Pearson Correlation (JavaScript)

pearson-correlation.js

/**
 *  @fileoverview Pearson correlation score algorithm.
 *  @author [email protected] (Matt West)
 *  @license Copyright 2013 Matt West.
 *  Licensed under MIT (http://opensource.org/licenses/MIT).
 */


/**
 *  Calculate the person correlation score between two items in a dataset.
 *
 *  @param  {object}  prefs The dataset containing data about both items that
 *                    are being compared.
 *  @param  {string}  p1 Item one for comparison.
 *  @param  {string}  p2 Item two for comparison.
 *  @return {float}  The pearson correlation score.
 */
function pearsonCorrelation(prefs, p1, p2) {
  var si = [];

  for (var key in prefs[p1]) {
    if (prefs[p2][key]) si.push(key);
  }

  var n = si.length;

  if (n == 0) return 0;

  var sum1 = 0;
  for (var i = 0; i < si.length; i++) sum1 += prefs[p1][si[i]];

  var sum2 = 0;
  for (var i = 0; i < si.length; i++) sum2 += prefs[p2][si[i]];

  var sum1Sq = 0;
  for (var i = 0; i < si.length; i++) {
    sum1Sq += Math.pow(prefs[p1][si[i]], 2);
  }

  var sum2Sq = 0;
  for (var i = 0; i < si.length; i++) {
    sum2Sq += Math.pow(prefs[p2][si[i]], 2);
  }

  var pSum = 0;
  for (var i = 0; i < si.length; i++) {
    pSum += prefs[p1][si[i]] * prefs[p2][si[i]];
  }

  var num = pSum - (sum1 * sum2 / n);
  var den = Math.sqrt((sum1Sq - Math.pow(sum1, 2) / n) *
      (sum2Sq - Math.pow(sum2, 2) / n));

  if (den == 0) return 0;

  return num / den;
}

could you give one example please for it with example dataset?

If you still need it :)

var data = new Array(
                    new Array(21,54,60,78,82),
                    new Array(20,54,54,65,45)
        );
console.log(pearsonCorrelation(data,0,1))

please note the function works with qualitative distributions too (associative array)

Hi there, i used ur library just lazy to write one...i got a pearson relation as NaN for some huge dataset arrays. i tried writing a new code ..then got the actual value.

i have one array with values like 0.4554 of more than 20,000 rows and another with values like 20. as it is big file, i cannot link it here but just want to bring to ur attention.

function pearson(values) {
const n = values.length;

if (n == 0) return 0;

let meanX = 0;
let meanY = 0;
for (var i = 0; i < n; i++) {
meanX += values[i].x / n
meanY += values[i].y / n
}

let num = 0;
let den1 = 0;
let den2 = 0;

for (var i = 0; i < n; i++) {
let dx = (values[i].x - meanX);
let dy = (values[i].y - meanY);
num += dx * dy
den1 += dx * dx
den2 += dy * dy
}

const den = Math.sqrt(den1) * Math.sqrt(den2);

if (den == 0) return 0;

return num / den;
}

pearson([{x: 0, y: 0}, {x:1, y:1}])

thank u ，i just need this

I've rewrote this function in es6.
The code is more concise, but it does not work with associative arrays that were possible in the original code.

/**
 * calculates pearson correlation
 * @param {number[]} d1
 * @param {number[]} d2
 */
export function corr(d1, d2) {
  let { min, pow, sqrt } = Math
  let add = (a, b) => a + b
  let n = min(d1.length, d2.length)
  if (n === 0) {
    return 0
  }
  [d1, d2] = [d1.slice(0, n), d2.slice(0, n)]
  let [sum1, sum2] = [d1, d2].map(l => l.reduce(add))
  let [pow1, pow2] = [d1, d2].map(l => l.reduce((a, b) => a + pow(b, 2), 0))
  let mulSum = d1.map((n, i) => n * d2[i]).reduce(add)
  let dense = sqrt((pow1 - pow(sum1, 2) / n) * (pow2 - pow(sum2, 2) / n))
  if (dense === 0) {
    return 0
  }
  return (mulSum - (sum1 * sum2 / n)) / dense
}

function pearson(x, y){
	let promedio = (lista) => { return lista.reduce((s, a) => s + a, 0) / lista.length };
	let n = x.length, prom_x = promedio(x) , prom_y = promedio(y);
	return (x.map( (e, i, r) => (r = {x:e, y:y[i]}) ).reduce( (s, a) => s + a.x * a.y, 0) - n * prom_x * prom_y) / 
	((Math.sqrt(x.reduce( (s, a) => (s + a * a) , 0) - n * prom_x * prom_x)) *
	(Math.sqrt(y.reduce( (s, a) => (s + a * a) , 0) - n * prom_y * prom_y)));
} 

This is my version!. I just did it as a practice for an exam that I have soon

@joracha your version is perfect! It's fastest version at this time!

