percipio
v0.1.2
Published
Easy Data science (Machine learning) in JavaScript & Node (Including Bandits & Naive Bayes)
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Percipio - Easy Data Science (Machine Learning) in JavaScript & Node
Percipio is a simple minimalistic JavaScript library for understanding & making decisions with data.
Features
- Bayesian Bandit algorithm (using Thompson sampling)
- Naive Bayes classifier
Install
npm install percipio
Quick Start
Let's find out which programming language is better! Java or C#, anyone? (this might be a bit contrived example...) We can model this using simple Multi-armed bandit experiment (Multi-armed bandit experiments are even used by Google)
Experiment setup
We define 2 arms (possible outcomes) as follows
- Arm 1 - id: 1, reward: Java
- Arm 2 - id: 2, reward: C#
and create the Bandit predictor
var bandits = require('percipio').bandits
var BanditPredictor = bandits.Predictor
var rewards = ["Java", "C#"]
var armIds = [0, 1]
var predictor = BanditPredictor([
bandits.createArm(armIds[0], rewards[0]),
bandits.createArm(armIds[1], rewards[1])
])
Hidden probabilities
Next let's choose the probabilities which the predictor should find
var hiddenProbabilities = [0.5, 0.7]
Simulation
Let's define our result simulation function (in the real world you should get results from your app, users etc.)
function simulateResult(p){
return Math.random() < p ? 1 : 0
}
And run the simulation
for (var i = 0; i < 1000; i++) {
var arm = predictor.predict()
var p = hiddenProbabilities[arm.id]
predictor.learn(arm, simulateResult(p))
}
Result
Now the predictor has (hopefully) learned the hidden probabilities and we can get them
var javaProbabilities = predictor.posteriorProbabilities()[0]
var cSharpProbabilities = predictor.posteriorProbabilities()[1]
console.log(javaProbabilities)
console.log(cSharpProbabilities)
Complete example
Now try to run this yourself
var bandits = require('percipio').bandits
var BanditPredictor = bandits.Predictor
var rewards = ["Java", "C#"]
var armIds = [0, 1]
var predictor = BanditPredictor([
bandits.createArm(armIds[0], rewards[0]),
bandits.createArm(armIds[1], rewards[1])
])
var hiddenProbabilities = [0.5, 0.7]
function simulateResult(p){
return Math.random() < p ? 1 : 0
}
for (var i = 0; i < 1000; i++) {
var arm = predictor.predict()
var p = hiddenProbabilities[arm.id]
predictor.learn(arm, simulateResult(p))
}
var javaProbabilities = predictor.posteriorProbabilities()[0]
var cSharpProbabilities = predictor.posteriorProbabilities()[1]
console.log(javaProbabilities)
console.log(cSharpProbabilities)
Current state
Pretty alphaish, I guess. Looking forward to implement
- kNN
- Linear regression
- Data loaders/importers
Wanna help out?
Hop right in!
Development setup
git clone [email protected]:naughtyspirit/percipio.git
cd percipio
npm install
Run tests
npm test
License
MIT