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Security review needed
popularity
Limited
maintenance
Inactive
community
Limited

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Security

Security review needed

All security vulnerabilities belong to production dependencies of direct and indirect packages.

Security and license risk for significant versions

Version				Vulnerabilities	License Risk
1.7.0	\|	08/2020	Popular	0 C 0 H 0 M 0 L	0 H 0 M 0 L
1.6.0	\|	08/2020		0 C 0 H 0 M 0 L	0 H 0 M 0 L
1.5.1	\|	08/2019		0 C 0 H 0 M 0 L	0 H 0 M 0 L
1.3.1	\|	07/2019		0 C 0 H 0 M 0 L	0 H 0 M 0 L
1.4.0	\|	07/2019		0 C 0 H 0 M 0 L	0 H 0 M 0 L

License

MIT

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Popularity

Limited

Weekly Downloads (1)

GitHub Stars: 5
Forks: 4
Contributors: 5

Direct Usage Popularity

The npm package ac-learn receives a total of 1 downloads a week. As such, we scored ac-learn popularity level to be Limited.

Based on project statistics from the GitHub repository for the npm package ac-learn, we found that it has been starred 5 times.

Downloads are calculated as moving averages for a period of the last 12 months, excluding weekends and known missing data points.

Community

Limited

Readme.md: Yes
Contributing.md: No
Code of Conduct: Yes
Contributors: 5
Funding: No

This project has seen only 10 or less contributors.

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Maintenance

Inactive

Commit Frequency

Open Issues: 2
Open PR: 4
Last Release: 4 years ago
Last Commit: 6 months ago

Further analysis of the maintenance status of ac-learn based on released npm versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for ac-learn is that it hasn't seen any new versions released to npm in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

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Package

Node.js Compatibility: >=8

Age: 6 years
Dependencies: 10 Direct
Versions: 15
Install Size: 118 kB
Dist-tags: 1
# of Files: 15
Maintainers: 1
TS Typings: No

ac-learn has more than a single and default latest tag published for the npm package. This means, there may be other tags available for this package, such as next to indicate future releases, or stable to indicate stable releases.

Readme

ac-learn

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

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!

ac-learn dependencies

@babel/runtime camel-case chalk clui easy-table javascript-lemmatizer limdu nclr serialization tvt-split

ac-learn development dependencies

anychart-nodejs codecov concurrently cz-conventional-changelog documentation eslint-config-kentcdodds git-cz jsdom kcd-scripts plotly.js-dist semantic-release snyk

FAQs

What is ac-learn?

ML platform for All Contributors. Visit Snyk Advisor to see a full health score report for ac-learn, including popularity, security, maintenance & community analysis.

Is ac-learn popular?

The npm package ac-learn receives a total of 1 weekly downloads. As such, ac-learn popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is ac-learn well maintained?

We found indications that ac-learn is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is ac-learn safe to use?

While scanning the latest version of ac-learn, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 9 December-2024, at 02:30 (UTC).

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Readme

ac-learn

Install

Usage

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