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Test Info:
"use strict";
/// <reference path="head.js" />
const {
silhouetteCoefficients,
dotProduct,
getAccuracyStats,
meanDistance,
euclideanDistanceNormalized,
euclideanDistancesSquared,
euclideanDistance,
stableCumsum,
searchSorted,
vectorMean,
vectorNormalize,
cohenKappa2x2,
computeRandScore,
initializeCentroidsSorted,
kmeansPlusPlus,
} = ChromeUtils.importESModule(
"chrome://global/content/ml/ClusterAlgos.sys.mjs"
);
add_task(function testEquals() {
ok(numberLooseEquals(1, 1.001), "Loose equals number");
ok(numberLooseEquals(1, 0.999), "Loose equals number");
ok(!numberLooseEquals(1, 1.1), "Loose equals number");
ok(!numberLooseEquals(1, 1.14), "Loose equals number");
ok(vectorLooseEquals([1, 0], [1.001, 0]), "Loose equals vector");
ok(!vectorLooseEquals([1, 0], [1.001, 0.1]), "Loose equals vector");
});
add_task(function testKappa() {
ok(
numberLooseEquals(
cohenKappa2x2({
truePositives: 10,
trueNegatives: 10,
falsePositives: 10,
falseNegatives: 10,
}),
0
),
"Kappa basic test 0"
);
ok(
numberLooseEquals(
cohenKappa2x2({
truePositives: 10,
trueNegatives: 10,
falsePositives: 0,
falseNegatives: 0,
}),
1
),
"Kappa basic test 1"
);
});
add_task(function testGetAccuracyStats() {
// Test Case 1: Perfect classification (accuracy should be 1, kappa should be 1)
let result = getAccuracyStats({
truePositives: 10,
trueNegatives: 10,
falsePositives: 0,
falseNegatives: 0,
});
ok(numberLooseEquals(result.accuracy, 1), "Perfect accuracy test");
ok(numberLooseEquals(result.kappa, 1), "Perfect kappa test");
// Test Case 2: Random classification (accuracy should be 0.5, kappa should be 0)
result = getAccuracyStats({
truePositives: 5,
trueNegatives: 5,
falsePositives: 5,
falseNegatives: 5,
});
ok(
numberLooseEquals(result.accuracy, 0.5),
"Random classification accuracy test"
);
ok(numberLooseEquals(result.kappa, 0), "Random classification kappa test");
// Test Case 3: Completely incorrect classification (accuracy should be 0, kappa should be -1)
result = getAccuracyStats({
truePositives: 0,
trueNegatives: 0,
falsePositives: 10,
falseNegatives: 10,
});
ok(
numberLooseEquals(result.accuracy, 0),
"Completely wrong classification accuracy test"
);
ok(
numberLooseEquals(result.kappa, -1),
"Completely wrong classification kappa test"
);
});
add_task(function testSilhouette() {
const silScores = silhouetteCoefficients(
[
[1.0, 1.0],
[0.0, 0.0],
[3.0, 3.0],
[0.1, 0.1],
],
[[0, 3], [1], [2]]
);
ok(
vectorLooseEquals(silScores, [0.1, 0, 0, -0.88888889]),
"Silhouette score test"
);
});
add_task(function testVectorMean() {
ok(
vectorLooseEquals(
vectorMean([
[2.0, 0.0],
[0.0, 1.0],
]),
[1.0, 0.5]
),
"Mean"
);
});
add_task(function testVectorNormalize() {
ok(
vectorLooseEquals(vectorNormalize([2.0, 0.0]), [1.0, 0.0]),
"Normalize test simple"
);
ok(
vectorLooseEquals(vectorNormalize([0.0, -1.0]), [0.0, -1.0]),
"Normalize test negative"
);
ok(
vectorLooseEquals(vectorNormalize([1.0, 1.0]), [0.707, 0.707]),
"Normalize test 45 degrees"
);
});
add_task(function testDotProduct() {
Assert.equal(dotProduct([1.0, 0.0], [0.0, 1.0]), 0.0);
Assert.equal(dotProduct([1.0, 0.0], [1.0, 1.0]), 1.0);
Assert.equal(dotProduct([1.0, 0.0], [1.0, 6.0]), 1.0);
Assert.equal(dotProduct([1.0, 0.0], [6.0, 6.0]), 6.0);
});
add_task(async function testEuclideanDistanceNormalized() {
const dist = euclideanDistanceNormalized([1.0, 0.0], [0.0, 1.0]);
