Add Multiclass Ridge classifier (#697)

* Add Multiclass Ridge classifier

* Fix test

* Fix least square

README.md

@@ -122,7 +122,7 @@ for (let i = 0; i < n; i++) {
 | task | model |
 | ---- | ----- |
 | clustering | (Soft / Kernel / Genetic / Weighted) k-means, k-means++, k-medois, k-medians, x-means, G-means, LBG, ISODATA, Fuzzy c-means, Possibilistic c-means, Agglomerative (complete linkage, single linkage, group average, Ward's, centroid, weighted average, median), DIANA, Monothetic, Mutual kNN, Mean shift, DBSCAN, OPTICS, HDBSCAN, DENCLUE, DBCLASD, CLUES, PAM, CLARA, CLARANS, BIRCH, CURE, ROCK, C2P, PLSA, Latent dirichlet allocation, GMM, VBGMM, Affinity propagation, Spectral clustering, Mountain, (Growing) SOM, GTM, (Growing) Neural gas, Growing cell structures, LVQ, ART, SVC, CAST, CHAMELEON, COLL, CLIQUE, PROCLUS, ORCLUS, FINDIT, NMF, Autoencoder |
-| classification | (Fisher's) Linear discriminant, Quadratic discriminant, Mixture discriminant, Least squares, Ridge, (Complement / Negation / Universal-set / Selective) Naive Bayes (gaussian), AODE, (Fuzzy / Weighted) k-nearest neighbor, Radius neighbor, Nearest centroid, ENN, ENaN, NNBCA, ADAMENN, DANN, IKNN, Decision tree, Random forest, Extra trees, GBDT, XGBoost, ALMA, (Aggressive) ROMMA, (Bounded) Online gradient descent, (Budgeted online) Passive aggressive, RLS, (Selective-sampling) Second order perceptron, AROW, NAROW, Confidence weighted, CELLIP, IELLIP, Normal herd, Stoptron, (Kernelized) Pegasos, MIRA, Forgetron, Projectron, Projectron++, Banditron, Ballseptron, (Multiclass) BSGD, ILK, SILK, (Multinomial) Logistic regression, (Multinomial) Probit, SVM, Gaussian process, HMM, CRF, Bayesian Network, LVQ, (Average / Multiclass / Voted / Kernelized / Selective-sampling / Margin / Shifting / Budget / Tighter / Tightest) Perceptron, PAUM, RBP, ADALINE, MADALINE, MLP, LMNN |
+| classification | (Fisher's) Linear discriminant, Quadratic discriminant, Mixture discriminant, Least squares, (Multiclass / Kernel) Ridge, (Complement / Negation / Universal-set / Selective) Naive Bayes (gaussian), AODE, (Fuzzy / Weighted) k-nearest neighbor, Radius neighbor, Nearest centroid, ENN, ENaN, NNBCA, ADAMENN, DANN, IKNN, Decision tree, Random forest, Extra trees, GBDT, XGBoost, ALMA, (Aggressive) ROMMA, (Bounded) Online gradient descent, (Budgeted online) Passive aggressive, RLS, (Selective-sampling) Second order perceptron, AROW, NAROW, Confidence weighted, CELLIP, IELLIP, Normal herd, Stoptron, (Kernelized) Pegasos, MIRA, Forgetron, Projectron, Projectron++, Banditron, Ballseptron, (Multiclass) BSGD, ILK, SILK, (Multinomial) Logistic regression, (Multinomial) Probit, SVM, Gaussian process, HMM, CRF, Bayesian Network, LVQ, (Average / Multiclass / Voted / Kernelized / Selective-sampling / Margin / Shifting / Budget / Tighter / Tightest) Perceptron, PAUM, RBP, ADALINE, MADALINE, MLP, LMNN |
 | semi-supervised classification | k-nearest neighbor, Radius neighbor, Label propagation, Label spreading, k-means, GMM, S3VM, Ladder network |
 | regression | Least squares, Ridge, Lasso, Elastic net, RLS, Bayesian linear, Poisson, Least absolute deviations, Huber, Tukey, Least trimmed squares, Least median squares, Lp norm linear, SMA, Deming, Segmented, LOWESS, LOESS, spline, Gaussian process, Principal components, Partial least squares, Projection pursuit, Quantile regression, k-nearest neighbor, Radius neighbor, IDW, Nadaraya Watson, Priestley Chao, Gasser Muller, RBF Network, RVM, Decision tree, Random forest, Extra trees, GBDT, XGBoost, SVR, MLP, GMR, Isotonic, Ramer Douglas Peucker, Theil-Sen, Passing-Bablok, Repeated median |
 | interpolation | Nearest neighbor, IDW, (Spherical) Linear, Brahmagupta, Logarithmic, Cosine, (Inverse) Smoothstep, Cubic, (Centripetal) Catmull-Rom, Hermit, Polynomial, Lagrange, Trigonometric, Spline, RBF Network, Akima, Natural neighbor, Delaunay |

