78 lines
3.4 KiB
TypeScript
78 lines
3.4 KiB
TypeScript
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/**
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* Training-health panel (simplification-plan §6.5e / ALIGNMENT defect 6).
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*
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* The point of the panel is that "is the network learning?" becomes GENUINELY
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* answerable, so the test asserts two things a placeholder could not satisfy:
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*
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* 1. Before any training it says so plainly — no plot, no numbers.
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* 2. After a real fit it reports the iteration count and the endpoints of the
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* core's own loss curve, and draws a polyline with one vertex per
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* iteration.
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*
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* It also pins the disclosure rule: the panel is advanced surface, so it lives
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* at the Learning drawer's `expanded` depth (Manifold's existing DrawerDepth
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* mechanism) and must NOT appear in the condensed panel.
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*/
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import { test, expect } from '@playwright/test';
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import { loadProbe } from './helpers';
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import { PafSynthSchema } from '../../src/modes/generated';
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const N_OUTPUTS = PafSynthSchema.ml.output_size;
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const LOW = { input: [0.1, 0.9], output: new Array(N_OUTPUTS).fill(0.1) };
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const HIGH = { input: [0.9, 0.1], output: new Array(N_OUTPUTS).fill(0.9) };
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/** Open the Learning drawer and expand it to the advanced depth. */
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async function openLearningExpanded(page: import('@playwright/test').Page) {
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await page.getByTitle('Learning', { exact: true }).click();
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await page.getByTitle('Expand', { exact: true }).click();
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}
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test.beforeEach(async ({ page }) => {
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await loadProbe(page);
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});
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test('training health is advanced surface — absent from the condensed drawer', async ({ page }) => {
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await page.getByTitle('Learning', { exact: true }).click();
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await expect(page.getByText('Training health')).toHaveCount(0);
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});
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test('with no training run the panel says so instead of drawing a curve', async ({ page }) => {
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await openLearningExpanded(page);
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await expect(page.getByText('Training health')).toBeVisible();
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await expect(page.getByText(/no training run yet/)).toBeVisible();
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await expect(page.locator('svg polyline')).toHaveCount(0);
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});
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test('after a real fit the panel reports the core loss curve', async ({ page }) => {
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const hist = await page.evaluate(
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([low, high]) => {
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window.__nisps!.addExample(low.input, low.output);
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window.__nisps!.addExample(high.input, high.output);
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window.__nisps!.train();
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return Array.from(window.__nisps!.getLossHistory());
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},
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[LOW, HIGH],
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);
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expect(hist.length).toBeGreaterThan(1);
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await openLearningExpanded(page);
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await expect(page.getByText(/no training run yet/)).toHaveCount(0);
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await expect(page.getByText(`${hist.length} iter`)).toBeVisible();
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await expect(page.getByText(`start ${hist[0]!.toFixed(4)}`)).toBeVisible();
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await expect(page.getByText(`end ${hist[hist.length - 1]!.toFixed(4)}`)).toBeVisible();
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// One polyline vertex per recorded iteration — the plot is the data, not decor.
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const points = await page.locator('svg polyline').first().getAttribute('points');
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expect(points!.trim().split(/\s+/)).toHaveLength(hist.length);
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});
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test('layer stats show one row per layer with real weight-health numbers', async ({ page }) => {
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const layers = await page.evaluate(() => window.__nisps!.describe().numLayers);
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await openLearningExpanded(page);
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const rows = page.locator('table tbody tr');
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await expect(rows).toHaveCount(layers);
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// mean|w| of a freshly-drawn net is non-zero — the table is reading the net.
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const meanAbs = await rows.first().locator('td').nth(1).innerText();
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expect(Number(meanAbs)).toBeGreaterThan(0);
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});
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