ok(numberLooseEquals(dist, 1.414), "Euclidean distance normalized");
});
add_task(async function testEuclideanDistance() {
const dist = euclideanDistance([1.0, 0.0], [0.0, 1.0]);
ok(numberLooseEquals(dist, 1.414), "Euclidean distance");
const dist2 = euclideanDistance([1.0, 0.0], [5.0, 0.0]);
ok(numberLooseEquals(dist2, 4), "Euclidean distance");
const dist3 = euclideanDistance([1.0, 0.0], [5.0, 0.0], true);
ok(numberLooseEquals(dist3, 16), "Euclidean distance - squared");
});
add_task(async function testEuclideanDistancesSquared() {
const dist = euclideanDistancesSquared(
[0.0, 1.0],
[
[0.0, 1.0],
[0, 0],
[0, 5],
[5, 1],
]
);
ok(vectorLooseEquals(dist, [0, 1, 16, 25]), "Euclidean distance");
});
add_task(async function testMeansDistance() {
const selfPoint = [3.0];
const d = meanDistance(
selfPoint,
[[0.0], [1.0], [2.0], selfPoint, [4.0]],
false
);
const d2 = meanDistance(
selfPoint,
[[0.0], [1.0], [2.0], selfPoint, [4.0]],
true
);
const d3 = meanDistance(selfPoint, [[3.0], [3.0]], false);
Assert.equal(d, (3 + 2 + 1 + 1) / 5.0, "Mean distance");
Assert.equal(d2, (3 + 2 + 1 + 1) / 4.0, "Mean distance exclude self");
Assert.equal(d3, 0, "Mean distance - zero");
});
add_task(async function testInitializeCentroidsSorted() {
const centroids = initializeCentroidsSorted({
X: [
[1, 0, 0],
[1, 0, 0],
[0, 1, 1],
[0, 1, 1],
],
randomFunc: simpleNumberSequence(),
k: 2,
});
Assert.equal(centroids.length, 2);
});
add_task(async function testKMeansPlusPlusAllClusters() {
const results = kmeansPlusPlus({
data: [
[1, 0, 0],
[1, 0, 0],
[0, 1, 1],
[0, 1, 1],
],
k: 2,
maxIterations: 2,
randomFunc: simpleNumberSequence(),
});
Assert.equal(results.length, 2);
});
add_task(async function testKMeansPlusPlusPinnedItem() {
const results = kmeansPlusPlus({
data: [
[1, 0, 0],
[1, 0, 0],
[0, 1, 1],
[0, 1, 1],
],
k: 2,
maxIterations: 2,
randomFunc: simpleNumberSequence(),
anchorIndices: [0],
});
results[0].includes(0);
results[0].includes(1);
results[1].includes(2);
results[1].includes(3);
Assert.equal(results.length, 2);
});
add_task(async function testKMeansPlusPlusPinnedItems() {
const results = kmeansPlusPlus({
data: [
[1, 0, 0],
[1, 0, 0],
[0, 1, 1],
[0, 1, 1],
],
k: 2,
maxIterations: 2,
randomFunc: simpleNumberSequence(),
anchorIndices: [0, 1],
});
results[0].includes(0);
results[0].includes(1);
results[1].includes(2);
results[1].includes(3);
Assert.equal(results.length, 2);
});
add_task(async function testSearchSorted() {
Assert.equal(searchSorted([1.1, 2.1, 3.1], 2), 1, "Sorted search");
Assert.equal(searchSorted([1.1, 2.1, 3.1], 2.2), 2, "Sorted search"); /// TODO - investigate
Assert.equal(
searchSorted([1.1, 2.1, 3.1], 2.1),
1,
"Sorted search exact match"
);
Assert.equal(searchSorted([1.1, 2.1, 3.1], 0), 0, "Sorted search underflow");
Assert.equal(searchSorted([1.1, 2.1, 3.1], 5), 3, "Sorted search overflow"); // TODO - make sure edge case is handled by users
});
add_task(async function testStableCumsum() {
ok(
vectorLooseEquals(stableCumsum([1, 2, 3]), [1, 3, 6]),
"Stable cumulative sum basic"
);
Assert.strictEqual(stableCumsum([]).length, 0, "Stable cumulative sum empty");
});
add_task(async function testComputeRandScore() {
Assert.strictEqual(
computeRandScore(
[
{ A: 1, B: 1 },
{ A: 2, B: 2 },
],
"A",
"B"
),
1,
"computeRandScore basic all match"
);
Assert.strictEqual(stableCumsum([]).length, 0, "Stable cumulative sum empty");
});