js/view/least_square.js

@@ -3,6 +3,7 @@ import Matrix from '../../lib/util/matrix.js'
 import LeastSquares from '../../lib/model/least_square.js'
 import stringToFunction from '../expression.js'
 import EnsembleBinaryModel from '../../lib/model/ensemble_binary.js'
+import Controller from '../controller.js'
 
 const combination_repetition = (n, k) => {
 	const c = []
@@ -56,6 +57,7 @@ export class BasisFunctions {
 	}
 
 	makeHtml(r) {
+		r = d3.select(r)
 		if (!this._e) {
 			this._e = r.append('div').attr('id', `ls_model_${this._name}`)
 		} else {
@@ -160,47 +162,12 @@ export class BasisFunctions {
 	}
 }
 
-var dispLeastSquares = function (elm, platform) {
+export default function (platform) {
+	platform.setting.ml.usage = 'Click and add data point. Next, click "Fit" button.'
 	platform.setting.ml.reference = {
 		title: 'Least squares (Wikipedia)',
 		url: 'https://en.wikipedia.org/wiki/Least_squares',
 	}
-	const fitModel = () => {
-		let model
-		if (platform.task === 'CF') {
-			const method = elm.select('[name=method]').property('value')
-			model = new EnsembleBinaryModel(LeastSquares, method)
-		} else {
-			model = new LeastSquares()
-		}
-		model.fit(basisFunctions.apply(platform.trainInput).toArray(), platform.trainOutput)
-
-		let pred = model.predict(basisFunctions.apply(platform.testInput(2)).toArray())
-		platform.testResult(pred)
-	}
-
-	if (platform.task === 'CF') {
-		elm.append('select')
-			.attr('name', 'method')
-			.selectAll('option')
-			.data(['oneone', 'onerest'])
-			.enter()
-			.append('option')
-			.property('value', d => d)
-			.text(d => d)
-	}
-	const basisFunctions = new BasisFunctions(platform)
-	basisFunctions.makeHtml(elm)
-
-	elm.append('input')
-		.attr('type', 'button')
-		.attr('value', 'Fit')
-		.on('click', () => fitModel())
-}
-
-export default function (platform) {
-	platform.setting.ml.usage = 'Click and add data point. Next, click "Fit" button.'
-	dispLeastSquares(platform.setting.ml.configElement, platform)
 	platform.setting.ml.detail = `
 The model form is
 $$
@@ -218,4 +185,26 @@ $$
 $$
 where $ G_{ij} = g_i(x_j) $.
 `
+	const controller = new Controller(platform)
+	const fitModel = () => {
+		let model
+		if (platform.task === 'CF') {
+			model = new EnsembleBinaryModel(LeastSquares, method.value)
+		} else {
+			model = new LeastSquares()
+		}
+		model.fit(basisFunctions.apply(platform.trainInput).toArray(), platform.trainOutput)
+
+		let pred = model.predict(basisFunctions.apply(platform.testInput(2)).toArray())
+		platform.testResult(pred)
+	}
+
+	let method = null
+	if (platform.task === 'CF') {
+		method = controller.select(['oneone', 'onerest'])
+	}
+	const basisFunctions = new BasisFunctions(platform)
+	basisFunctions.makeHtml(controller.element)
+
+	controller.input.button('Fit').on('click', () => fitModel())
 }

js/view/ridge.js

@@ -1,31 +1,52 @@
 import Matrix from '../../lib/util/matrix.js'
 
 import { BasisFunctions } from './least_square.js'
-import { Ridge, KernelRidge } from '../../lib/model/ridge.js'
+import { Ridge, MulticlassRidge, KernelRidge } from '../../lib/model/ridge.js'
 import EnsembleBinaryModel from '../../lib/model/ensemble_binary.js'
+import Controller from '../controller.js'
 
