRT \u2265 ${cfg.tier1LrtGate}`, tier2: `LRT \u2265 ${cfg.tier2LrtGate}` } + : { + tier1: `Top ${(100 - cfg.tier1Percentile).toFixed(0)}%`, + tier2: `Top ${(100 - cfg.tier2Percentile).toFixed(0)}%` + }; + for (const i of varIdx) { const r = rows[i]; let t1 = false; @@ -180,8 +208,11 @@ export function buildPredictions(outputs, sites, callOptions = {}) { t1 = r.lrt >= cfg.tier1LrtGate; t2 = !t1 && r.lrt >= cfg.tier2LrtGate; } - if (t1) r.call = 'Tier 1 (High)'; - else if (t2) r.call = 'Tier 2 (Medium)'; + // Labels say what the tier MEANS rather than how confident it sounds. "High" and "Medium" + // imply a calibrated confidence the model does not have; "Top 2%" is exactly what percentile + // mode computed, and a reader can tell at a glance that it is relative to this alignment. + if (t1) r.call = tierLabels.tier1; + else if (t2) r.call = tierLabels.tier2; } return rows; diff --git a/src/test/axomeme-postprocess.test.js b/src/test/axomeme-postprocess.test.js index 7b0cb0d..a0b68f4 100644 --- a/src/test/axomeme-postprocess.test.js +++ b/src/test/axomeme-postprocess.test.js @@ -163,17 +163,65 @@ describe('buildPredictions', () => { expect(rows[3].percentile).toBeCloseTo(100, 6); }); - it('calls tiers off the LRT gates by default', () => { - expect(CALL_DEFAULTS.mode).toBe('pvalue'); + it("DEFAULTS TO percentile, not the reference driver's pvalue", () => { + // Measured, not preferential. Across 12 real DataMonkey submissions and 662 variable sites the + // highest predicted LRT anywhere was 3.902; one site cleared the 3.12 gate and none cleared + // 4.45 — including an alignment where MEME itself reports 17 sites at p <= 0.05. Under pvalue + // this feature reports nothing on real data. + expect(CALL_DEFAULTS.mode).toBe('percentile'); + // The gates themselves are unchanged, so switching modes still reproduces the reference. expect(CALL_DEFAULTS.tier1LrtGate).toBe(4.45); expect(CALL_DEFAULTS.tier2LrtGate).toBe(3.12); + expect(CALL_DEFAULTS.tier1Percentile).toBe(98.0); + expect(CALL_DEFAULTS.tier2Percentile).toBe(95.0); + }); + + it('returns top-ranked sites by default even when no score approaches an LRT gate', () => { + // The whole reason for the default. These scores are realistic for the model — nothing near + // 3.12 — and pvalue mode calls nothing on them while percentile surfaces the top of the range. + const lrt = new Float32Array(100); + for (let i = 0; i < 100; i++) lrt[i] = (i / 99) * 2.5; + const ranked = buildPredictions(outputs(100, { lrt }), sites(100)); + expect(ranked.filter((r) => r.call !== NEUTRAL_CALL).length).toBeGreaterThan(0); + const gated = buildPredictions(outputs(100, { lrt }), sites(100), { mode: 'pvalue' }); + expect(gated.filter((r) => r.call !== NEUTRAL_CALL)).toEqual([]); + }); + + it('labels tiers by what they MEAN, not by a confidence word', () => { + // "High"/"Medium" imply a calibrated confidence the model does not have. Each label states the + // rule that produced it, so a reader can see it is relative to this alignment. + const lrt = new Float32Array(100); + for (let i = 0; i < 100; i++) lrt[i] = i; + const pct = buildPredictions(outputs(100, { lrt }), sites(100)); + expect(pct.map((r) => r.call)).toContain('Top 2%'); + expect(pct.map((r) => r.call)).toContain('Top 5%'); + + // A uniform spread cannot reach z = 2.5 — its maximum z is about 1.73 regardless of n — so this + // needs a genuine outlier. Same bound as the short-alignment case pinned further down. + const spike = new Float32Array(100).fill(1); + spike[42] = 100; + const z = buildPredictions(outputs(100, { lrt: spike }), sites(100), { mode: 'zscore' }); + expect(z[42].call).toBe('Z ≥ 2.5'); + + const p = buildPredictions( + outputs(4, { lrt: new Float32Array([5.0, 3.5, 3.13, 1.0]) }), + sites(4), + { mode: 'pvalue' } + ); + expect(p[0].call).toBe('LRT ≥ 4.45'); + expect(p[1].call).toBe('LRT ≥ 3.12'); + expect(p[3].call).toBe(NEUTRAL_CALL); + }); + + it('still reproduces the reference exactly when pvalue mode is chosen', () => { const rows = buildPredictions( outputs(4, { lrt: new Float32Array([5.0, 3.5, 3.13, 1.0]) }), - sites(4) + sites(4), + { mode: 'pvalue' } ); - expect(rows[0].call).toMatch(/Tier 1/); - expect(rows[1].call).toMatch(/Tier 2/); - expect(rows[2].call).toMatch(/Tier 2/); + expect(rows[0].call).toMatch(/4\.45/); + expect(rows[1].call).toMatch(/3\.12/); + expect(rows[2].call).toMatch(/3\.12/); expect(rows[3].call).toBe(NEUTRAL_CALL); }); @@ -185,7 +233,7 @@ describe('buildPredictions', () => { // sitting exactly on a gate falls to the lower tier. const exact = new Float32Array([3.12]); expect(exact[0]).toBeLessThan(3.12); - const rows = buildPredictions(outputs(1, { lrt: exact }), sites(1)); + const rows = buildPredictions(outputs(1, { lrt: exact }), sites(1), { mode: 'pvalue' }); expect(rows[0].call).toBe(NEUTRAL_CALL); }); @@ -195,7 +243,7 @@ describe('buildPredictions', () => { // possible z is 1.732, below even the Tier 2 threshold of 2.0. Ten sites clears it. const lrt = new Float32Array([1, 1, 1, 1, 1, 1, 1, 1, 1, 100]); const z = buildPredictions(outputs(10, { lrt }), sites(10), { mode: 'zscore' }); - expect(z[9].call).toMatch(/Tier/); // the outlier + expect(z[9].call).toMatch(/^Z ≥ /); // the outlier expect(z[0].call).toBe(NEUTRAL_CALL); const p = buildPredictions(outputs(10, { lrt }), sites(10), { @@ -203,7 +251,7 @@ describe('buildPredictions', () => { tier1Percentile: 90, tier2Percentile: 70 }); - expect(p[9].call).toMatch(/Tier 1/); + expect(p[9].call).toBe('Top 10%'); expect(p[0].call).toBe(NEUTRAL_CALL); }); From 82481c24af202c618086d2152cc41587d944f724 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Mon, 10 Aug 2026 10:44:26 -0700 Subject: [PATCH 10/15] Render AxoMEME results with hyphy-scope's AxomemeVisualization Moves the plots and the per-site table into hyphy-scope, where every consumer of AxoMEME output can reach them, and leaves behind only what is specific to THIS application: what DataMonkey did to the user's data before the model saw it. That split is not bookkeeping. DM3's own neighbour-joining inference emits negative branch lengths, sequences absent from the tree are dropped, and taxa past the 512 cap are subsampled -- none of it visible in a per-site score, all of it changing that score. hyphy-scope has no business knowing about NJ.bf, and a reader has every business knowing their tree was altered. The library component brings four plots (ranked sites, score by site, score distribution, dS vs dN+), a sortable paginated table, and deliberately no significance threshold line -- see its commit for why. vite.config.ts gains a note rather than a change: hyphy-scope must NOT be added to optimizeDeps.exclude even when linked. It pulls d3, phylotree, circos and @observablehq/plot, and excluding it makes the dev server transform all of that per request -- enough to stall the page, which presents as an analysis that never finishes rather than as a slow import. Pick up library changes by rebuilding hyphy-scope and restarting dev. --- e2e/19-axomeme.spec.js | 21 ++-- src/lib/AxomemeResults.svelte | 205 +++++----------------------------- vite.config.ts | 6 + 3 files changed, 48 insertions(+), 184 deletions(-) diff --git a/e2e/19-axomeme.spec.js b/e2e/19-axomeme.spec.js index 9185a1e..6cdb2bc 100644 --- a/e2e/19-axomeme.spec.js +++ b/e2e/19-axomeme.spec.js @@ -136,13 +136,20 @@ test.describe('AxoMEME', () => { // writing it up, so the page must say MEME was not run AND that the number is not a p-value. const body = await page.locator('body').innerText(); expect(body).toMatch(/MEME was not run/i); - expect(body).toMatch(/not a p-value/i); - - // The table leads with RANK, and the model's output is labelled Score rather than LRT. - // Measured across 12 real submissions, its predicted LRT clears the chi-square gates once in - // 662 sites, so presenting it as an LRT invites a comparison it cannot support. - await expect(page.getByRole('columnheader', { name: 'Rank' })).toBeVisible(); - await expect(page.getByRole('columnheader', { name: 'Score' })).toBeVisible(); + expect(body).toMatch(/not calibrated/i); + + // The table and plots come from hyphy-scope's AxomemeVisualization; DataMonkey contributes only + // the caveats about what it did to the data first. These assertions are on the library's + // output, so they also catch a stale `npm link` or a package build that did not include it. + await expect(page.locator('.axomeme-visualization')).toBeVisible(); + await expect(page.locator('.axomeme-plot svg').first()).toBeVisible({ timeout: 20000 }); + + // Rank is a first-class column and the score is NOT labelled LRT. Measured across 12 real + // submissions, the model's predicted LRT clears the chi-square gates once in 662 sites, so + // presenting it as an LRT invites a comparison it cannot support. + await expect(page.getByRole('columnheader', { name: 'Percentile' })).toBeVisible(); + // exact: log(1+score) also matches a loose "Score". + await expect(page.getByRole('columnheader', { name: 'Score', exact: true })).toBeVisible(); await expect(page.getByRole('columnheader', { name: 'LRT' })).toHaveCount(0); await expect(page.getByRole('columnheader', { name: /dN/ })).toBeVisible(); }); diff --git a/src/lib/AxomemeResults.svelte b/src/lib/AxomemeResults.svelte index daa0260..5e84886 100644 --- a/src/lib/AxomemeResults.svelte +++ b/src/lib/AxomemeResults.svelte @@ -1,66 +1,35 @@ {#if data}
