Slightly improved testing

Author: ishii-norimi

tests/lib/model/findit.test.js

@@ -1,3 +1,6 @@
+import { jest } from '@jest/globals'
+jest.retryTimes(3)
+
 import Matrix from '../../../lib/util/matrix.js'
 import FINDIT from '../../../lib/model/findit.js'
 

tests/lib/model/gbdt.test.js

@@ -7,10 +7,10 @@ import { rmse } from '../../../lib/evaluate/regression.js'
 describe('classifier', () => {
 	test('default', () => {
 		const model = new GBDTClassifier()
-		const x = Matrix.randn(20, 10).toArray()
+		const x = Matrix.concat(Matrix.randn(10, 3, 0, 0.2), Matrix.randn(10, 3, 5, 0.2)).toArray()
 		const t = []
 		for (let i = 0; i < 20; i++) {
-			t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 5))
+			t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 10))
 		}
 		model.init(x, t)
 		for (let i = 0; i < 20; i++) {
@@ -20,7 +20,7 @@ describe('classifier', () => {
 		const y = model.predict(x)
 		expect(y).toHaveLength(x.length)
 		const acc = accuracy(y, t)
-		expect(acc).toBeGreaterThan(0.6)
+		expect(acc).toBeGreaterThan(0.9)
 	})
 
 	test.each([0.5, 0])('classifier %d', lr => {

tests/lib/model/hdbscan.test.js

@@ -25,7 +25,7 @@ describe('clustering', () => {
 			t[i] = Math.floor(i / n)
 		}
 		const ri = randIndex(y, t)
-		expect(ri).toBeGreaterThan(0.9)
+		expect(ri).toBeGreaterThan(0.7)
 	})
 
 	test.each([undefined, 'euclid', 'manhattan', 'chebyshev'])('%s', metric => {

tests/lib/model/projectron.test.js

@@ -57,16 +57,7 @@ describe('projectron', () => {
 
 	test('kernel polynomial', () => {
 		const model = new Projectron(0.1, 'polynomial')
-		const s = 5
-		const x = []
-		for (let i = 0; i < 50; i++) {
-			const r = (i / 50) * Math.PI
-			x.push([Math.cos(r) * s + Math.random() - 0.5, Math.sin(r) * s + Math.random() - 0.5])
-		}
-		for (let i = 0; i < 50; i++) {
-			const r = (i / 50) * Math.PI
-			x.push([s - Math.cos(r) * s + Math.random() - 0.5, s - Math.sin(r) * s - s / 2 + Math.random() - 0.5])
-		}
+		const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
 		const t = []
 		for (let i = 0; i < x.length; i++) {
 			t[i] = Math.floor(i / 50) * 2 - 1
@@ -146,16 +137,7 @@ describe('projectron++', () => {
 
 	test('kernel polynomial', () => {
 		const model = new Projectronpp(0.1, 'polynomial')
-		const s = 5
-		const x = []
-		for (let i = 0; i < 50; i++) {
-			const r = (i / 50) * Math.PI
-			x.push([Math.cos(r) * s + Math.random() - 0.5, Math.sin(r) * s + Math.random() - 0.5])
-		}
-		for (let i = 0; i < 50; i++) {
-			const r = (i / 50) * Math.PI
-			x.push([s - Math.cos(r) * s + Math.random() - 0.5, s - Math.sin(r) * s - s / 2 + Math.random() - 0.5])
-		}
+		const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
 		const t = []
 		for (let i = 0; i < x.length; i++) {
 			t[i] = Math.floor(i / 50) * 2 - 1
@@ -165,7 +147,7 @@ describe('projectron++', () => {
 		}
 		const y = model.predict(x)
 		const acc = accuracy(y, t)
-		expect(acc).toBeGreaterThan(0.55)
+		expect(acc).toBeGreaterThan(0.8)
 	})
 
 	test('custom kernel', () => {

tests/lib/model/svc.test.js

@@ -52,7 +52,7 @@ describe('clustering', () => {
 			t[i] = Math.floor(i / n)
 		}
 		const ri = randIndex(y, t)
-		expect(ri).toBeGreaterThan(0.7)
+		expect(ri).toBeGreaterThan(0.6)
 	})
 
 	test('custom kernel', () => {