-var dispRidge = function (elm, platform) {
+export default function (platform) {
+	platform.setting.ml.usage = 'Click and add data point. Next, click "Fit" button.'
 	platform.setting.ml.reference = {
 		title: 'Ridge regression (Wikipedia)',
 		url: 'https://en.wikipedia.org/wiki/Ridge_regression',
 	}
+	platform.setting.ml.detail = `
+The model form is
+$$
+f(X) = X W + \\epsilon
+$$
+
+The loss function can be written as
+$$
+L(W) = \\| X W - y \\|^2 + \\lambda \\| W \\|^2
+$$
+where $ y $ is the observed value corresponding to $ X $.
+Therefore, the optimum parameter $ \\hat{W} $ is estimated as
+$$
+\\hat{W} = \\left( X^T X + \\lambda I \\right)^{-1} X^T y
+$$
+`
+	const controller = new Controller(platform)
 	const task = platform.task
-	const fitModel = cb => {
+	const fitModel = () => {
 		const dim = platform.datas.dimension
-		const kernel = elm.select('[name=kernel]').property('value')
-		const kernelName = kernel === 'no kernel' ? null : kernel
+		const kernelName = kernel.value === 'no kernel' ? null : kernel.value
 		let model
-		const l = +elm.select('[name=lambda]').property('value')
+		const l = +lambda.value
 		if (task === 'CF') {
-			const method = elm.select('[name=method]').property('value')
 			if (kernelName) {
 				model = new EnsembleBinaryModel(function () {
 					return new KernelRidge(l, kernelName)
-				}, method)
+				}, method.value)
 			} else {
-				model = new EnsembleBinaryModel(function () {
-					return new Ridge(l)
-				}, method)
+				if (method.value === 'multiclass') {
+					model = new MulticlassRidge(l)
+				} else {
+					model = new EnsembleBinaryModel(function () {
+						return new Ridge(l)
+					}, method.value)
+				}
 			}
 		} else {
 			if (kernelName) {
@@ -53,61 +74,23 @@ var dispRidge = function (elm, platform) {
 	}
 
 	const basisFunction = new BasisFunctions(platform)
+	let method = null
 	if (task === 'CF') {
-		elm.append('select')
-			.attr('name', 'method')
-			.selectAll('option')
-			.data(['oneone', 'onerest'])
-			.enter()
-			.append('option')
-			.property('value', d => d)
-			.text(d => d)
+		method = controller.select(['oneone', 'onerest', 'multiclass']).on('change', () => {
+			if (method.value === 'multiclass') {
+				kernel.element.style.display = 'none'
+			} else {
+				kernel.element.style.display = null
+			}
+		})
 	}
+	let kernel = null
 	if (task !== 'FS') {
-		basisFunction.makeHtml(elm)
-		elm.append('select')
-			.attr('name', 'kernel')
-			.selectAll('option')
-			.data(['no kernel', 'gaussian'])
-			.enter()
-			.append('option')
-			.property('value', d => d)
-			.text(d => d)
+		basisFunction.makeHtml(controller.element)
+		kernel = controller.select(['no kernel', 'gaussian'])
 	} else {
-		elm.append('input').attr('type', 'hidden').attr('name', 'kernel').property('value', '')
+		kernel = controller.input({ type: 'hidden', value: '' })
 	}
-	elm.append('span').text('lambda = ')
-	elm.append('select')
-		.attr('name', 'lambda')
-		.selectAll('option')
-		.data([0, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100])
-		.enter()
-		.append('option')
-		.property('value', d => d)
-		.text(d => d)
-	elm.append('input')
-		.attr('type', 'button')
-		.attr('value', 'Fit')
-		.on('click', () => fitModel())
-}
-
-export default function (platform) {
-	platform.setting.ml.usage = 'Click and add data point. Next, click "Fit" button.'
-	dispRidge(platform.setting.ml.configElement, platform)
-	platform.setting.ml.detail = `
-The model form is
-$$
-f(X) = X W + \\epsilon
-$$
-
-The loss function can be written as
-$$
-L(W) = \\| X W - y \\|^2 + \\lambda \\| W \\|^2
-$$
-where $ y $ is the observed value corresponding to $ X $.
-Therefore, the optimum parameter $ \\hat{W} $ is estimated as
-$$
-\\hat{W} = \\left( X^T X + \\lambda I \\right)^{-1} X^T y
-$$
-`
+	const lambda = controller.select({ label: 'lambda = ', values: [0, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100] })
+	controller.input.button('Fit').on('click', () => fitModel())
 }

