• ac-learn
  • Install
  • Documentation
    • Learner
    • ConfusionMatrix
  • Contributors

ac-learn

Where the All Contributors machine can learn about your contributions.

Install

yarn add ac-learn --save
#or
npm i -D ac-learn

Usage

const Learner = require('ac-learn')

//If you want to load a learner from a JSON export:
const learner = Learner.fromJSON(require('./your-learner.json'))
//If you want to use the default one
const learner = new Learner()
//If you want to your own dataset or customise the learner, check https://github.com/all-contributors/ac-learn#learner

//Training
learner.train() //Or
learner.train(someTrainingSet)

//Testing and getting stats
const fullStats = learner.eval()

//Cross-validation
const {microAvg, macroAvg} = learner.crossValidate()

//Confusion matrix (as string or console table)
const textualTable = learner.confusionMatrix.toString()
const cmTable = learner.confusionMatrix.toTable()

//Classifying an input
const output = learner.classify(someInput)
//Getting an input from an output
const input = learner.backClassify(someOutput)

//Saving the model to a JSON file
const savedModel = learner.toJSON()
const {writeFileSync} = require('fs')
writeFileSync('your-learner.json', JSON.stringify(jsonData))

Documentation

Table of Contents

• Learner
  • Parameters
  • Examples
  • train
    • Parameters
  • eval
    • Parameters
  • serializeClassifier
  • serializeAndSaveClassifier
    • Parameters
  • deserializeClassifier
    • Parameters
  • loadAndDeserializeClassifier
    • Parameters
  • classify
    • Parameters
  • crossValidate
    • Parameters
  • backClassify
    • Parameters
  • toJSON
  • fromJSON
    • Parameters
  • getCategoryPartition
    • Parameters
  • getStats
    • Parameters
• ConfusionMatrix
  • addEntry
    • Parameters
  • setEntry
    • Parameters
  • getEntry
    • Parameters
  • getTotal
  • getTP
    • Parameters
  • getFP
    • Parameters
  • getFN
    • Parameters
  • getTN
    • Parameters
  • getDiagonal
  • getTrue
  • getFalse
  • getPositive
    • Parameters
  • getNegative
    • Parameters
  • getPredPositive
    • Parameters
  • getPredNegative
    • Parameters
  • getSupport
    • Parameters
  • getAccuracy
    • Parameters
  • getMicroAccuracy
  • getMacroAccuracy
  • getWeightedAccuracy
  • getTotalPositiveRate
    • Parameters
  • getMicroRecall
  • getMacroRecall
  • getWeightedRecall
  • getPositivePredictiveValue
    • Parameters
  • getPositivePredictiveValue
    • Parameters
  • getMicroPrecision
  • getMacroPrecision
  • getWeightedPrecision
  • getMicroF1
  • getMacroF1
  • getWeightedF1
  • getFalseNegativeRate
    • Parameters
  • getMicroMissRate
  • getMacroMissRate
  • getWeightedMissRate
  • getFalsePositiveRate
    • Parameters
  • getMicroFallOut
  • getMacroFallOut
  • getWeightedFallOut
  • getTrueNegativeRate
    • Parameters
  • getMicroSpecificity
  • getMacroSpecificity
  • getWeightedSpecificity
  • getPrevalence
    • Parameters
  • getMicroPrevalence
  • getMacroPrevalence
  • getWeightedPrevalence
  • toString
    • Parameters
    • Examples
  • toTable
    • Parameters
  • getShortStats
    • Parameters
  • getStats
  • fromData
    • Parameters

Learner

NodeJS Classification-based learner.

Parameters

• opts Object Options.
  • opts.dataset Array<Object> Dataset (for training and testing) (optional, default require('./conv')('io'))
  • opts.splits number Dataset split percentage for the training/validation set (default: 70%/15%/15%) (optional, default [.7,.15])
  • opts.classifier function (): Object Classifier builder function (optional, default classifierBuilder)
  • opts.pastTrainingSamples Array<Object> Past training samples for the classifier (optional, default [])
  • opts.classes Array<string> List of classes (categories) (optional, default require('./categories'))

Examples

Using pre-defined data

const learner = new Learner()

Using a custom dataset

const learner = new Learner({
  dataset: [
    {input: 'something bad', output: 'bad'},
    {input: 'a good thing', output: 'good'},
  ],
})

Using a specified classifier function

const learner = new Learner({
  classifier: myClassifierBuilderFn, //see {@link module:./classifier} for an example (or checkout `limdu`'s examples)
})

Changing the train/test split percentage

const learner = new Learner({
  splits: [0.6, 0.2],
})

(Re-)Using past-training samples

const learner = new Learner({
  pastTrainingSamples: [
    {input: 'something bad', output: 'bad'},
    {input: 'a good thing', output: 'good'},
  ],
})

train

Parameters

• trainSet Array<Object> Training set (optional, default this.trainSet)

eval

Parameters

• log boolean Log events (optional, default false)

Returns Object Statistics from a confusion matrix

serializeClassifier

Returns string Serialized classifier

serializeAndSaveClassifier

Parameters

• file string Filename (optional, default 'classifier.json')

Returns Promise<(string | Error)> Serialized classifier

deserializeClassifier

Parameters

• serializedClassifier string .

