Autotuner.js

Autotuner is a machine learning model selection and hyper-parameter tuning module. Uses a Bayesian optimization approach to pick most promising hyperparameters.

Getting Started

Install and use the package with Node:

npm install autotuner

var autotuner = require('autotuner');

For usage within the browser, install and use the with Bower:

bower install autotuner

Or simply download the autotuner.js file.

Usage

We first define the parameter space. It is done with the Paramspace class. We add models to it by calling addModel(modelName, modelParameters) where modelName is a string model identifier, and modelParameters is an object where fields are parameter names and values are lists of possible parameter values.

Here is an example:

var p = new autotuner.Paramspace();
p.addModel('model1', {'param1' : [1,2,3], 'param2' : 10});
p.addModel('model2', {'param3' : [5,10,15]});

Then we use the parameter space to initialize the optimizer:

// Initialize the optimizer with the parameter space.
var opt = new autotuner.Optimizer(p.domainIndices, p.modelsDomains);

while (optimizing) {
    // Take a suggestion from the optimizer.
    var point = opt.getNextPoint();
    
    // We can extract the model name and parameters.
    var model = p.domain[point]['model'];
    var params = p.domain[point]['params'];
    
    // Train a model given the params and obtain a quality metric value.
    // ...
    
    // Report the obtained quality metric value.
    p.addSample(point, value);
}

Transfer learning

If we want to take advantage of the observed values from the previous optimization runs to improve our next optimization run, we need the Priors helper class.

// This object is created only once and kept across optimization runs.
var priors = new autotuner.Priors(p.domainIndices);

We then use this class in our optimization runs as follows:

// Use the mean and kernel from the Priors instance to
// initialize the optimizer. 
var opt = new autotuner.Optimizer(p.domainIndices, p.modelsDomains, priors.mean, priors.kernel);

// Regular optimization run.
while (optimizing) {
    var point = opt.getNextPoint();
    var model = p.domain[point]['model'];
    var params = p.domain[point]['params'];
    // ...
    p.addSample(point, value);
}

// Commit the observed points to the priors.
priors.commit(p.observedValues);

After commiting the observed values, the priors.mean and priors.kernel are updated with the observed values so we can use them to initialize the next optimization run.

Development

Pull and initialize:

git pull https://github.com/cytoai/autotuner.git
cd autotuner
npm install

To run tests:

npm test

To build the bundled autotuner.js script:

npm run-script build