https://github.com/all-contributors/ac-learn

ML platform for all contributors
https://github.com/all-contributors/ac-learn
all-contributors classification learning ml
Last synced: 3 months ago
JSON representation
ML platform for all contributors
Host: GitHub
URL: https://github.com/all-contributors/ac-learn
Owner: all-contributors
License: mit
Created: 2019-04-17T13:23:58.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2024-12-13T00:33:24.000Z (about 1 year ago)
Last Synced: 2025-03-20T23:41:51.909Z (10 months ago)
Topics: all-contributors, classification, learning, ml
Language: JavaScript
Size: 19.2 MB
Stars: 5
Watchers: 5
Forks: 4
Open Issues: 7
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

          

- [ac-learn](#ac-learn)

  - [Install](#install)

  - [Documentation](#documentation)

    - [Learner](#learner)

    - [ConfusionMatrix](#confusionmatrix)

  - [Contributors](#contributors)

# ac-learn

Where the All Contributors machine can learn about your contributions.

## Install

```bash

yarn add ac-learn --save

#or

npm i -D ac-learn

```

## Usage

```js

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](#learner)

  - [Parameters](#parameters)

  - [Examples](#examples)

  - [train](#train)

    - [Parameters](#parameters-1)

  - [eval](#eval)

    - [Parameters](#parameters-2)

  - [serializeClassifier](#serializeclassifier)

  - [serializeAndSaveClassifier](#serializeandsaveclassifier)

    - [Parameters](#parameters-3)

  - [deserializeClassifier](#deserializeclassifier)

    - [Parameters](#parameters-4)

  - [loadAndDeserializeClassifier](#loadanddeserializeclassifier)

    - [Parameters](#parameters-5)

  - [classify](#classify)

    - [Parameters](#parameters-6)

  - [crossValidate](#crossvalidate)

    - [Parameters](#parameters-7)

  - [backClassify](#backclassify)

    - [Parameters](#parameters-8)

  - [toJSON](#tojson)

  - [fromJSON](#fromjson)

    - [Parameters](#parameters-9)

  - [getCategoryPartition](#getcategorypartition)

    - [Parameters](#parameters-10)

  - [getStats](#getstats)

    - [Parameters](#parameters-11)

- [ConfusionMatrix](#confusionmatrix)

  - [addEntry](#addentry)

    - [Parameters](#parameters-12)

  - [setEntry](#setentry)

    - [Parameters](#parameters-13)

  - [getEntry](#getentry)

    - [Parameters](#parameters-14)

  - [getTotal](#gettotal)

  - [getTP](#gettp)

    - [Parameters](#parameters-15)

  - [getFP](#getfp)

    - [Parameters](#parameters-16)

  - [getFN](#getfn)

    - [Parameters](#parameters-17)

  - [getTN](#gettn)

    - [Parameters](#parameters-18)

  - [getDiagonal](#getdiagonal)

  - [getTrue](#gettrue)

  - [getFalse](#getfalse)

  - [getPositive](#getpositive)

    - [Parameters](#parameters-19)

  - [getNegative](#getnegative)

    - [Parameters](#parameters-20)

  - [getPredPositive](#getpredpositive)

    - [Parameters](#parameters-21)

  - [getPredNegative](#getprednegative)

    - [Parameters](#parameters-22)

  - [getSupport](#getsupport)

    - [Parameters](#parameters-23)

  - [getAccuracy](#getaccuracy)

    - [Parameters](#parameters-24)

  - [getMicroAccuracy](#getmicroaccuracy)

  - [getMacroAccuracy](#getmacroaccuracy)

  - [getWeightedAccuracy](#getweightedaccuracy)

  - [getTotalPositiveRate](#gettotalpositiverate)

    - [Parameters](#parameters-25)

  - [getMicroRecall](#getmicrorecall)

  - [getMacroRecall](#getmacrorecall)

  - [getWeightedRecall](#getweightedrecall)

  - [getPositivePredictiveValue](#getpositivepredictivevalue)

    - [Parameters](#parameters-26)

  - [getPositivePredictiveValue](#getpositivepredictivevalue-1)

    - [Parameters](#parameters-27)

  - [getMicroPrecision](#getmicroprecision)

  - [getMacroPrecision](#getmacroprecision)

  - [getWeightedPrecision](#getweightedprecision)

  - [getMicroF1](#getmicrof1)

  - [getMacroF1](#getmacrof1)

  - [getWeightedF1](#getweightedf1)

  - [getFalseNegativeRate](#getfalsenegativerate)

    - [Parameters](#parameters-28)

  - [getMicroMissRate](#getmicromissrate)

  - [getMacroMissRate](#getmacromissrate)

  - [getWeightedMissRate](#getweightedmissrate)

  - [getFalsePositiveRate](#getfalsepositiverate)

    - [Parameters](#parameters-29)

  - [getMicroFallOut](#getmicrofallout)

  - [getMacroFallOut](#getmacrofallout)

  - [getWeightedFallOut](#getweightedfallout)

  - [getTrueNegativeRate](#gettruenegativerate)

    - [Parameters](#parameters-30)

  - [getMicroSpecificity](#getmicrospecificity)

  - [getMacroSpecificity](#getmacrospecificity)

  - [getWeightedSpecificity](#getweightedspecificity)

  - [getPrevalence](#getprevalence)

    - [Parameters](#parameters-31)

  - [getMicroPrevalence](#getmicroprevalence)

  - [getMacroPrevalence](#getmacroprevalence)

  - [getWeightedPrevalence](#getweightedprevalence)

  - [toString](#tostring)

    - [Parameters](#parameters-32)

    - [Examples](#examples-1)

  - [toTable](#totable)

    - [Parameters](#parameters-33)

  - [getShortStats](#getshortstats)

    - [Parameters](#parameters-34)

  - [getStats](#getstats-1)

  - [fromData](#fromdata)

    - [Parameters](#parameters-35)

### Learner

NodeJS Classification-based learner.

#### Parameters

- `opts`

  **[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

  Options.

  - `opts.dataset`

    **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)>**

    Dataset (for training and testing) (optional, default

    `require('./conv')('io')`)

  - `opts.splits`

    **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

    Dataset split percentage for the training/validation set (default:

    70%/15%/15%) (optional, default `[.7,.15]`)

  - `opts.classifier` **function ():

    [Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

    Classifier builder function (optional, default `classifierBuilder`)

  - `opts.pastTrainingSamples`

    **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)>**

    Past training samples for the classifier (optional, default `[]`)

  - `opts.classes`

    **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**

    List of classes (categories) (optional, default `require('./categories')`)

#### Examples

Using pre-defined data

