bayes_classifier_spec.js

/*
Copyright (c) 2011, Chris Umbel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/

'use strict'

const natural = require('../lib/natural')
// var sinon = require('sinon');
// var baseClassifier = require('../lib/natural/classifiers/classifier.js');

function setupClassifier () {
  const classifier = new natural.BayesClassifier()
  classifier.addDocument(['fix', 'box'], 'computing')
  classifier.addDocument(['write', 'code'], 'computing')
  classifier.addDocument(['script', 'code'], 'computing')
  classifier.addDocument(['write', 'book'], 'literature')
  classifier.addDocument(['read', 'book'], 'literature')
  classifier.addDocument(['study', 'book'], 'literature')
  return classifier
}

describe('bayes classifier', function () {
  describe('classifier', function () {
    it('should classify with arrays', function () {
      const classifier = setupClassifier()
      classifier.train()
      expect(classifier.classify(['bug', 'code'])).toBe('computing')
      expect(classifier.classify(['read', 'thing'])).toBe('literature')
    })

    it('should classify with parallel training', function () {
      const classifier = setupClassifier()
      // Check for parallel method
      if (classifier.trainParallel) {
        classifier.trainParallel(2, function (err) {
          if (err) {
            console.log(err)
            return
          }
          expect(classifier.classify(['bug', 'code'])).toBe('computing')
          expect(classifier.classify(['read', 'thing'])).toBe('literature')
          // asyncSpecDone();
        })
      }
    })

    it('should classify with parallel batched training', function () {
      const classifier = setupClassifier()
      if (classifier.trainParallelBatches) {
        // Check for parallel method
        classifier.events.on('doneTraining', function () {
          expect(classifier.classify(['bug', 'code'])).toBe('computing')
          expect(classifier.classify(['read', 'thing'])).toBe('literature')
          // asyncSpecDone();
        })
        classifier.trainParallelBatches({ numThreads: 2, batchSize: 2 })
      }
    })

    it('should provide all classification scores', function () {
      const classifier = setupClassifier()
      classifier.train()

      expect(classifier.getClassifications('i write code')[0].label).toBe('computing')
      expect(classifier.getClassifications('i write code')[1].label).toBe('literature')
    })

    function setupClassifierWithSentences () {
      const classifier = new natural.BayesClassifier()
      classifier.addDocument('i fixed the box', 'computing')
      classifier.addDocument('i write code', 'computing')
      classifier.addDocument('nasty script code', 'computing')
      classifier.addDocument('write a book', 'literature')
      classifier.addDocument('read a book', 'literature')
      classifier.addDocument('study the books', 'literature')
      return classifier
    }

    it('should classify with strings', function () {
      const classifier = setupClassifierWithSentences()
      classifier.train()
      expect(classifier.classify('a bug in the code')).toBe('computing')
      expect(classifier.classify('read all the books')).toBe('literature')
    })

    it('should classify and re-classify after document-removal', function () {
      const classifier = new natural.BayesClassifier()
      let item
      const classifications = {}

      // Add some good/bad docs and train
      classifier.addDocument('foo bar baz', 'good')
      classifier.addDocument('qux zooby', 'bad')
      classifier.addDocument('asdf qwer', 'bad')
      classifier.train()

      expect(classifier.classify('foo')).toBe('good')
      expect(classifier.classify('qux')).toBe('bad')

      // Remove one of the bad docs, retrain
      classifier.removeDocument('qux zooby', 'bad')
      classifier.retrain()

      // Simple `classify` will still return a single result, even if
      // ratio for each side is equal -- have to compare actual values in
      // the classifications, should be equal since qux is unclassified
      const arr = classifier.getClassifications('qux')
      for (let i = 0, ii = arr.length; i < ii; i++) {
        item = arr[i]
        classifications[item.label] = item.value
      }
      expect(classifications.good).toEqual(classifications.bad)

      // Re-classify as good, retrain
      classifier.addDocument('qux zooby', 'good')
      classifier.retrain()

      // Should now be good, original docs should be unaffected
      expect(classifier.classify('foo')).toBe('good')
      expect(classifier.classify('qux')).toBe('good')
    })

    it('should serialize and deserialize a working classifier', function () {
      const classifier = setupClassifierWithSentences()
      const obj = JSON.stringify(classifier)
      const newClassifier = natural.BayesClassifier.restore(JSON.parse(obj))

      newClassifier.addDocument('kick a ball', 'sports')
      newClassifier.addDocument('hit some balls', 'sports')
      newClassifier.addDocument('kick and punch', 'sports')

      newClassifier.train()

      expect(newClassifier.classify('a bug in the code')).toBe('computing')
      expect(newClassifier.classify('read all the books')).toBe('literature')
      expect(newClassifier.classify('kick butt')).toBe('sports')
    })

    /*
        it('should save and load a working classifier', function(done) {
            var classifier = new natural.BayesClassifier();
            classifier.addDocument('i fixed the box', 'computing');
            classifier.addDocument('i write code', 'computing');
            classifier.addDocument('nasty script code', 'computing');
            classifier.addDocument('write a book', 'literature');
            classifier.addDocument('read a book', 'literature');
            classifier.addDocument('study the books', 'literature');

            classifier.train();

            classifier.save('bayes_classifier.json', function(err) {
            natural.BayesClassifier.load('bayes_classifier.json', null, function(err, newClassifier){
              newClassifier.addDocument('kick a ball', 'sports');
              newClassifier.addDocument('hit some balls', 'sports');
              newClassifier.addDocument('kick and punch', 'sports');

              newClassifier.train();

              expect(newClassifier.classify('a bug in the code')).toBe('computing');
              expect(newClassifier.classify('read all the books')).toBe('literature');
              expect(newClassifier.classify('kick butt')).toBe('sports');
              done();
            });
          });
        });
        */

    /*
        it('should only execute the callback once when failing to load a classifier', function(done) {
            natural.BayesClassifier.load('nonexistant_bayes_classifier.json', null, function(err, newClassifier){
              expect(err.code).toBe('ENOENT');
              expect(newClassifier).toBe(undefined);
              done();
            });
        });
        */

    it('should accept an optional smoothing parameter for the Bayesian estimates', function () {
      const defaultClassifier = new natural.BayesClassifier()
      const PorterStemmer = require('../lib/natural/stemmers/porter_stemmer')
      const newClassifier1 = new natural.BayesClassifier(PorterStemmer)
      const newClassifier2 = new natural.BayesClassifier(PorterStemmer, 0.1)

      expect(defaultClassifier.classifier.smoothing).toBe(1.0)
      expect(newClassifier1.classifier.smoothing).toBe(1.0)
      expect(newClassifier2.classifier.smoothing).toBe(0.1)
    })
  })

  /*
    describe('load', function () {
        var sandbox;

        beforeEach(function () {
            sandbox = sinon.sandbox.create();
        });

        afterEach(function () {
            sandbox.restore();
        });
        it('should pass an error to the callback function', function () {
            sandbox.stub(baseClassifier, 'load', function (filename, cb) {
                cb(new Error('An error occurred'));
            });
            natural.BayesClassifier.load('/spec/test_data/tfidf_document1.txt', null, function (err, newClassifier) {
                expect(err).toBe.ok;
                expect(newClassifier).not.toBe.ok;
            });
        });
    });
    */
})