    -
    -
    - -

    AxoMEME predictions

    -
    -

    - A neural model RANKS the sites of this alignment by how MEME-like their signal looks. MEME - was not run, and the score is not a p-value — it orders sites within this alignment and says - nothing about significance. Use it to decide where to look, then run MEME to find out. -

    -
    - - - {#if summary.speciesUsed !== summary.speciesInAlignment || summary.treeWarnings?.length || summary.duplicateSelections > 0} + {#if hasCaveats}
    @@ -69,12 +38,18 @@
      {#if summary.speciesUsed !== summary.speciesInAlignment}
    • - {summary.speciesUsed} of {summary.speciesInAlignment} sequences were used — the rest were - not found in the tree{summary.speciesUsed === 512 + {summary.speciesUsed} of {summary.speciesInAlignment} sequences were used — the rest + were not found in the tree{summary.speciesUsed === 512 ? ', or fell outside the 512-taxon limit' : ''}.
      • {/if} + {#if summary.duplicateSelections > 0} +
    • + {summary.duplicateSelections} taxon slots repeated the same sequence, which happens when + a tree carries no usable branch lengths. +
      • + {/if} {#if summary.mostNegativeDistance < -0.001}
    • The tree contains negative branch lengths — the most negative pairwise distance was @@ -82,12 +57,6 @@ is what the model was trained on, but a tree this far negative is worth checking.
      • {/if} - {#if summary.duplicateSelections > 0} -
    • - {summary.duplicateSelections} taxon slots repeated the same sequence, which happens when - a tree carries no usable branch lengths. -
      • - {/if} {#each summary.treeWarnings ?? [] as warning}
    • {warning}
      • {/each} @@ -95,124 +64,6 @@
    {/if} -
    -
    -
    {called.length}
    -
    - top-ranked {called.length === 1 ? 'site' : 'sites'} -
    -
    -
    -
    {summary.variableSites ?? 0}
    -
    - variable {summary.variableSites === 1 ? 'site' : 'sites'} scored -
    -
    -
    -
    {summary.totalSites ?? sites.length}
    -
    - codon {(summary.totalSites ?? sites.length) === 1 ? 'site' : 'sites'} -
    -
    -
    -
    {summary.speciesUsed ?? '—'}
    -
    - {summary.speciesUsed === 1 ? 'sequence' : 'sequences'} used -
    -
    -
    - - {#if called.length === 0} -
    - - - Nothing to rank — no site in this alignment has amino-acid variation, so the model has - nothing to score. A MEME run would have the same problem. - -
    - {/if} - -
    - - - showing {shown.length} of {total} - -
    - -
    - - - - - - - - - - - - - - - - {#each shown as row (row.site)} - - - - - {#if row.isVariable} - - - - - - {:else} - - - {/if} - - - {/each} - -
    SiteCodonAA - Rank - - Score - dN⁺dSp⁺Call
    {row.site}{row.refCodon}{row.refAa} - {row.percentile.toFixed(0)}% - {fmt(row.lrt)}{fmt(row.betaPosDn)}{fmt(row.alphaDs)}{fmt(row.pPos, 4)} - not scored — no amino-acid variation - - {row.call} -
    -
    - - {#if shown.length < total} - - {/if} - -
    - AxoMEME {data.modelVersion} · ranked by {summary.callMode ?? 'percentile'} - {#if summary.referenceSequence} - · reference sequence {summary.referenceSequence} - {/if} - {#if data.modelSha256} - · model {data.modelSha256.slice(0, 12)} - {/if} -
    +
    {/if} diff --git a/vite.config.ts b/vite.config.ts index b399928..99f27de 100644 --- a/vite.config.ts +++ b/vite.config.ts @@ -67,6 +67,12 @@ export default defineConfig({ optimizeDeps: { include: ['@biowasm/aioli', 'toml', 'marked', 'socket.io-client'], // Exclude linked packages so changes are picked up immediately. + // + // hyphy-scope is deliberately NOT in this list even when linked via `npm link`. It pulls d3, + // phylotree, circos and @observablehq/plot, and excluding it from pre-bundling makes the dev + // server transform all of that on every request — enough to stall the page, which presents as + // an analysis that never finishes rather than as a slow import. Pre-bundle it and pick up + // library changes by rebuilding hyphy-scope and restarting dev (or `vite dev --force`). exclude: ['phylotree', 'alivibe'] } }); From 1d5eb1466156c1c5d86bf5393f2f446a5b52cd62 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Mon, 10 Aug 2026 12:41:35 -0700 Subject: [PATCH 11/15] Mark AxoMEME as Beta in the selector and on its results MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The MODEL is still under active development, not the integration around it, and that is the distinction the badge is for: the code is settled, the numbers may move between releases. It appears in both places a user meets the method, because they are reached independently — someone opening a shared results view never sees the selector, and someone browsing methods never reaches the results. Follows the existing METHOD_INFO pattern (a flag beside `supported`, rendered next to the Coming Soon badge) rather than a special case, so a second beta method needs one line. Coloured in the app's accent rather than the amber used for Coming Soon, so "beta" does not read as a warning. The library component takes `beta` as a prop rather than hard-coding it; DataMonkey knows this model's status, hyphy-scope should not assume it. e2e asserts the badge in both places AND its absence on FEL, so it cannot start decorating every method. --- e2e/19-axomeme.spec.js | 34 ++++++++++++++++++++++++++++++++++ src/lib/AxomemeResults.svelte | 6 +++--- src/lib/MethodSelector.svelte | 27 +++++++++++++++++++++++++++ 3 files changed, 64 insertions(+), 3 deletions(-) diff --git a/e2e/19-axomeme.spec.js b/e2e/19-axomeme.spec.js index 6cdb2bc..93e0a94 100644 --- a/e2e/19-axomeme.spec.js +++ b/e2e/19-axomeme.spec.js @@ -154,6 +154,40 @@ test.describe('AxoMEME', () => { await expect(page.getByRole('columnheader', { name: /dN/ })).toBeVisible(); }); + test('is marked Beta wherever a user meets it', async ({ page }) => { + // The MODEL is under active development, not the integration. The badge has to appear both + // where the method is chosen and where the numbers are read — someone landing on a shared + // results view never sees the selector. + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + + await selectMethod(page, 'AxoMEME'); + await expect(page.locator('.beta-badge')).toBeVisible({ timeout: 10000 }); + + // And it must be specific to AxoMEME rather than decorating every method. + await selectMethod(page, 'FEL'); + await expect(page.locator('.beta-badge')).toHaveCount(0); + + await selectMethod(page, 'AxoMEME'); + expect(await clickRunAnalysis(page), 'run button was not clickable').toBe(true); + await expect(page.getByText(/AxoMEME prediction complete/i)).toBeVisible({ timeout: 120000 }); + await page + .getByRole('button', { name: /results/i }) + .first() + .click(); + await page + .locator('.analysis-card') + .filter({ hasText: /AXOMEME Analysis/i }) + .getByRole('button', { name: 'View' }) + .first() + .click(); + await expect(page.locator('.axomeme-beta')).toBeVisible({ timeout: 30000 }); + }); + test('suppresses controls that cannot reach the model', async ({ page }) => { // Both were live at first and both imply a capability AxoMEME does not have: there is no // server-side AxoMEME, and the model's tokenizer bakes in the universal genetic code. diff --git a/src/lib/AxomemeResults.svelte b/src/lib/AxomemeResults.svelte index 5e84886..d0a82c3 100644 --- a/src/lib/AxomemeResults.svelte +++ b/src/lib/AxomemeResults.svelte @@ -38,8 +38,8 @@
      {#if summary.speciesUsed !== summary.speciesInAlignment}
    • - {summary.speciesUsed} of {summary.speciesInAlignment} sequences were used — the rest - were not found in the tree{summary.speciesUsed === 512 + {summary.speciesUsed} of {summary.speciesInAlignment} sequences were used — the rest were + not found in the tree{summary.speciesUsed === 512 ? ', or fell outside the 512-taxon limit' : ''}.
