@ai-on-browser/data-analysis-models/lib/model/ensemble_binary.js

Data analysis model package without any dependencies

/**
 * @typedef {object} BinaryModel
 * @property {function(Array<Array<number>>, *[]): void} init Initialize model
 * @property {function(...*): void} fit Fit model
 * @property {function(Array<Array<number>>): number[]} predict Returns predicted values
 */
/**
 * Ensemble binary models
 */
export default class EnsembleBinaryModel {
	/**
	 * @param {() => BinaryModel} model Function to generate the model
	 * @param {'oneone' | 'onerest'} type Type name
	 * @param {*[]} [classes] Initial class labels
	 */
	constructor(model, type, classes) {
		if (type === 'oneone') {
			return new OneVsOneModel(model, classes)
		} else if (type === 'onerest') {
			return new OneVsRestModel(model, classes)
		}
		return null
	}

	/**
	 * Initialize model.
	 * @param {Array<Array<number>>} train_x Training data
	 * @param {*[]} train_y Target values
	 */
	init(train_x, train_y) {}

	/**
	 * Fit model.
	 * @param {Array<Array<number>>} x Training data
	 * @param {*[]} y Target values
	 * @param {...*} args Arguments for fit
	 */
	fit(x, y, ...args) {}

	/**
	 * Returns predicted values.
	 * @param {Array<Array<number>>} data Sample data
	 * @returns {*[]} Predicted values
	 */
	predict(data) {}
}

class OneVsRestModel {
	// one vs rest
	constructor(model, classes) {
		if (classes && !Array.isArray(classes)) {
			classes = [...classes]
		}
		this._modelcls = model
		this._classes = classes
		this._model = []
		this._n = 0
		if (classes) {
			this._n = classes.length
			for (let i = 0; i < this._n; i++) {
				this._model[i] = model()
			}
		}
	}

	init(train_x, train_y) {
		train_y = train_y.flat()
		if (!this._classes) {
			this._classes = [...new Set(train_y)]
			this._n = this._classes.length
			for (let i = 0; i < this._n; i++) {
				this._model[i] = this._modelcls()
			}
		}
		for (let i = 0; i < this._n; i++) {
			if (this._model[i].init) {
				const dy = train_y.map(c => (c === this._classes[i] ? 1 : -1))
				this._model[i].init(train_x, dy)
			}
		}
	}

	fit(x, y, ...args) {
		if (!this._classes) {
			this.init(x, y)
		}
		for (let i = 0; i < this._n; i++) {
			if (this._model[i].init && this._model[i].init.length >= 2) {
				this._model[i].fit(x, y, ...args)
			} else {
				const dy = y.flat().map(c => (c === this._classes[i] ? 1 : -1))
				this._model[i].fit(x, dy, ...args)
			}
		}
	}

	predict(data) {
		const pred = []
		for (let i = 0; i < this._n; i++) {
			this._model[i].predict(data).forEach((v, k) => {
				if (!pred[k]) {
					pred[k] = []
				}
				pred[k][i] = Array.isArray(v) ? v[0] : v
			})
		}
		return pred.map(p => {
			let maxv = -Infinity
			let maxi = -1
			for (let i = 0; i < p.length; i++) {
				if (p[i] > maxv) {
					maxv = p[i]
					maxi = i
				}
			}
			return this._classes[maxi]
		})
	}
}

class OneVsOneModel {
	// one vs one
	constructor(model, classes) {
		if (classes && !Array.isArray(classes)) {
			classes = [...classes]
		}
		this._modelcls = model
		this._classes = classes
		this._model = []
		this._n = 0
		if (classes) {
			this._n = classes.length
			for (let i = 0; i < this._n; i++) {
				this._model[i] = []
				for (let j = 0; j < i; j++) {
					this._model[i][j] = model()
				}
			}
		}
	}

	init(train_x, train_y) {
		train_y = train_y.flat()
		if (!this._classes) {
			this._classes = [...new Set(train_y)]
			this._n = this._classes.length
			for (let i = 0; i < this._n; i++) {
				this._model[i] = []
				for (let j = 0; j < i; j++) {
					this._model[i][j] = this._modelcls()
				}
			}
		}
		const d = {}
		train_y.forEach((c, i) => {
			if (!d[c]) d[c] = []
			d[c].push(train_x[i])
		})
		const lbl = []
		for (let i = 0; i < this._n; i++) {
			lbl[i] = Array(d[this._classes[i]].length).fill(1)
			for (let j = 0; j < i; j++) {
				if (this._model[i][j].init) {
					const dx = d[this._classes[i]].concat(d[this._classes[j]])
					const dy = lbl[i].concat(lbl[j])
					this._model[i][j].init(dx, dy)
				}
			}
			lbl[i].fill(-1)
		}
	}

	fit(x, y, ...args) {
		if (!this._classes) {
			this.init(x, y)
		}
		const d = {}
		y?.flat().forEach((c, i) => {
			if (!d[c]) d[c] = []
			d[c].push(x[i])
		})
		const lbl = []
		for (let i = 0; i < this._n; i++) {
			lbl[i] = Array(d[this._classes[i]]?.length ?? 0).fill(1)
			for (let j = 0; j < i; j++) {
				if (this._model[i][j].init && this._model[i][j].init.length >= 2) {
					this._model[i][j].fit(x, y, ...args)
				} else {
					const dx = d[this._classes[i]].concat(d[this._classes[j]])
					const dy = lbl[i].concat(lbl[j])
					this._model[i][j].fit(dx, dy, ...args)
				}
			}
			lbl[i].fill(-1)
		}
	}

	predict(data) {
		const pred = []
		for (let i = 0; i < data.length; i++) {
			pred.push(Array(this._n).fill(0))
		}
		for (let i = 0; i < this._n; i++) {
			for (let j = 0; j < i; j++) {
				this._model[i][j].predict(data).forEach((v, k) => {
					pred[k][v > 0 ? i : j]++
				})
			}
		}
		return pred.map(p => {
			let maxv = -Infinity
			let maxi = -1
			for (let i = 0; i < p.length; i++) {
				if (p[i] > maxv) {
					maxv = p[i]
					maxi = i
				}
			}
			return this._classes[maxi]
		})
	}
}