natural/lib/natural/classifiers/maxent/Classifier.js

General natural language (tokenizing, stemming (English, Russian, Spanish), part-of-speech tagging, sentiment analysis, classification, inflection, phonetics, tfidf, WordNet, jaro-winkler, Levenshtein distance, Dice's Coefficient) facilities for node.

/*
Classifier class that provides functionality for training and
classification
Copyright (C) 2017, 2023 Hugo W.L. ter Doest

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 fs = require('fs')

const Context = require('./Context')
const Element = require('./Element')
const Sample = require('./Sample')
const Scaler = require('./GISScaler')
const FeatureSet = require('./FeatureSet')

const DEBUG = false

class Classifier {
  constructor (features, sample) {
    if (features) {
      this.features = features
    } else {
      this.features = new FeatureSet()
    }
    this.features = features
    if (sample) {
      this.sample = sample
    } else {
      this.sample = new Sample()
    }
  }

  // Loads a classifier from file.
  // Caveat: feature functions are generated from the sample elements. You need
  // to create your own specialisation of the Element class that can generate
  // your own specific feature functions
  load (filename, ElementClass, callback) {
    fs.readFile(filename, 'utf8', function (err, data) {
      if (!err) {
        const classifierData = JSON.parse(data)
        const sample = new Sample()
        classifierData.sample.elements.forEach(function (elementData) {
          const elt = new ElementClass(elementData.a, new Context(elementData.b.data))
          sample.addElement(elt)
        })
        const featureSet = new FeatureSet()
        sample.generateFeatures(featureSet)
        const classifier = new Classifier(featureSet, sample)
        callback(err, classifier)
      } else {
        if (callback) {
          callback(err)
        }
      }
    })
  }

  save (filename, callback) {
    const data = JSON.stringify(this, null, 2)
    const classifier = this
    fs.writeFile(filename, data, 'utf8', function (err) {
      if (callback) {
        DEBUG && console.log('Saved classifier to ' + filename)
        callback(err, err ? null : classifier)
      }
    })
  }

  addElement (x) {
    this.sample.addElement(x)
  }

  addDocument (context, classification, ElementClass) {
    Classifier.prototype.addElement(new ElementClass(classification, context))
  }

  train (maxIterations, minImprovement) {
    this.scaler = new Scaler(this.features, this.sample)
    this.p = this.scaler.run(maxIterations, minImprovement)
  }

  getClassifications (b) {
    const scores = []
    const that = this
    this.sample.getClasses().forEach(function (a) {
      const x = new Element(a, b)
      scores.push({
        label: a,
        value: that.p.calculateAPriori(x)
      })
    })
    return scores
  }

  classify (b) {
    const scores = this.getClassifications(b)
    // Sort the scores in an array
    scores.sort(function (a, b) {
      return b.value - a.value
    })
    // Check if the classifier discriminates
    const min = scores[scores.length - 1].value
    const max = scores[0].value
    if (min === max) {
      return ''
    } else {
      // Return the highest scoring classes
      return scores[0].label
    }
  }
}

module.exports = Classifier