      • @@ -64,6 +64,6 @@
    {/if} - +
    {/if} diff --git a/src/lib/MethodSelector.svelte b/src/lib/MethodSelector.svelte index ef6b4ae..39692c5 100644 --- a/src/lib/MethodSelector.svelte +++ b/src/lib/MethodSelector.svelte @@ -41,6 +41,10 @@ axomeme: { name: 'AxoMEME', fullName: 'AxoMEME 2.0 — neural surrogate for MEME', + // The MODEL is still under active development, not the integration. That distinction is + // what the badge is for: a user should know the numbers may move between releases even + // though the code around them is settled. + beta: true, // Deliberately says PREDICTS. This is a fitted model estimating what MEME would report, // not MEME, and the dropdown is the first place a user forms that expectation. shortDescription: 'Predict MEME per-site selection in seconds, in your browser', @@ -1292,6 +1296,7 @@ {#each availableMethods as method} {/each} @@ -1302,6 +1307,14 @@ {#if currentMethod}
    {currentMethod.info.shortDescription} + {#if currentMethod.info.beta} +
    + Beta +
    + {/if} {#if !currentMethod.info.supported}
    Coming Soon @@ -1706,6 +1719,20 @@ gap: 12px; } + .beta-badge { + display: inline-flex; + align-items: center; + padding: 2px 8px; + background: #ede9fe; + border: 1px solid #8b5cf6; + border-radius: 12px; + font-size: 11px; + font-weight: 500; + color: #5b21b6; + text-transform: uppercase; + letter-spacing: 0.025em; + } + .coming-soon-badge { display: inline-flex; align-items: center; From da41bed770237a9312bf3f23a7e86672114928a0 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Mon, 10 Aug 2026 13:01:03 -0700 Subject: [PATCH 12/15] Consume hyphy-scope 1.10.0 from the registry instead of a link MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The AxoMEME visualization lives in hyphy-scope, which was consumed via `npm link` while both sides were developed together. Published 1.9.1 predates the component, so until now this branch only built on a machine with the symlink in place. 1.10.0 is published with AxomemeVisualization; this pins it and drops the link. Verified with the symlink removed and the Vite cache cleared, so nothing stale could mask a failure: clean build, 514 unit tests, 9 AxoMEME e2e including every bundled demo, and the MEME byte guards still passing. The note in vite.config.ts stays. It explains why hyphy-scope must not be added to optimizeDeps.exclude — it pulls d3, phylotree and circos, and excluding it stalls the dev server badly enough to look like a hung analysis — and that applies to anyone who links it again for local development. --- package-lock.json | 20 ++++++++++---------- package.json | 2 +- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/package-lock.json b/package-lock.json index 4b51b14..12443c9 100644 --- a/package-lock.json +++ b/package-lock.json @@ -13,7 +13,7 @@ "@sentry/sveltekit": "^9", "alivibe": ">=0.1.0", "d3": "^7.9.0", - "hyphy-scope": "^1.9.1", + "hyphy-scope": "^1.10.0", "jszip": "^3.10.1", "lucide-svelte": "^0.559.0", "marked": "^15.0.12", @@ -6269,9 +6269,9 @@ "license": "MIT" }, "node_modules/d3-request/node_modules/d3-dispatch": { - "version": "1.0.3", - "resolved": "https://registry.npmjs.org/d3-dispatch/-/d3-dispatch-1.0.3.tgz", - "integrity": "sha512-Qh2DR3neW3lq/ug4oymXHYoIsA91nYt47ERb+fPKjRg6zLij06aP7KqHHl2NyziK9ASxrR3GLkHCtZvXe/jMVg==", + "version": "1.0.6", + "resolved": "https://registry.npmjs.org/d3-dispatch/-/d3-dispatch-1.0.6.tgz", + "integrity": "sha512-fVjoElzjhCEy+Hbn8KygnmMS7Or0a9sI2UzGwoB7cCtvI1XpVN9GpoYlnb3xt2YV66oXYb1fLJ8GMvP4hdU1RA==", "license": "BSD-3-Clause" }, "node_modules/d3-request/node_modules/d3-dsv": { @@ -7947,9 +7947,9 @@ } }, "node_modules/hyphy-scope": { - "version": "1.9.1", - "resolved": "https://registry.npmjs.org/hyphy-scope/-/hyphy-scope-1.9.1.tgz", - "integrity": "sha512-KzvOkZGhAgnJINThk/PcfAq6xYOVX2RYFeZ9WDuxiNB0r2EXAHOGwHH/LPcHXRS/93R3b+cwTGQuUtEYAId/Pg==", + "version": "1.10.0", + "resolved": "https://registry.npmjs.org/hyphy-scope/-/hyphy-scope-1.10.0.tgz", + "integrity": "sha512-vywKNq2wgAwDns16orJn95tZqiKNLVKzuNywIHglKrebHwec0QENRbNDtimx9J+ux6YpbUOJTmwkMtaa5jZdUQ==", "license": "MIT", "dependencies": { "@observablehq/plot": "^0.6.11", @@ -8722,9 +8722,9 @@ "license": "MIT" }, "node_modules/lodash-es": { - "version": "4.17.21", - "resolved": "https://registry.npmjs.org/lodash-es/-/lodash-es-4.17.21.tgz", - "integrity": "sha512-mKnC+QJ9pWVzv+C4/U3rRsHapFfHvQFoFB92e52xeyGMcX6/OlIl78je1u8vePzYZSkkogMPJ2yjxxsb89cxyw==", + "version": "4.18.1", + "resolved": "https://registry.npmjs.org/lodash-es/-/lodash-es-4.18.1.tgz", + "integrity": "sha512-J8xewKD/Gk22OZbhpOVSwcs60zhd95ESDwezOFuA3/099925PdHJ7OFHNTGtajL3AlZkykD32HykiMo+BIBI8A==", "license": "MIT" }, "node_modules/lodash.castarray": { diff --git a/package.json b/package.json index dcb339b..8f9282c 100644 --- a/package.json +++ b/package.json @@ -105,7 +105,7 @@ "@sentry/sveltekit": "^9", "alivibe": ">=0.1.0", "d3": "^7.9.0", - "hyphy-scope": "^1.9.1", + "hyphy-scope": "^1.10.0", "jszip": "^3.10.1", "lucide-svelte": "^0.559.0", "marked": "^15.0.12", From 2f9dec446af2f76e2f957fee1f2cf6de166b46ef Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Mon, 10 Aug 2026 13:32:40 -0700 Subject: [PATCH 13/15] Run E2E only on demand, not on every PR and push MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The team runs e2e locally before pushing, which made the CI copy redundant on every PR and expensive: 13 minutes, of which 84% was the tests themselves. That cost was not fixable the easy ways. The setup — install, browsers, build — is only 2 minutes, so caching or a faster runner buys nothing. Playwright runs with `workers: 1` in CI deliberately, because parallel workers contend for the single dev server and reintroduce the flake that setting was chosen to stop, so raising it trades minutes for intermittent red. Sharding across jobs would work and is more machinery than a locally-run suite justifies. The workflow is kept rather than deleted, and gains a suite selector, so it can be started from the Actions tab or `gh workflow run e2e.yml`. The header says when that is worth doing: before a release, and on any change to what the app DOWNLOADS. The byte-budget guards exist because a previous version shipped 13.5 MB of ML runtime to every method and rendered nothing while an "is the element absent?" assertion passed throughout — the class of regression least likely to be checked from memory. `wasm-e2e` was previously gated on `github.ref == 'refs/heads/main'`. With no push trigger left that condition could never fire, so the selector replaces it rather than leaving a job that silently never runs. main is not a protected branch, so no required status check is affected. --- .github/workflows/e2e.yml | 31 ++++++++++++++++++++++++++----- 1 file changed, 26 insertions(+), 5 deletions(-) diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml index fc4b3cc..f7c555f 100644 --- a/.github/workflows/e2e.yml +++ b/.github/workflows/e2e.yml @@ -1,14 +1,33 @@ name: E2E Tests +# MANUAL ONLY. These do not run on pull requests or pushes. +# +# The team's workflow is to run e2e locally before pushing (`npm run test:e2e`), which made this +# redundant on every PR — and expensive: 13 minutes, of which 84% was the tests themselves. Playwright +# runs with `workers: 1` in CI, deliberately, because parallel workers contend for the single dev +# server and reintroduce flake. So the cost was not fixable by caching or a faster runner; only by +# sharding across jobs, which is more machinery than a locally-run suite justifies. +# +# The workflow is kept rather than deleted, and can be started from the Actions tab or with +# `gh workflow run e2e.yml`. Worth doing before a release, or on any change to what the app +# DOWNLOADS: the byte-budget guards in 18-meme-hit-likelihood.spec.js and 19-axomeme.spec.js exist +# because a previous version shipped 13.5 MB of ML runtime to every method and rendered nothing, and +# an "is the element absent?" assertion passed the whole time. Those are the tests least likely to be +# run from memory and most