lib/model/ridge.js

@@ -50,6 +50,73 @@ export class Ridge {
 	}
 }
 
+/**
+ * Multiclass ridge regressioin
+ */
+export class MulticlassRidge {
+	/**
+	 * @param {number} [lambda=0.1] Regularization strength
+	 */
+	constructor(lambda = 0.1) {
+		this._w = null
+		this._lambda = lambda
+		this._classes = []
+	}
+
+	/**
+	 * Category list
+	 *
+	 * @type {*[]}
+	 */
+	get categories() {
+		return this._classes
+	}
+
+	/**
+	 * Fit model.
+	 *
+	 * @param {Array<Array<number>>} x Training data
+	 * @param {*[]} y Target values
+	 */
+	fit(x, y) {
+		x = Matrix.fromArray(x)
+		this._classes = [...new Set(y)]
+		const p = new Matrix(y.length, this._classes.length, -1)
+		for (let i = 0; i < y.length; i++) {
+			p.set(i, this._classes.indexOf(y[i]), 1)
+		}
+		const xtx = x.tDot(x)
+		for (let i = 0; i < xtx.rows; i++) {
+			xtx.addAt(i, i, this._lambda)
+		}
+
+		this._w = xtx.solve(x.t).dot(p)
+	}
+
+	/**
+	 * Returns predicted values.
+	 *
+	 * @param {Array<Array<number>>} x Sample data
+	 * @returns {*[]} Predicted values
+	 */
+	predict(x) {
+		x = Matrix.fromArray(x)
+		return x
+			.dot(this._w)
+			.argmax(1)
+			.value.map(i => this._classes[i])
+	}
+
+	/**
+	 * Returns importances of the features.
+	 *
+	 * @returns {number[]} Importances
+	 */
+	importance() {
+		return this._w.toArray()
+	}
+}
+
 /**
  * Kernel ridge regression
  */

tests/gui/view/least_square.test.js

@@ -19,7 +19,7 @@ describe('classification', () => {
 		const methodMenu = await page.waitForSelector('#ml_selector #method_menu')
 		const buttons = await methodMenu.waitForSelector('.buttons')
 
-		const methods = await buttons.waitForSelector('[name=method]')
+		const methods = await buttons.waitForSelector('select:nth-of-type(1)')
 		await expect((await methods.getProperty('value')).jsonValue()).resolves.toBe('oneone')
 		const preset = await buttons.waitForSelector('[name=preset]')
 		await expect((await preset.getProperty('value')).jsonValue()).resolves.toBe('linear')

tests/gui/view/ridge.test.js

@@ -19,13 +19,13 @@ describe('classification', () => {
 		const methodMenu = await page.waitForSelector('#ml_selector #method_menu')
 		const buttons = await methodMenu.waitForSelector('.buttons')
 
-		const methods = await buttons.waitForSelector('[name=method]')
+		const methods = await buttons.waitForSelector('select:nth-of-type(1)')
 		await expect((await methods.getProperty('value')).jsonValue()).resolves.toBe('oneone')
 		const preset = await buttons.waitForSelector('[name=preset]')
 		await expect((await preset.getProperty('value')).jsonValue()).resolves.toBe('linear')
-		const kernel = await buttons.waitForSelector('[name=kernel]')
+		const kernel = await buttons.waitForSelector('select:nth-of-type(2)')
 		await expect((await kernel.getProperty('value')).jsonValue()).resolves.toBe('no kernel')
-		const lambda = await buttons.waitForSelector('[name=lambda]')
+		const lambda = await buttons.waitForSelector('select:nth-of-type(3)')
 		await expect((await lambda.getProperty('value')).jsonValue()).resolves.toBe('0')
 	}, 10000)
 

tests/lib/model/ridge.test.js

@@ -2,9 +2,10 @@ import { jest } from '@jest/globals'
 jest.retryTimes(3)
 
 import Matrix from '../../../lib/util/matrix.js'
-import { Ridge, KernelRidge } from '../../../lib/model/ridge.js'
+import { Ridge, MulticlassRidge, KernelRidge } from '../../../lib/model/ridge.js'
 
 import { rmse } from '../../../lib/evaluate/regression.js'
+import { accuracy } from '../../../lib/evaluate/classification.js'
 
 describe('ridge', () => {
 	test('default', () => {
@@ -40,6 +41,41 @@ describe('ridge', () => {
 	})
 })
 
+describe('multiclass ridge', () => {
+	test('default', () => {
+		const model = new MulticlassRidge()
+		expect(model._lambda).toBe(0.1)
+	})
+
+	test('fit', () => {
+		const model = new MulticlassRidge(0.001)
+		const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, [0, 5], 0.2)).toArray()
+		const t = []
+		for (let i = 0; i < x.length; i++) {
+			t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
+		}
+		model.fit(x, t)
+		const y = model.predict(x)
+		const acc = accuracy(y, t)
+		expect(acc).toBeGreaterThan(0.75)
+	})
+
+	test('importance', () => {
+		const model = new MulticlassRidge(0.01)
+		const x = Matrix.concat(Matrix.randn(50, 3, 0, 0.2), Matrix.randn(50, 3, 5, 0.2)).toArray()
+		const t = []
+		for (let i = 0; i < x.length; i++) {
+			t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
+		}
+		model.fit(x, t)
+		const importance = model.importance()
+		expect(importance).toHaveLength(3)
+		expect(importance[0]).toHaveLength(2)
+		expect(importance[1]).toHaveLength(2)
+		expect(importance[2]).toHaveLength(2)
+	})
+})
+
 describe('kernel ridge', () => {
 	test('default', () => {
 		const model = new KernelRidge()