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classification.js
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classification.js
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/**
* Returns accuracy.
*
* @param {*[]} pred Predicted classes
* @param {*[]} t True classes
* @returns {number} Accuracy
*/
export function accuracy(pred, t) {
const n = pred.length
let c = 0
for (let i = 0; i < n; i++) {
if (pred[i] === t[i]) {
c++
}
}
return c / n
}
/**
* Returns precision with macro average.
*
* @param {*[]} pred Predicted classes
* @param {*[]} t True classes
* @returns {number} Precision
*/
export function precision(pred, t) {
const n = pred.length
const classes = [...new Set(t)]
let c = 0
for (let k = 0; k < classes.length; k++) {
let tp = 0
let fp = 0
for (let i = 0; i < n; i++) {
if (t[i] === classes[k] && pred[i] === classes[k]) {
tp++
} else if (t[i] !== classes[k] && pred[i] === classes[k]) {
fp++
}
}
c += tp / (tp + fp)
}
return c / classes.length
}
/**
* Returns recall with macro average.
*
* @param {*[]} pred Predicted classes
* @param {*[]} t True classes
* @returns {number} Recall
*/
export function recall(pred, t) {
const n = pred.length
const classes = [...new Set(t)]
let c = 0
for (let k = 0; k < classes.length; k++) {
let tp = 0
let fn = 0
for (let i = 0; i < n; i++) {
if (t[i] === classes[k] && pred[i] === classes[k]) {
tp++
} else if (t[i] === classes[k] && pred[i] !== classes[k]) {
fn++
}
}
c += tp / (tp + fn)
}
return c / classes.length
}
/**
* Returns F-score with macro average.
*
* @param {*[]} pred Predicted classes
* @param {*[]} t True classes
* @param {number} [beta] Positive real factor. Recall is considered `beta` times as important as precision.
* @returns {number} F-score
*/
export function fScore(pred, t, beta = 1) {
const n = pred.length
const classes = [...new Set(t)]
let c = 0
for (let k = 0; k < classes.length; k++) {
let tp = 0
let fp = 0
let fn = 0
for (let i = 0; i < n; i++) {
if (t[i] === classes[k] && pred[i] === classes[k]) {
tp++
} else if (t[i] !== classes[k] && pred[i] === classes[k]) {
fp++
} else if (t[i] === classes[k] && pred[i] !== classes[k]) {
fn++
}
}
c += ((1 + beta ** 2) * tp) / ((1 + beta ** 2) * tp + fp + beta ** 2 * fn)
}
return c / classes.length
}
/**
* Returns Cohen's kappa coefficient.
*
* @param {*[]} pred Predicted classes
* @param {*[]} t True classes
* @returns {number} Cohen's kappa coefficient
*/
export function cohensKappa(pred, t) {
const n = pred.length
const classes = [...new Set(t)]
const cp = Array(classes.length).fill(0)
const ct = Array(classes.length).fill(0)
let p0 = 0
for (let i = 0; i < n; i++) {
cp[classes.indexOf(pred[i])]++
ct[classes.indexOf(t[i])]++
if (pred[i] === t[i]) {
p0++
}
}
p0 /= n
const pe = cp.reduce((s, v, k) => s + v * ct[k], 0) / n ** 2
return (p0 - pe) / (1 - pe)
}