mirror of
https://github.com/MBombeck/HealthLog.git
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Marc-André Bombeck
760f261a5f
The ratchet on tests that read a source file by path matched readFileSync and readFile only, so a guard that called readSourceText with a fixed path, directly or through a local read(rel) wrapper, read the file without the anchors readSource demands and was never counted. The classifier now counts both shapes, and leaves alone a readSource call with anchors and a sweep that feeds the reader from a walk. The twelve guards that read a fixed file that way now go through readSource with an anchor each read asserts on, so the baseline stays where it was.
107 lines
4.0 KiB
TypeScript
107 lines
4.0 KiB
TypeScript
/**
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* Structural guard: a standard deviation is computed in `lib/stats/spread`,
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* with its kind named at the call site.
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*
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* About a dozen hand-rolled spreads each picked population (÷ n) or sample
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* (÷ n − 1) without saying so, and the two surfaces a person compares picked
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* differently: the series API reported the population SD of a blood-pressure
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* series, the MCP and AI payload the sample SD of the same readings, about
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* 22 % apart at three readings. The reading spread both report is now
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* `READING_SPREAD`.
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*
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* The matcher flags a squared deviation from a mean in a file that also takes
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* a square root. The files below compute one for a test statistic or a fit,
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* never as a reported spread, and say so. The doctor report's glucose panel
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* composes its SD from per-bucket sums of squares (sample, the same rule as
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* `glucoseSD`) and is out of the matcher's reach by shape. Comments are
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* stripped first; the planted case proves the matcher fires.
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*/
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import { join } from "node:path";
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import { describe, expect, it } from "vitest";
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import {
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readSource,
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readSourceText,
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stripComments,
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walkSourceFiles,
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} from "./helpers/source-files";
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const SRC = join(process.cwd(), "src");
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const ALLOWED: Record<string, string> = {
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"lib/stats/spread.ts": "The implementation.",
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"lib/insights/correlations.ts":
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"The correlation engine's centred sums and the Welch t-test variances.",
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"lib/insights/mood-tag-influence.ts": "Welch t-test variances.",
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"lib/insights/mood-pattern-cards.ts": "A pooled variance for a t statistic.",
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"lib/stats/segmented-regression.ts":
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"The total sum of squares behind R² and the HAC standard errors of a segmented fit; neither is reported as a spread.",
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"lib/insights/derived/trajectory.ts":
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"The residual standard error of a regression fit (÷ n − 2).",
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"lib/insights/derived/sleep-score.ts":
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"A circular SD of the sleep midpoint around its circular mean, the input of the consistency score; `spread` has no circular distance, and the score is never shown as a spread.",
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};
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/**
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* A squared deviation: `(x - m) ** 2`, `(x - m) * (x - m)`, or a two-argument
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* distance squared, `dist(x, m) ** 2` (a circular or otherwise custom
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* deviation is still a hand-rolled spread).
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*/
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const SQUARED_DEVIATION =
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/\(\s*[\w.[\]]+\s*-\s*[\w.[\]]+\s*\)\s*\*\*\s*2|\(\s*([\w.[\]]+)\s*-\s*([\w.[\]]+)\s*\)\s*\*\s*\(\s*\1\s*-\s*\2\s*\)|\b[A-Za-z_]\w*\s*\(\s*[\w.[\]]+\s*,\s*[\w.[\]]+\s*\)\s*\*\*\s*2/;
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function computesSpread(code: string): boolean {
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return SQUARED_DEVIATION.test(code) && code.includes("Math.sqrt(");
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}
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function read(file: string): string {
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return stripComments(readSourceText(join(SRC, file)));
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}
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describe("one standard deviation", () => {
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it("the matcher fires on a hand-rolled spread", () => {
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expect(
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computesSpread(
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"const v = xs.reduce((s, x) => s + (x - m) ** 2, 0) / n; Math.sqrt(v);",
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),
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).toBe(true);
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expect(
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computesSpread("acc += (v - mean) * (v - mean); Math.sqrt(acc / n);"),
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).toBe(true);
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expect(
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computesSpread(
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"xs.reduce((s, v) => s + circularMinuteDistance(v, mean) ** 2, 0); Math.sqrt(x);",
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),
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).toBe(true);
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});
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it("no file outside the list computes its own", () => {
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const offenders = walkSourceFiles(SRC, { floor: 3000 })
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.filter(
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(file) =>
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!file.startsWith("generated/") &&
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!file.includes("__tests__/") &&
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!/\.test\.tsx?$/.test(file),
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)
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.filter((file) => computesSpread(read(file)) && !(file in ALLOWED));
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expect(offenders).toEqual([]);
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});
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it("the series API and the AI and MCP payload report the same spread", () => {
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expect(
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stripComments(
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readSource(
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"src/app/api/measurements/series/route.ts",
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"READING_SPREAD",
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),
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),
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).toMatch(/standardDeviation\(\s*values,\s*READING_SPREAD\s*\)/);
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const features = stripComments(
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readSource("src/lib/insights/features.ts", "READING_SPREAD"),
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);
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expect(
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features.match(/roundedSpread\(\s*vals,\s*READING_SPREAD\s*\)/g),
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).toHaveLength(2);
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});
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});
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