likely to catch a regression nobody thought to look for. on: - pull_request: - branches: [main] - push: - branches: [main] + workflow_dispatch: + inputs: + suite: + description: 'Which suite to run' + required: false + default: 'fast' + type: choice + options: [fast, wasm, both] jobs: fast-e2e: name: Fast E2E Tests + if: inputs.suite == 'fast' || inputs.suite == 'both' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -40,8 +59,10 @@ jobs: wasm-e2e: name: WASM E2E Tests + # Previously main-only. With no push trigger left, that condition would never fire, so the + # selector above is what decides now. + if: inputs.suite == 'wasm' || inputs.suite == 'both' runs-on: ubuntu-latest - if: github.ref == 'refs/heads/main' steps: - uses: actions/checkout@v4 From 1ce19b0a41a87ea31857559bb457bfa44b410845 Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Mon, 10 Aug 2026 13:51:07 -0700 Subject: [PATCH 14/15] Fix state and correctness defects found in review of #153 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Nine findings, each verified before being fixed. STATE Execution mode was clobbered permanently. `browserOnly` assigned `executionMode = 'local'` with no restore, so choosing Backend Server for FEL, browsing to AxoMEME, and returning to FEL silently ran the next analysis through WASM — for a large dataset, the exact case the server exists for. The previous choice is now saved and given back. The runner reported `executionMode: 'browser'`, a third value no consumer knows. It is persisted to IndexedDB, and `cleanupInterruptedAnalyses` reaps stale runs by testing `=== 'wasm'`, so an AxoMEME run interrupted during the ~600 ms-2 s synchronous MDS was stuck at `running` forever, and the viewer showed "Execution: Unknown". It now reports 'wasm', which is what it is. CORRECTNESS The calling mode was resolved with OPPOSITE precedence for scoring and for reporting, so a config carrying both keys could score with percentile gates while the footer told the reader the calls meant "Z >= 2.5". One source of truth now. `push(...out[key])` overflowed V8's argument limit. `batchSizeFor` scales as 1/N^2, so a ten-taxon alignment batches 167,772 sites — measured to throw "Maximum call stack size exceeded" at ~90% progress. Copied element-wise instead. Sequences were re-resolved with `names.indexOf(name)`, which returns the FIRST match, while `orderSpecies` keeps the LAST. With duplicate FASTA headers the variability flags came from a different sequence than the model was tokenised from. `prepareAlignment` now exposes indices. `loadSession` memoised on a hand-listed set of options that omitted `verifyHash`, so one `loadSession({verifyHash: false})` could cache a session never checked against the pinned hash — which the runner then stamped as verified. The guard is inverted: memoise only when no options are passed. HONESTY `matchedFromTree` was plumbed into the summary and read by nothing. When no sequence name matches a tree label — `seq1` vs `seq1_2009` — every distance and every embedding coordinate is zero, yet every other counter looks healthy, so the caveat panel stayed hidden and the user saw a full ranked table built without a phylogeny. It is now the first caveat listed. The tree guard checked only that the string was non-empty, while the comment directly above it said branch lengths were mandatory. A topology-only tree passed and produced confident numbers from an all-zero distance matrix. It now uses `treeHasBranchLengths`, and says why in the error. TESTS The test named "memoises, so a second alignment does not re-download 17 MB" asserted the opposite — it passed options, which bypass the memo, so the production path had no coverage. Rewritten to drive the real path, plus a second test pinning that options bypass it. --- src/lib/AnalyzeTab.svelte | 14 +++++++ src/lib/AxomemeResults.svelte | 13 +++++++ src/lib/MethodSelector.svelte | 19 +++++++++- src/lib/services/AxomemeAnalysisRunner.js | 42 ++++++++++++++------- src/lib/services/axomeme/assemble.js | 5 +++ src/lib/services/axomeme/session.js | 12 +++++- src/test/axomeme-review-regressions.test.js | 35 +++++++++++++++++ src/test/axomeme-session.test.js | 36 +++++++++++++++--- 8 files changed, 154 insertions(+), 22 deletions(-) create mode 100644 src/test/axomeme-review-regressions.test.js diff --git a/src/lib/AnalyzeTab.svelte b/src/lib/AnalyzeTab.svelte index 817de46..a1fa3c2 100644 --- a/src/lib/AnalyzeTab.svelte +++ b/src/lib/AnalyzeTab.svelte @@ -12,6 +12,7 @@ import FileIndicator from './FileIndicator.svelte'; import TabNavigation from './TabNavigation.svelte'; import TreeSourceSelector from './TreeSourceSelector.svelte'; + import { treeHasBranchLengths } from './services/prescreen/scope.js'; import backendAnalysisRunner from './services/BackendAnalysisRunner.js'; import wasmAnalysisRunner from './services/WasmAnalysisRunner.js'; import { ChevronDown, TreeDeciduous } from '$lib/icons'; @@ -169,6 +170,19 @@ 'AxoMEME needs a phylogenetic tree. Infer one or upload your own before running it.' ); } + // A non-empty string is not enough, and the comment above already said so. A + // topology-only tree — `(a,(b,c));` — or one whose lengths are all zero is truthy, + // reaches prepareAlignment, and yields an all-zero distance matrix and an all-zero + // embedding. The model then returns confident-looking numbers computed from nothing, + // and inspectBranchLengths reports `ok: true` for the all-zero case, so no warning + // fires either. treeHasBranchLengths is the predicate that actually answers this. + if (!treeHasBranchLengths(tree)) { + throw new Error( + 'AxoMEME needs a tree with branch lengths — it reads them as evolutionary distances. ' + + 'This tree has none, so every pair of sequences would look equally related. Infer a ' + + 'neighbor-joining tree or upload one with branch lengths.' + ); + } const { axomemeAnalysisRunner } = await import('./services/AxomemeAnalysisRunner.js'); const result = await axomemeAnalysisRunner.runAnalysis( diff --git a/src/lib/AxomemeResults.svelte b/src/lib/AxomemeResults.svelte index d0a82c3..46b42af 100644 --- a/src/lib/AxomemeResults.svelte +++ b/src/lib/AxomemeResults.svelte @@ -21,6 +21,7 @@ // Only worth interrupting the reader for. Sequences dropped, taxa repeated, a tree negative beyond // float noise, or branch-length problems the sanitiser judged worth reporting. $: hasCaveats = + summary.matchedFromTree === false || summary.speciesUsed !== summary.speciesInAlignment || summary.treeWarnings?.length > 0 || summary.duplicateSelections > 0 || @@ -36,6 +37,18 @@ What the model was given
      + {#if summary.matchedFromTree === false} + +
    • + No sequence name matched a tree label, so the tree contributed + nothing — every pair of sequences was treated as equally related. These rankings come + from the alignment alone. Check that your tree's labels match your sequence names. +
      • + {/if} {#if summary.speciesUsed !== summary.speciesInAlignment}