Unfortunately I found that it doesn't respect to "empty" values, but it have to.

I've improved it a bit:

function pearson (x, y) {
  const promedio = l => l.reduce((s, a) => s + a, 0) / l.length
  const calc = (v, prom) => Math.sqrt(v.reduce((s, a) => (s + a * a), 0) - n * prom * prom)
  let n = x.length
  let nn = 0
  for (let i = 0; i < n; i++, nn++) {
    if ((!x[i] && x[i] !== 0) || (!y[i] && y[i] !== 0)) {
      nn--
      continue
    }
    x[nn] = x[i]
    y[nn] = y[i]
  }
  if (n !== nn) {
    x = x.splice(0, nn)
    y = y.splice(0, nn)
    n = nn
  }
  const prom_x = promedio(x), prom_y = promedio(y)
  return (x
      .map((e, i) => ({ x: e, y: y[i] }))
      .reduce((v, a) => v + a.x * a.y, 0) - n * prom_x * prom_y
  ) / (calc(x, prom_x) * calc(y, prom_y))
}

Test showed that the speed degradation is insignificant (6.92%):

// test.js
const initialPearson = require('../initialPearson')
const improvedPearson = require('../improvedPearson')

const series1 = [80.026413,80.330908,76.68564,72.095302,75.473899,82.647118]
const series2 = [81.662683,85.802179,78.148427,81.126326,77.853491,85.974228]

console.log('initialPearson', initialPearson(series1, series2))
console.log('improvedPearson', improvedPearson(series1, series2))

benchmarkjs.options({ testTime: 4000 })
benchmarkjs('initialPearson', () => initialPearson(series1, series2))
benchmarkjs('improvedPearson', () => improvedPearson(series1, series2))
console.log(benchmarkjs.results)

Output

$ node test.js
initialPearson 0.6917288370450843
improvedPearson 0.6917288370450843
[
  {
    name: 'initialPearson',
    elapsed: 4397,
    checks: 3,
    totalIterations: 48518134,
    perSecondIterations: 11034372,
    isOptimized: null,
    diff: '0%'
  },
  {
    name: 'improvedPearson',
    elapsed: 4395,
    checks: 3,
    totalIterations: 45139154,
    perSecondIterations: 10270569,
    isOptimized: null,
    diff: '6.92%'
  }
]

In case of having a null it works as expected (and even faster actually):

// test.js
const initialPearson = require('../initialPearson')
const improvedPearson = require('../improvedPearson')

const series1 = [80.026413,80.330908,76.68564,72.095302,null,82.647118]
const series2 = [81.662683,85.802179,78.148427,81.126326,77.853491,85.974228]

benchmarkjs.options({ testTime: 4000 })
benchmarkjs('initialPearson', () => initialPearson(series1, series2))
benchmarkjs('improvedPearson', () => improvedPearson(series1, series2))
console.log(benchmarkjs.results)

$ node test.js
initialPearson 0.5995223578720159
improvedPearson 0.6570987279478532
[
  {
    name: 'initialPearson',
    elapsed: 4136,
    checks: 2,
    totalIterations: 26454098,
    perSecondIterations: 6396058,
    isOptimized: null,
    diff: '0%'
  },
  {
    name: 'improvedPearson',
    elapsed: 4139,
    checks: 2,
    totalIterations: 26027407,
    perSecondIterations: 6288332,
    isOptimized: null,
    diff: '1.68%'
  }
]

Thank you!

The above code has a bug, n is used before being defined.

let n = x.length

needs to be before the const calc ... line.

The above code has a bug, n is used before being defined.

let n = x.length

needs to be before the const calc ... line.

No it hasn't. The third line defines function calc without calling it.

The above code has a bug, n is used before being defined.
let n = x.length
needs to be before the const calc ... line.

No it hasn't. The third line defines function calc without calling it.

Well, it's still bad form. You're referencing a global that's defined later in scope and generally that's bad form.
The current version of ESLint doesn't like this, but as you're only giving an example here I guess it might be fine.