/
ranknet.js
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/
ranknet.js
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import Matrix from '../util/matrix.js'
import { AdamOptimizer } from './nns/optimizer.js'
const ActivationFunctions = {
identity: {
calc: i => i,
grad: () => 1,
},
relu: {
calc: i => Math.max(0, i),
grad: i => (i > 0 ? 1 : 0),
},
sigmoid: {
calc: i => 1 / (1 + Math.exp(-i)),
grad: (i, o) => o * (1 - o),
},
tanh: {
calc: Math.tanh,
grad: (i, o) => 1 - o ** 2,
},
}
/**
* RankNet
*/
export default class RankNet {
// Learning to Rank using Gradient Descent
// https://www.microsoft.com/en-us/research/wp-content/uploads/2005/08/icml_ranking.pdf
/**
* @param {number[]} layer_sizes Sizes of all layers
* @param {string | string[]} [activations] Activation names
* @param {number} [rate] Learning rate
*/
constructor(layer_sizes, activations = 'tanh', rate = 0.01) {
this._rate = rate
this._layer_sizes = layer_sizes
this._activations = activations
this._a = []
this._w = []
this._b = []
this._optimizer = new AdamOptimizer(rate).manager()
}
_init(sizes) {
if (typeof this._activations === 'string') {
this._activations = Array(sizes.length - 2).fill(this._activations)
}
for (let i = 0; i < sizes.length - 1; i++) {
this._a[i] = ActivationFunctions[this._activations[i]]
this._w[i] = Matrix.randn(sizes[i], sizes[i + 1], 0, 0.1)
this._b[i] = Matrix.zeros(1, sizes[i + 1])
}
}
_calc(x) {
const ins = [x]
const outs = [x]
for (let i = 0; i < this._w.length; i++) {
ins[i + 1] = x = x.dot(this._w[i])
x.add(this._b[i])
outs[i + 1] = x = x.copy()
if (this._a[i]) {
x.map(this._a[i].calc)
}
}
return [ins, outs]
}
/**
* Fit model.
*
* @param {Array<Array<number>>} x1 Training data 1
* @param {Array<Array<number>>} x2 Training data 2
* @param {Array<-1 | 0 | 1>} comp Sign of (data 1 rank - data 2 rank). If data 1 rank is bigger than data 2, corresponding value is 1.
* @returns {number} loss
*/
fit(x1, x2, comp) {
if (this._w.length === 0) {
const sizes = [x1[0].length, ...this._layer_sizes, 1]
this._init(sizes)
}
const n = x1.length
x1 = Matrix.fromArray(x1)
x2 = Matrix.fromArray(x2)
const [ins1, outs1] = this._calc(x1)
const [ins2, outs2] = this._calc(x2)
const o = Matrix.sub(outs1[outs1.length - 1], outs2[outs2.length - 1])
let e1 = o.copy()
let e2 = o.copy()
let c = 0
for (let i = 0; i < n; i++) {
const pbar = comp[i] > 0 ? 1 : comp[i] === 0 ? 0.5 : 0
const oi = o.value[i]
e1.value[i] = -pbar + 1 / (1 + Math.exp(-oi))
e2.value[i] = pbar - 1 / (1 + Math.exp(-oi))
c += -pbar * oi + Math.log(1 + Math.exp(oi))
}
for (let i = this._w.length - 1; i >= 0; i--) {
if (this._a[i]) {
for (let k = 0; k < e1.length; k++) {
e1.value[k] *= this._a[i].grad(ins1[i + 1].value[k], outs1[i + 1].value[k])
e2.value[k] *= this._a[i].grad(ins2[i + 1].value[k], outs2[i + 1].value[k])
}
}
const dw = outs1[i].tDot(e1)
dw.add(outs2[i].tDot(e2))
dw.div(n)
const db = e1.mean(0)
db.add(e2.mean(0))
e1 = e1.dot(this._w[i].t)
e2 = e2.dot(this._w[i].t)
this._w[i].sub(this._optimizer.delta(`w${i}`, dw))
this._b[i].sub(this._optimizer.delta(`b${i}`, db))
}
return c
}
/**
* Returns predicted values.
*
* @param {Array<Array<number>>} x Sample data
* @returns {Array<number>} Predicted values
*/
predict(x) {
x = Matrix.fromArray(x)
const [, outs] = this._calc(x)
return outs[outs.length - 1].value
}
}