Package Exports

@tensorscript/ts-deeplearning

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@tensorscript/ts-deeplearning) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

@tensorscript/ts-deeplearning

Deep Learning Classification, Clustering and Regression with Tensorflow

Full Documentation

Installation

$ npm i @tensorscript/ts-deeplearning

Usage

Test against the Portland housing price dataset

import { MultipleLinearRegression, } from '@tensorscript/ts-deeplearning';
import ms from 'modelscript';

function scaleColumnMap(columnName) {
  return {
    name: columnName,
    options: {
      strategy: 'scale',
      scaleOptions: {
        strategy:'standard'
      }
    }
  }
}

async function main(){
  const housingdataCSV = await ms.csv.loadCSV('./test/mock/data/portland_housing_data.csv', {
    colParser: {
      sqft: 'number',
      bedrooms: 'number',
      price: 'number',
    }
  });
  /*
  housingdataCSV = [ 
    { sqft: 2104, bedrooms: 3, price: 399900 },
    { sqft: 1600, bedrooms: 3, price: 329900 },
    ...
    { sqft: 1203, bedrooms: 3, price: 239500 } 
  ] 
  */
  const DataSet = new ms.DataSet(housingdataCSV);
  DataSet.fitColumns({
    columns: [
      'sqft',
      'bedrooms',
      'price',
    ].map(scaleColumnMap),
    returnData:true,
  });
  const independentVariables = [ 'sqft', 'bedrooms',];
  const dependentVariables = [ 'price', ];
  const x_matrix = DataSet.columnMatrix(independentVariables); 
  const y_matrix = DataSet.columnMatrix(dependentVariables);
  /* x_matrix = [
      [2014, 3],
      [1600, 3],
    ];
    y_matrix = [
      [399900],
      [329900],
    ];
    const y_vector = ms.util.pivotVector(y_matrix)[ 0 ];// not used but just illustrative
    // y_vector = [ 399900, 329900]
   */
  const testSqft = DataSet.scalers.get('sqft').scale(1650);
  const testBedrooms = DataSet.scalers.get('bedrooms').scale(3);
  const input_x = [
    testSqft,
    testBedrooms,
  ]; // input_x: [ -0.4412732005944351, -0.2236751871685913 ]
  const tfMLR = new MultipleLinearRegression();
  const model = await tfMLR.train(x_matrix, y_matrix);
  const scaledPrediction = await tfMLR.predict(input_x); // [ -0.3785287367962629 ]
  const prediction = DataSet.scalers.get('price').descale(scaledPrediction); // prediction: 293081.4643348962
}

main();

This MLR module give you a similar ml.js interface for machine learning

// Similarly with ml.js
import ms from 'modelscript';
const MLR = ms.ml.Regression.MultivariateLinearRegression;
const regression = new MLR(x_matrix, y_matrix);
const MLJSscaledPrediction = regression.predict(input_x); //[ -0.3785287367962629 ],
const MLJSprediction = DataSet.scalers.get('price').descale(MLJSscaledPrediction); // prediction: 293081.4643348962

Testing

$ npm i
$ npm test

Contributing

Fork, write tests and create a pull request!

Misc

As of Node 8, ES modules are still used behind a flag, when running natively as an ES module

$ node --experimental-modules my-machine-learning-script.mjs
# Also there are native bindings that require Python 2.x, make sure if you're using Andaconda, you build with your Python 2.x bin
$ npm i --python=/usr/bin/python

License

MIT