    • {summary.speciesUsed} of {summary.speciesInAlignment} sequences were used — the rest were diff --git a/src/lib/MethodSelector.svelte b/src/lib/MethodSelector.svelte index 39692c5..da04953 100644 --- a/src/lib/MethodSelector.svelte +++ b/src/lib/MethodSelector.svelte @@ -1005,8 +1005,23 @@ * nothing, which is the worst of the three options. */ $: browserOnly = Boolean(currentMethod?.config?.browserOnly); - // Keep the reported mode honest for analytics and for the run-started toast. - $: if (browserOnly && executionMode !== 'local') executionMode = 'local'; + // Keep the reported mode honest for analytics and the run-started toast — but REMEMBER what the + // user chose and give it back. + // + // This used to assign `executionMode = 'local'` with no restore. Picking Backend Server for FEL, + // then browsing to AxoMEME, then returning to FEL left the radio on Local and silently ran the + // next analysis through WASM instead of the server — which for a large dataset is the whole reason + // the server exists. Nothing told the user their choice had been discarded. + let executionModeBeforeBrowserOnly = null; + $: if (browserOnly) { + if (executionMode !== 'local') { + executionModeBeforeBrowserOnly = executionMode; + executionMode = 'local'; + } + } else if (executionModeBeforeBrowserOnly) { + executionMode = executionModeBeforeBrowserOnly; + executionModeBeforeBrowserOnly = null; + } // Get current method's advanced options $: currentMethodOptions = selectedMethod diff --git a/src/lib/services/AxomemeAnalysisRunner.js b/src/lib/services/AxomemeAnalysisRunner.js index 642ff76..4b1014e 100644 --- a/src/lib/services/AxomemeAnalysisRunner.js +++ b/src/lib/services/AxomemeAnalysisRunner.js @@ -53,7 +53,13 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { */ async runAnalysis(method, config, fastaData, treeData, fileId = null) { const analysisId = await this.createAnalysis(fileId, 'AXOMEME'); - this.startAnalysisTracking(analysisId, 'AXOMEME', 'browser', 'Starting AxoMEME prediction...'); + // 'wasm', NOT 'browser'. This value is persisted to IndexedDB and read by consumers that know + // only two vocabularies: analysisStore.cleanupInterruptedAnalyses() reaps stale runs by testing + // `executionMode === 'wasm'`, and the result viewer and progress row label the mode from it. A + // third value made an interrupted AxoMEME run unreapable — stuck at `running` forever after a + // refresh during the ~600 ms-2 s synchronous MDS — and rendered "Execution: Unknown". AxoMEME + // runs in the browser like the WASM path, so it shares that label. + this.startAnalysisTracking(analysisId, 'AXOMEME', 'wasm', 'Starting AxoMEME prediction...'); try { this.updateProgress(analysisId, 'parsing', 5, 'Reading alignment...'); @@ -110,7 +116,14 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { throw new Error(`AxoMEME input check failed: ${check.errors.slice(0, 3).join('; ')}`); } const out = await runSites(session, bundle, ort); - for (const key of Object.keys(accumulated)) accumulated[key].push(...out[key]); + // NOT `push(...out[key])`. batchSizeFor scales as 1/N^2, so a few-taxon alignment gets + // batches of 10^5-10^6 sites, and spreading that many elements into a call blows V8's + // argument limit with "Maximum call stack size exceeded" at ~90% progress. Measured: it + // throws at 167,772, which is exactly the batch size for a 10-taxon alignment. + for (const key of Object.keys(accumulated)) { + const src = out[key]; + for (let i = 0; i < src.length; i++) accumulated[key].push(src[i]); + } const done = Math.min(start + batch, prepared.totalCodons); this.updateProgress( @@ -125,20 +138,23 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { this.updateProgress(analysisId, 'processing', 95, 'Building per-site results...'); // Variability is judged over the SELECTED species, which is what the model saw — not over // every sequence in the file, which may include taxa the tree did not contain. - const selectedSeqs = prepared.selectedNames.map((n) => sequences[names.indexOf(n)] ?? ''); + // By INDEX, not `names.indexOf(name)`. With duplicate FASTA headers indexOf returns the FIRST + // match while orderSpecies keeps the LAST, so the variability flags would come from a + // different sequence than the one the model was tokenised from. + const selectedSeqs = prepared.selectedIndices.map((i) => sequences[i] ?? ''); const variable = siteVariability(selectedSeqs, prepared.totalCodons); - const refSeq = sequences[names.indexOf(prepared.referenceName)] ?? sequences[0]; + const refSeq = sequences[prepared.referenceIndex] ?? sequences[0]; const refCodons = Array.from({ length: prepared.totalCodons }, (_, i) => refSeq.slice(i * 3, i * 3 + 3) ); - const sites = buildPredictions( - accumulated, - { refCodons, variable }, - { - ...(config?.calling ?? {}), - ...(config?.callMode ? { mode: config.callMode } : {}) - } - ); + // ONE source of truth for the calling mode. This and the summary below used to resolve it + // with OPPOSITE precedence, so a config carrying both could score with percentile gates + // while the footer told the reader the calls meant "Z >= 2.5". + const callConfig = { + ...(config?.calling ?? {}), + ...(config?.callMode ? { mode: config.callMode } : {}) + }; + const sites = buildPredictions(accumulated, { refCodons, variable }, callConfig); const result = { method: 'AxoMEME', @@ -157,7 +173,7 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { referenceSequence: prepared.referenceName, // Named in the footer: it changes what a "call" means, and the default is not the // reference driver's. - callMode: config?.calling?.mode ?? config?.callMode ?? CALL_DEFAULTS.mode, + callMode: callConfig.mode ?? CALL_DEFAULTS.mode, matchedFromTree: prepared.matchedFromTree, duplicateSelections: prepared.duplicateSelections, // Reported, not hidden. Clamping matches the model's training pipeline, so it is not diff --git a/src/lib/services/axomeme/assemble.js b/src/lib/services/axomeme/assemble.js index c9fbcc4..800ef5d 100644 --- a/src/lib/services/axomeme/assemble.js +++ b/src/lib/services/axomeme/assemble.js @@ -255,6 +255,11 @@ export function prepareAlignment(input) { windowSize, referenceName: refName, selectedNames: selected.map((i) => names[i]), + // Indices as well as names. A caller that maps a name back with `names.indexOf()` gets the + // FIRST match, which is the wrong record when the alignment has duplicate headers — and this + // module already resolved duplicates the other way round. + selectedIndices: selected.slice(), + referenceIndex: names.indexOf(refName), matchedFromTree, duplicateSelections: duplicates, clampedDistances, diff --git a/src/lib/services/axomeme/session.js b/src/lib/services/axomeme/session.js index 8221c6f..23f9395 100644 --- a/src/lib/services/axomeme/session.js +++ b/src/lib/services/axomeme/session.js @@ -61,7 +61,15 @@ async function sha256Hex(buffer) { * @returns {Promise<{session: any, sha256: string|null, bytes: number}>} */ export function loadSession(options = {}) { - if (sessionPromise && !options.url && !options.ort && !options.fetchImpl) return sessionPromise; + // Memoise ONLY the production call — an options object of any kind bypasses the cache. + // + // This used to list url/ort/fetchImpl by name, which left `verifyHash` and `wasmPaths` out: a + // single `loadSession({ verifyHash: false })` would populate sessionPromise with a session that was + // never checked against VERIFIED_MODEL_SHA256, and every later production call would return it + // with `sha256: null` — which the runner then stamps as the verified hash. Inverting the test to + // "no options at all" cannot go stale when a new option is added. + const isProductionCall = Object.keys(options).length === 0; + if (sessionPromise && isProductionCall) return sessionPromise; const promise = (async () => { const url = options.url ?? MODEL_URL; @@ -123,7 +131,7 @@ export function loadSession(options = {}) { return { session, sha256, bytes: buffer.byteLength }; })(); - if (!options.url && !options.ort && !options.fetchImpl) sessionPromise = promise; + if (isProductionCall) sessionPromise = promise; // A failed load must not be memoised as a permanent failure — a transient fetch error would // otherwise disable the feature for the rest of the page's life. promise.catch(() => { diff --git a/src/test/axomeme-review-regressions.test.js b/src/test/axomeme-review-regressions.test.js new file mode 100644 index 0000000..2781d12 --- /dev/null +++ b/src/test/axomeme-review-regressions.test.js @@ -0,0 +1,35 @@ +import { describe, it, expect } from 'vitest'; +import { prepareAlignment, batchSizeFor } from '../lib/services/axomeme/assemble.js'; + +describe('review regressions', () => { + it('exposes indices, so a duplicate FASTA header cannot resolve to the wrong record', () => { + // names.indexOf() returns the FIRST match; orderSpecies keeps the LAST. Without indices the + // variability flags would be computed from a different sequence than the model was fed. + const names = ['dup', 'other', 'dup']; + const sequences = ['ATGAAA', 'ATGTTT', 'ATGCCC']; + const p = prepareAlignment({ + names, + sequences, + treeText: '((dup:0.1,other:0.2):0.05,dup:0.3);', + maxSpecies: 8 + }); + expect(p.selectedIndices).toBeDefined(); + expect(p.selectedIndices.every((i) => Number.isInteger(i))).toBe(true); + // The duplicate must resolve to an index whose name really is that name... + for (let k = 0; k < p.selectedIndices.length; k++) { + expect(names[p.selectedIndices[k]]).toBe(p.selectedNames[k]); + } + // ...and at least one selected index must NOT be the naive indexOf answer, or this alignment + // would not exercise the bug. + const naive = p.selectedNames.map((n) => names.indexOf(n)); + expect(naive).not.toEqual(p.selectedIndices); + expect(Number.isInteger(p.referenceIndex)).toBe(true); + }); + + it('batchSizeFor can exceed the argument-spread limit, so results must not be spread', () => { + // The number that made `push(...batch)` throw "Maximum call stack size exceeded" at ~90% + // progress. Pinned so nobody reintroduces the spread thinking the batches are small. + expect(batchSizeFor(10)).toBeGreaterThan(125000); + expect(batchSizeFor(4)).toBeGreaterThan(125000); + }); +}); diff --git a/src/test/axomeme-session.test.js b/src/test/axomeme-session.test.js index 7e57199..6867c66 100644 --- a/src/test/axomeme-session.test.js +++ b/src/test/axomeme-session.test.js @@ -102,16 +102,42 @@ describe('loadSession', () => { expect(err.message).toMatch(/404/); }); - it('memoises, so a second alignment does not re-download 17 MB', async () => { + it('memoises the PRODUCTION path, so a second alignment does not re-download 17 MB', async () => { + // This test used to pass `ort`/`fetchImpl`, which loadSession treats as "do not memoise" — so it + // asserted the BYPASS path and the memo itself had no coverage at all. A regression that dropped + // the memo would have left the suite green while every alignment re-downloaded 3.78 MB of model. + // + // The production path takes no options, so it is driven here by stubbing the globals it reaches + // for. onnxruntime-web cannot be imported under jsdom, so the assertion is on the FETCH count: + // one download no matter how many callers ask. + const buffer = SOME_BYTES; + let fetches = 0; + const realFetch = globalThis.fetch; + globalThis.fetch = async () => { + fetches++; + return { ok: true, status: 200, statusText: 'OK', arrayBuffer: async () => buffer }; + }; + try { + const a = loadSession(); + const b = loadSession(); + expect(a, 'a second call returned a different promise — the memo is not shared').toBe(b); + expect(isSessionLoaded(), 'isSessionLoaded stayed false on the production path').toBe(true); + await Promise.allSettled([a, b]); + // It fails at the hash check or the ort import, but only ONE fetch may have happened. + expect(fetches).toBeLessThanOrEqual(1); + } finally { + globalThis.fetch = realFetch; + } + }); + + it('bypasses the memo when options are supplied, so tests stay independent', async () => { const ort = fakeOrt(); const fetchImpl = okFetch(); - // The memo only applies to the production path (no overrides), so drive it through a module - // where the default URL is used but the runtime is injected — the override check is by - // identity of the options, so assert on the fetch count for the overridden path instead. await loadSession({ ort, fetchImpl, verifyHash: false }); await loadSession({ ort, fetchImpl, verifyHash: false }); - // Overridden calls deliberately bypass the memo so tests stay independent. expect(fetchImpl).toHaveBeenCalledTimes(2); + // And an options call must never populate the production memo — including verifyHash:false, + // which would otherwise cache a session that was never checked against the pinned artifact. expect(isSessionLoaded()).toBe(false); }); From ceee9ca7d0e805bd8f0b58fd58298d3bbc511eaf Mon Sep 17 00:00:00 2001 From: Steven Weaver Date: Mon, 10 Aug 2026 14:25:24 -0700 Subject: [PATCH 15/15] Fix the remaining review findings MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit CANCELLATION. The runner never registered in `activeAnalyses`, which is how the base class cancels, and its scoring loop read no abort flag. Cancelling an AxoMEME run left it scoring, calling updateProgress against a row the user had cancelled, and finally writing `completed` over the cancelled record with a success toast. It now records cancellations itself and checks between batches — the only point the loop yields — returning without touching the record. DUPLICATE TOASTS. BaseAnalysisRunner.completeAnalysis already reports both outcomes, so the branch's own success toast stacked a second one, and letting the runner's rethrow reach the outer catch stacked a second error toast. Neither other branch rethrows on the normal path, which is why only AxoMEME double-reported. STALE ESTIMATE. MemeHitLikelihood kept rendering the previous alignment's band, and its "about 93%" sentence, while a new estimate was in flight. `seq` stops a superseded RESPONSE overwriting a newer one but does not clear what is on screen. `computing` was assigned in three places and read in none; gating `shown` and `idle` on it lets the pending branch fire. REVALIDATION. dist_matrix, mds_coords and padding_mask are per-alignment, emitted as b memcpy'd copies of one array, and every batch rescanned up to the whole 64 MB budget three times to re-check the same block. Validated once instead. RUNOUTLOOK. The panel lived inside the execution-mode branch, so making AxoMEME browser-only removed it as a side effect of nesting. It now has its own condition, browser-only methods are excluded deliberately rather than accidentally — RunOutlook's runtime model has nothing to say about a fixed neural model — and their real cost is stated in the note instead. `outlookTree` is no longer computed for a panel that is not mounted. LAYOUT RESERVE. RunOutlook's placeholder height was two hand-measured pixel constants duplicated from MemeHitLikelihood.css, with a comment telling the next person to re-measure whenever the copy changed, and recording that they were already 5.5px stale. The reserve is now declared once in app.css — which is always loaded, unlike the lazily-loaded chunk the placeholder is drawn before — and the placeholder derives its height with calc(). The derived values are identical to the measured ones (121px / 103.5px), and an e2e now asserts the property those numbers existed to protect: the Run button does not move when the estimate resolves. Measured shift is 0. --- e2e/18-meme-hit-likelihood.spec.js | 40 +++++++++++++++++++ src/app.css | 26 ++++++++++++ src/lib/AnalyzeTab.svelte | 26 +++++++----- src/lib/MemeHitLikelihood.css | 11 ++---- src/lib/MemeHitLikelihood.svelte | 13 ++++-- src/lib/MethodSelector.svelte | 41 ++++++++++++------- src/lib/RunOutlook.svelte | 23 +++++------ src/lib/services/AxomemeAnalysisRunner.js | 48 +++++++++++++++++++---- 8 files changed, 173 insertions(+), 55 deletions(-) diff --git a/e2e/18-meme-hit-likelihood.spec.js b/e2e/18-meme-hit-likelihood.spec.js index 651907a..53ec065 100644 --- a/e2e/18-meme-hit-likelihood.spec.js +++ b/e2e/18-meme-hit-likelihood.spec.js @@ -362,6 +362,46 @@ test.describe('MEME hit-likelihood', () => { * less methods pay for it again), was fetched before the listener existed and was therefore * invisible. Each test below owns its whole page lifecycle, with the recorder attached first. */ +test.describe('MEME hit-likelihood layout', () => { + test.setTimeout(180000); + + test('the Run button does not move when the estimate resolves', async ({ page }) => { + // The panel sits directly above the Run button, so if the pending placeholder is not exactly as + // tall as the mounted row, the button shifts under a pointer already travelling towards it. + // That reserve used to be two hand-measured pixel constants in RunOutlook, duplicated from + // MemeHitLikelihood.css with a comment telling the next person to re-measure — and the comment + // recorded that they were already 5.5px stale. They are now derived with calc() from tokens + // declared once in app.css, and this asserts the property those numbers existed to protect. + await freshStart(page); + await loadDemoFile(page, 'CD2-slim.fna'); + await expect(async () => { + await goToAnalyzeTab(page); + await expect(page.locator('[data-testid="method-dropdown"]')).toBeVisible({ timeout: 5000 }); + }).toPass({ timeout: 60000 }); + + // The tokens must resolve from the always-loaded stylesheet, not the lazy chunk — the + // placeholder is drawn before that chunk exists. + const reserve = await page.evaluate(() => + getComputedStyle(document.documentElement).getPropertyValue('--hit-body-reserve').trim() + ); + expect(reserve, '--hit-body-reserve is not defined in the always-loaded stylesheet').toMatch( + /\d/ + ); + + await selectMethod(page, 'MEME'); + const runButton = page.locator('[data-testid="run-analysis-btn"]'); + await runButton.waitFor({ timeout: 30000 }); + const before = (await runButton.boundingBox())?.y ?? 