Returns Object Deserialized classifier

loadAndDeserializeClassifier

Parameters

• file string Filename (optional, default 'classifier.json')

Returns Promise<(string | Error)> Deserialized classifier

classify

Parameters

• data {input: any, output: any} Data to classify

Returns Array<string> Classes

crossValidate

Parameters

• numOfFolds number Cross-validation folds (optional, default 5)
• verboseLevel number Verbosity level on limdu's explainations (optional, default 0)
• log boolean Cross-validation logging (optional, default false)

Returns {microAvg: Object, macroAvg: Object} Averages

backClassify

Parameters

• category string Category name.

Returns Array<string> Labels associated with category

toJSON

JSON representation of the learner with the serialized classification model.

Returns Object JSON representation

fromJSON

Parameters

• json (JSON | Object) JSON form

Returns Learner Generated learner from json

getCategoryPartition

Get the observational overall/train/validation/test count for each classes in the associated dataset.

Parameters

• log boolean Log events (optional, default false)
• outputFile string Filename for the output (to be used by chart.html) (optional, default '')

Returns Object<string, {overall: number, test: number, validation: number, train: number}> Partitions

getStats

Parameters

• log boolean Log events (optional, default false)
• categoryPartitionOutput string Filename for the output of the category partitions. (optional, default '')

Returns Object Statistics

ConfusionMatrix

Multi-class focused confusion matrix.

addEntry

Parameters

• actual string Actual class
• predicted string Predicted class

Returns number Updated entry

setEntry

Parameters

• actual string Actual class
• predicted string Predicted class
• val number New entry

getEntry

Parameters

• actual string Actual class
• predicted string Predicted class

Returns number Entry

getTotal

Get the total count of all entries.

Returns number Total count

getTP

Number of elements in the category class correctly predicted.

Parameters

• category string Class/category considered as positive

Returns number True Positives

getFP

Number of elements that aren't in the category class but predicted as such.

Parameters

• category string Class/category considered as positive

Returns number False Positives

getFN

Number of elements in the category class but predicted as not being in it.

Parameters

• category string Class/category considered as positive

Returns number False Negatives

getTN

Number of elements that aren't in the category class correctly predicted.

Parameters

• category string Class/category considered as positive

Returns number True Negatives

getDiagonal

Diagonal of truth (top-left → bottom-right)

Returns Array<number> Numbers in the diagonal

getTrue

Number of correct (truthful) predictions.

Returns number TP

getFalse

Number of incorrect predictions.

Returns number FP + FN

getPositive

Number of real (actual) "positive" elements (i.e. elements that belong to the category class).

Parameters

• category string Class/category considered as positive

Returns number TP + FN

getNegative

Number of real (actual) "negative" elements (i.e. elements that don't belong to the category class).

Parameters

• category string Class/category considered as positive

Returns number TN + FP

getPredPositive

Number of predicted "positive" elements (i.e. elements guessed as belonging to the category class).

Parameters

• category string Class/category considered as positive

Returns number TP + FN

getPredNegative

Number of predicted "negative" elements (i.e. elements guessed as not belonging to the category class).

Parameters

• category string Class/category considered as positive

Returns number TN + FP

getSupport

Support value (count/occurrences) of category in the matrix

Parameters

• category string Class/category to look at

Returns number Support value

getAccuracy

Prediction accuracy for category.

Parameters

• category string Class/category considered as positive

Returns number (TP + TN) / (TP + TN + FP + FN)

getMicroAccuracy

Micro-average of accuracy.

Returns number (TP0 + ... + TPn + TN0 + ... + TNn) / (TP0 + ... + TPn + TN0 + ... + TNn + FP0 + ... + FPn + FN0 + ... + FNn)

getMacroAccuracy

Macro-average of accuracy.

Returns number (A0 + ...+ An_1) / n

getWeightedAccuracy

Weighted accuracy.

Returns number (A0 _ s0 + ... + An _ sn) / Total

getTotalPositiveRate

Predicition recall.

Parameters

• category string Class/category considered as positive

Returns number TP / (TP + FN)

getMicroRecall

Micro-average of recall.

Returns number (TP0 + ... + TPn) / (TP0 + ... + TPn + FN0 + ... + FNn)

getMacroRecall

Macro-average of recall.

Returns number (R0 + R1 + ... + Rn-1) / n

getWeightedRecall

Weighted recalll.

Returns number (R0 _ s0 + ... + Rn _ sn) / Total

getPositivePredictiveValue

Prediction precision for category.

Parameters

• category string Class/category considered as positive

Returns number TP / (TP + FP)

getPositivePredictiveValue

Prediction F1 score for category.

Parameters

• category string Class/category considered as positive

Returns number 2 _ (Pr _ R) / (Pr + R)

getMicroPrecision

Micro-average of the precision.

