als-statistics/tests/table/extract-metric.test.js

Modular JS statistics toolkit for Node.js and the browser: descriptive stats, correlations (Pearson/Spearman/Kendall), t-tests & ANOVA (Student/Welch), reliability (Cronbach’s alpha), regression (linear/logistic), clustering (DBSCAN/HDBSCAN), and table/co

import { describe, it } from 'node:test'
import assert from 'node:assert'
import { extractMetrics } from '../../lib/table/simple-table/extract-metric.js'
import Column from '../../lib/column/index.js'
import Table from '../../lib/table/index.js';

describe('extractMetrics', () => {
   it('should extract a simple nested metric', () => {
      const col = { a: { b: { c: 42 } } };
      const result = extractMetrics({ col }, ['a.b.c'])
      assert.deepStrictEqual(result, { col: { 'a.b.c': 42 } });
   });

   it('should return undefined for a non-existent metric', () => {
      const col = { a: { b: {} } };
      assert.strictEqual(extractMetrics({ col }, ['a.b.c'])[0], undefined);
   });

   it('should handle undefined metric', () => {
      const col = { a: 42 };
      const result = extractMetrics({ col }, ['b']);
      assert.deepStrictEqual(result, { col: { b: undefined } });
   });

   it('should convert numeric arrays to Column with nested', () => {
      const col = { values: [1, 2, 3, 4] };
      const result = extractMetrics({ col }, ['values.sum']);
      assert.deepStrictEqual(result, { col: { 'values.sum': 10 } });
   });

   it('should handle empty array in extractMetrics', () => {
      const col = { values: [] };
      const result = extractMetrics({ col }, ['values']);
      assert.deepStrictEqual(result, { col: { values: [] } });
   });

   it('should handle empty array with zero length', () => {
      const col = { values: [] };
      const result = extractMetrics({ col }, ['values']);
      assert.deepStrictEqual(result, { col: { values: [] } });
   });

   it('nested descriptive', () => {
      const table = new Table({ test: [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] }, 'TestTable');
      const descriptives = table.descriptive('confidenceInterval.high', 'zScores.mean');
      assert.deepStrictEqual(descriptives, {
         test: {
            'confidenceInterval.high': 76.65700117744836,
            'zScores.mean': -1.1102230246251565e-16
         }
      })
   });

   it('should convert numeric arrays to Column', () => {
      const col = { values: [1, 2, 3, 4] };
      const result = extractMetrics({ col }, ['values']);
      // assert.ok(result.col.values instanceof Column);
      assert.deepStrictEqual(result.col.values, [1, 2, 3, 4]);
   });

   // Помощь: объектная метрика — frequencies; скаляр — mean.
   it('extractMetrics: object-only метрики → ранний return htmlResult', () => {
     const columns = {
       A: new Column([1,1,2,3,3], 'A'),
       B: new Column([0,0,0,1,2], 'B'),
     };
     const res = extractMetrics(columns, ['frequencies']); // только объектная метрика
     const html = res.htmlTable;
     assert.equal(typeof html, 'string');
     assert.ok(html.includes('for A'));
     assert.ok(html.includes('for B'));
   });
   
   it('extractMetrics: смешанный кейс (object + scalar)', () => {
     const columns = {
       A: new Column([1,2,3,4], 'A'),
       B: new Column([2,2,3,5], 'B'),
     };
     const res = extractMetrics(columns, ['frequencies', 'mean']);
     const html = res.htmlTable;
     assert.equal(typeof html, 'string');
     // должен быть блок по объектной метрике и итоговая таблица по scalar-метрикам
     assert.ok(html.toLowerCase().includes('descriptive statistics'));
   });
});