0; + // Long enough for the estimator chunk, the model fetch and the score to all land. + await page.waitForTimeout(9000); + const after = (await runButton.boundingBox())?.y ?? 0; + expect( + Math.abs(after - before), + 'the Run button moved when the estimate resolved' + ).toBeLessThan(2); + }); +}); + test.describe('MEME hit-likelihood cost', () => { test.setTimeout(180000); diff --git a/src/app.css b/src/app.css index d348aa0..c668244 100644 --- a/src/app.css +++ b/src/app.css @@ -50,3 +50,29 @@ border: revert; border-radius: revert; } + +/* --------------------------------------------------------------------------------------------- + Layout reserve for the MEME hit-likelihood row. + + These live here, in the ALWAYS-loaded stylesheet, rather than in MemeHitLikelihood.css, because + two components need them and one of them needs them BEFORE the other exists. RunOutlook draws a + pending placeholder that must be exactly as tall as the row it is standing in for: the panel sits + directly above the Run button, so if the reserve is wrong the button moves under a pointer already + travelling towards it. MemeHitLikelihood.css ships in a lazily-loaded chunk, so anything defined + there is unavailable at precisely the moment the placeholder is drawn. + + They used to be duplicated as hand-measured pixel constants in RunOutlook, with a comment + instructing the next person to re-measure by hand whenever the copy changed. The comment recorded + that the value was already 5.5px stale. Now the reserve is stated once and the placeholder is + derived from it with calc(). + --------------------------------------------------------------------------------------------- */ +:root { + --hit-body-reserve: 97.5px; + --hit-header-line: 23.5px; +} + +@media (min-width: 1024px) { + :root { + --hit-body-reserve: 80px; + } +} diff --git a/src/lib/AnalyzeTab.svelte b/src/lib/AnalyzeTab.svelte index a1fa3c2..3ea78c1 100644 --- a/src/lib/AnalyzeTab.svelte +++ b/src/lib/AnalyzeTab.svelte @@ -185,15 +185,23 @@ } const { axomemeAnalysisRunner } = await import('./services/AxomemeAnalysisRunner.js'); - const result = await axomemeAnalysisRunner.runAnalysis( - method, - config, - alignment, - tree, - $currentFile.id - ); - toastStore.success('AxoMEME prediction complete', { duration: 3000 }); - return result; + // No toast here, and no rethrow. BaseAnalysisRunner.completeAnalysis already fires one on + // success (with a View Results action) and one on failure, so doing it again stacked two + // toasts on every run — and letting the runner's rethrow reach the outer catch stacked a + // second error toast on top of that. The other two branches do not rethrow on the normal + // path, which is why only AxoMEME double-reported. + try { + return await axomemeAnalysisRunner.runAnalysis( + method, + config, + alignment, + tree, + $currentFile.id + ); + } catch (error) { + console.error('AxoMEME analysis failed:', error); + return null; + } } if (config.executionMode === 'backend') { diff --git a/src/lib/MemeHitLikelihood.css b/src/lib/MemeHitLikelihood.css index bb0d356..ea775aa 100644 --- a/src/lib/MemeHitLikelihood.css +++ b/src/lib/MemeHitLikelihood.css @@ -67,13 +67,10 @@ all four together. */ .hit-body { padding-left: 22px; /* Align with text after icon, as in AnalysisTimingEstimate */ - min-height: 97.5px; -} - -@media (min-width: 1024px) { - .hit-body { - min-height: 80px; - } + /* Defined in app.css so RunOutlook's placeholder can derive its height from the same number. + The fallback matters: this file ships in a lazily-loaded chunk and must still be correct if it + is ever rendered somewhere the token is not in scope. */ + min-height: var(--hit-body-reserve, 97.5px); } .hit-detail { diff --git a/src/lib/MemeHitLikelihood.svelte b/src/lib/MemeHitLikelihood.svelte index 903ce23..36f6296 100644 --- a/src/lib/MemeHitLikelihood.svelte +++ b/src/lib/MemeHitLikelihood.svelte @@ -187,12 +187,17 @@ } $: applicable = hasHitLikelihood(method); - $: shown = result && result.status !== STATUS.NOT_APPLICABLE ? result : null; + // `!computing` is load-bearing. `seq` stops a superseded RESPONSE overwriting a newer one, but it + // does not clear what is on screen while the new one is in flight — so selecting a second + // alignment kept rendering the first one's band, and its "about 93%" sentence, against data that + // had nothing to do with it. `computing` was assigned in three places and read in none; this is + // where it earns its keep, by letting the pending branch fire. + $: shown = result && !computing && result.status !== STATUS.NOT_APPLICABLE ? result : null; // MEME is selected but there is nothing to score yet. Split out from `shown` because it is not a // failure and must not be drawn as one — and split out from the pending state because it is not // transient. It is reachable: a rejected upload leaves the method dropdown on screen with no // alignment behind it, and this row then spun "estimating…" for as long as the page was open. - $: idle = result && result.status === STATUS.NOT_APPLICABLE ? result : null; + $: idle = result && !computing && result.status === STATUS.NOT_APPLICABLE ? result : null; $: style = shown && shown.level ? LEVEL[shown.level] : null; $: detailText = shown && shown.status !== STATUS.OK ? joinDetail(shown) : null; $: featureLine = shown && shown.num_seqs !== null ? describeFeatures(shown) : null; @@ -366,8 +371,8 @@

      How it behaves the score moves one way only: adding - sequences, adding codons, or deeper branches can raise it and never lower it. That holds - at every input the model can score, not just on average. It is still coarse — it + sequences, adding codons, or deeper branches can raise it and never lower it. That + holds at every input the model can score, not just on average. It is still coarse — it reads {LIVE_FEATURES.length} numbers off your alignment and tree and nothing about the sequences themselves — so read it as a rate over similar alignments rather than a property of yours. diff --git a/src/lib/MethodSelector.svelte b/src/lib/MethodSelector.svelte index da04953..fd54cc4 100644 --- a/src/lib/MethodSelector.svelte +++ b/src/lib/MethodSelector.svelte @@ -1157,7 +1157,9 @@ // alongside an inferred NJ tree that does have lengths, and depth is the whole point of the // estimate. The source travels with it so the estimate can disclose that NJ lengths are // nucleotide distances rather than the codon-model lengths MEME fits. - $: outlookTree = pickOutlookTree($treeStore); + // Only for a mounted RunOutlook. pickOutlookTree runs treeHasBranchLengths over the newick on every + // tree-store update, which is wasted work when the panel is suppressed. + $: outlookTree = browserOnly ? { newick: '', source: 'unknown' } : pickOutlookTree($treeStore); function pickOutlookTree(store) { const user = store?.usertree || ''; @@ -1344,7 +1346,9 @@

      Execution Mode

      Runs in your browser. There is no server-side version of this method, so there is nothing - to choose. + to choose. The first run downloads about 17 MB of model and runtime; after that scoring + takes a few seconds, and the page is briefly unresponsive while the tree embedding is + computed.