Returns number (TP0 + ... + TPn) / (TP0 + ... + TPn + FP0 + ... FPn)

getMacroPrecision

Macro-average of the precsion.

Returns number (Pr0 + Pr1 + ... + Pr_n-1) / n

getWeightedPrecision

Weighted precision.

Returns number (Pr0 _ s0 + ... + Prn _ sn) / Total

getMicroF1

Micro-average of the F1 score.

Returns number 2 _ (TP0 + ... + TPn) / (2 _ (TP0 + ... + TPn) + (FN0 + ... + FNn) + (FP0 + ... + FPn))

getMacroF1

Macro-average of the F1 score.

Returns number (F0_1 + F1_1 + ... + F_n-1_1) / n

getWeightedF1

Weighted F1.

Returns number (F01 * s0 + ... + Fn1 * sn) / Total

getFalseNegativeRate

Miss rates on predictions for category.

Parameters

• category string Class/category considered as positive

Returns number FN / (TP + FN)

getMicroMissRate

Micro-average of the miss rate.

Returns number (FN0 + ... + FNn) / (TP0 + ... + TPn + FN0 + ... FNn)

getMacroMissRate

Macro-average of the miss rate.

Returns number (M0 + M1 + ... + Mn) / n

getWeightedMissRate

Weighted miss rate.

Returns number (M0 _ s0 + ... + Mn _ sn) / Total

getFalsePositiveRate

Fall out (false alarm) on predictions for category.

Parameters

• category string Class/category considered as positive

Returns number FP / (FP + TN)

getMicroFallOut

Micro-average of the fall out.

Returns number (FP0 + ... + FPn) / (FP0 + ... + FPn + TN0 + ... TNn)

getMacroFallOut

Macro-average of the fall out.

Returns number (Fo0 + Fo1 + ... + Fo_n) / n

getWeightedFallOut

Weighted fall out.

Returns number (Fo0 _ s0 + ... + Fon _ sn) / Total

getTrueNegativeRate

Specificity on predictions for category.

Parameters

• category string Class/category considered as positive

Returns number TN / (FP + TN)

getMicroSpecificity

Micro-average of the specificity.

Returns number (TN0 + ... + TNn) / (FP0 + ... + FPn + TN0 + ... TNn)

getMacroSpecificity

Macro-average of the specificity.

Returns number (S0 + S1 + ... + Sn) / n

getWeightedSpecificity

Weighted specificity.

Returns number (S0 _ s0 + ... + Sn _ sn) / Total

getPrevalence

Prevalence on predictions for category.

Parameters

• category string Class/category considered as positive

Returns number (TP + FN) / (TP + TN + FP + FN)

getMicroPrevalence

Micro-average of the prevalence.

Returns number (TP0 + ... + TPn + FN0 + ... + FNn) / (TP0 + ... + TPn + TN0 + ... + TNn + FP0 + ... + FPn + FN0 + ... + FNn)

getMacroPrevalence

Macro-average of the prevalence.

Returns number (Pe0 + Pe1 + ... + Pen) / n

getWeightedPrevalence

Weighted prevalence.

Returns number (Pe0 _ s0 + ... + Pen _ sn) / Total

toString

Textual tabular representation of the confusion matrix.

Parameters

• opt Object Options (optional, default {})
  • opt.split boolean Split the classes in half (→ 2 matrices) (optional, default false)
  • opt.clean boolean Remove empty column/row pairs (optional, default false)
  • opt.colours boolean Colourize cells (optional, default true)
  • opt.maxValue (optional, default 100)

Examples

Example output (cf. /src/tests/confusionMatrix.js)

```
Actual \\ Predicted  bug   code  other
------------------  ----  ----  -----
bug                 5.00  0.00  1.00
code                1.00  2.00  0.00
other               0.00  3.00  8.00

Returns string String representation

toTable

console.table version of confusionMatrix.toString().

Parameters

• opt Object Options (optional, default {})
  • opt.split boolean Split the classes in half (→ 2 matrices) (optional, default false)
  • opt.clean boolean Remove empty column/row pairs (optional, default false)
  • opt.colours boolean Colourize cells (optional, default true)
  • opt.maxValue (optional, default 100)

getShortStats

Parameters

• type string Type of stats (micro/macro/weighted average) (optional, default 'micro')

Returns string Short statistics (total, true, false, accuracy, precision, recall and f1)

getStats

Returns {total: number, correctPredictions: number, incorrectPredictions: number, classes: Array<string>, microAvg: Object, macroAvg: Object, results: Object} (Long) statistics

fromData

Creates a confusion matrix from the actual and predictions classes.

Parameters

• actual Array<string> Actual classes
• predictions Array<string> Predicted classes
• classes Array<string> Classes/categories to use (optional, default [])

Returns ConfusionMatrix Filled confusion matrix

Contributors

Maximilian Berkmann 💻 📖 🤔 🚧 📦 🚇 ⚠️ 🛡️

This project follows the all-contributors specification. Contributions of any kind welcome!