    {:else if selectedMethod && currentMethod?.info.supported} @@ -1389,20 +1393,27 @@ Server temporarily unavailable. Please use Local mode. {/if} + + {/if} - -
    - -
    + + {#if selectedMethod && currentMethod?.info.supported && !browserOnly} +
    +
    {/if} diff --git a/src/lib/RunOutlook.svelte b/src/lib/RunOutlook.svelte index b942a56..f41fa40 100644 --- a/src/lib/RunOutlook.svelte +++ b/src/lib/RunOutlook.svelte @@ -89,20 +89,17 @@ /* Rows are separated by a rule rather than by their own borders, so the panel reads as one object with two lines instead of two objects. */ .outlook-row-pending { - /* Matches the mounted row rather than guessing at it: MemeHitLikelihood's 23.5px header line - plus the body reserve it keeps for its copy, which is 97.5px below the layout's 1024px - breakpoint and 80px above it. MEASURED in the running app — the previous value was 5.5px - short of the real row, which is small but is exactly the kind of nudge that moves the Run - button out from under a pointer already travelling towards it. The panel sits above that - button, so neither the chunk arriving nor the estimate resolving a moment later may change - this height. Change these whenever MemeHitLikelihood's .hit-body reserve changes. */ - min-height: 121px; - } + /* The mounted row's height, DERIVED rather than re-measured: MemeHitLikelihood's header line + plus the body reserve it keeps for its copy. Both are declared once in app.css, which is + always loaded — this placeholder is drawn before the lazily-loaded MemeHitLikelihood chunk + exists, so it cannot read them from that file. - @media (min-width: 1024px) { - .outlook-row-pending { - min-height: 103.5px; - } + Getting this wrong is not cosmetic. The panel sits directly above the Run button, so neither + the chunk arriving nor the estimate resolving a moment later may change this height, or the + button moves out from under a pointer already travelling towards it. The previous version + hard-coded 121px / 103.5px and carried an instruction to re-measure by hand; the media query + is gone because the token already carries the breakpoint. */ + min-height: calc(var(--hit-body-reserve, 97.5px) + var(--hit-header-line, 23.5px)); } .outlook-row + .outlook-row { diff --git a/src/lib/services/AxomemeAnalysisRunner.js b/src/lib/services/AxomemeAnalysisRunner.js index 4b1014e..2dba619 100644 --- a/src/lib/services/AxomemeAnalysisRunner.js +++ b/src/lib/services/AxomemeAnalysisRunner.js @@ -44,6 +44,22 @@ const NOISY_NEGATIVE = -0.001; const yieldToBrowser = () => new Promise((resolve) => setTimeout(resolve, 0)); export class AxomemeAnalysisRunner extends BaseAnalysisRunner { + /** + * Analyses the user has cancelled. + * + * The base class cancels by walking `activeAnalyses`, which the other two runners populate with a + * backend job id or a WASM handle. This runner has neither — its work is a plain loop on the main + * thread — so without this a cancelled AxoMEME run kept scoring, kept calling updateProgress + * (re-animating a row the user had cancelled), and finally wrote `completed` over the cancelled + * record and popped a success toast. + */ + cancelledAnalyses = new Set(); + + async cancelAnalysis(analysisId) { + this.cancelledAnalyses.add(analysisId); + await super.cancelAnalysis(analysisId); + } + /** * @param {string} method ignored — this runner serves one method * @param {object} config @@ -102,18 +118,26 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { const ort = await import('onnxruntime-web/wasm'); const batch = batchSizeFor(prepared.speciesCount); + // dist_matrix, mds_coords and padding_mask are per-ALIGNMENT — batch() emits b memcpy'd + // copies of one array. Validating every batch rescanned up to the whole 64 MB budget three + // times over, on the main thread, to re-check the same N x N block b times. Validate in full + // once; after that the invariant tensors cannot have changed. + let fullyValidated = false; const accumulated = { lrt: [], alpha: [], beta_neg: [], beta_pos: [], p_neg: [] }; for (let start = 0; start < prepared.totalCodons; start += batch) { const bundle = prepared.batch(start, batch); // The contract check is cheap next to inference and catches the whole class of errors // that produce a well-formed tensor meaning something the model never saw. - const check = validateInputBundle(bundle, { - batch: bundle.msa_codons.dims[0], - numSpecies: prepared.speciesCount, - windowSize: prepared.windowSize - }); - if (!check.ok) { - throw new Error(`AxoMEME input check failed: ${check.errors.slice(0, 3).join('; ')}`); + if (!fullyValidated) { + const check = validateInputBundle(bundle, { + batch: bundle.msa_codons.dims[0], + numSpecies: prepared.speciesCount, + windowSize: prepared.windowSize + }); + if (!check.ok) { + throw new Error(`AxoMEME input check failed: ${check.errors.slice(0, 3).join('; ')}`); + } + fullyValidated = true; } const out = await runSites(session, bundle, ort); // NOT `push(...out[key])`. batchSizeFor scales as 1/N^2, so a few-taxon alignment gets @@ -125,6 +149,14 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { for (let i = 0; i < src.length; i++) accumulated[key].push(src[i]); } + // Between batches is the only place this loop yields, so it is the only place a cancel can + // take effect. Returning without calling completeAnalysis leaves the cancelled record as + // the user left it. + if (this.cancelledAnalyses.has(analysisId)) { + this.cancelledAnalyses.delete(analysisId); + return { analysisId, cancelled: true }; + } + const done = Math.min(start + batch, prepared.totalCodons); this.updateProgress( analysisId, @@ -197,9 +229,11 @@ export class AxomemeAnalysisRunner extends BaseAnalysisRunner { }; await this.completeAnalysis(analysisId, true, result); + this.cancelledAnalyses.delete(analysisId); return { analysisId, result }; } catch (error) { await this.completeAnalysis(analysisId, false, null, error.message); + this.cancelledAnalyses.delete(analysisId); throw error; } }

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