```javascript

const learner = new Learner()

```

Using a custom dataset

```javascript

const learner = new Learner({

  dataset: [

    {input: 'something bad', output: 'bad'},

    {input: 'a good thing', output: 'good'},

  ],

})

```

Using a specified classifier function

```javascript

const learner = new Learner({

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

})

```

Changing the train/test split percentage

```javascript

const learner = new Learner({

  splits: [0.6, 0.2],

})

```

(Re-)Using past-training samples

```javascript

const learner = new Learner({

  pastTrainingSamples: [

    {input: 'something bad', output: 'bad'},

    {input: 'a good thing', output: 'good'},

  ],

})

```

#### train

##### Parameters

- `trainSet`

  **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)>**

  Training set (optional, default `this.trainSet`)

#### eval

##### Parameters

- `log`

  **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

  Log events (optional, default `false`)

Returns

**[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

Statistics from a confusion matrix

#### serializeClassifier

Returns

**[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

Serialized classifier

#### serializeAndSaveClassifier

##### Parameters

- `file`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Filename (optional, default `'classifier.json'`)

Returns

**[Promise](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Promise)<([string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)

\|

[Error](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Error))>**

Serialized classifier

#### deserializeClassifier

##### Parameters

- `serializedClassifier`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  .

Returns

**[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

Deserialized classifier

#### loadAndDeserializeClassifier

##### Parameters

- `file`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Filename (optional, default `'classifier.json'`)

Returns

**[Promise](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Promise)<([string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)

\|

[Error](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Error))>**

Deserialized classifier

#### classify

##### Parameters

- `data` **{input: any, output: any}** Data to classify

Returns

**[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**

Classes

#### crossValidate

##### Parameters

- `numOfFolds`

  **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

  Cross-validation folds (optional, default `5`)

- `verboseLevel`

  **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

  Verbosity level on limdu's explainations (optional, default `0`)

- `log`

  **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

  Cross-validation logging (optional, default `false`)

Returns **{microAvg:

[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object),

macroAvg:

[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)}**

Averages

#### backClassify

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Category name.

Returns

**[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**

Labels associated with `category`

#### toJSON

JSON representation of the learner with the serialized classification model.

Returns

**[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

JSON representation

#### fromJSON

##### Parameters

- `json`

  **([JSON](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/JSON)

  \|

  [Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object))**

  JSON form

Returns **[Learner](#learner)** Generated learner from `json`

#### getCategoryPartition

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

the associated dataset.

##### Parameters

- `log`

  **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

  Log events (optional, default `false`)

- `outputFile`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Filename for the output (to be used by chart.html) (optional, default `''`)

Returns

**[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String),

{overall:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number),

test:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number),

validation:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number),

train:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)}>**

Partitions

#### getStats

##### Parameters

- `log`

  **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

  Log events (optional, default `false`)

- `categoryPartitionOutput`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Filename for the output of the category partitions. (optional, default `''`)

Returns

**[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

Statistics

### ConfusionMatrix

Multi-class focused confusion matrix.

#### addEntry

##### Parameters

- `actual`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Actual class

- `predicted`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Predicted class

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

Updated entry

#### setEntry

##### Parameters

- `actual`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Actual class

- `predicted`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Predicted class

- `val`

  **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

  New entry

#### getEntry

##### Parameters

- `actual`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Actual class

- `predicted`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Predicted class

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

Entry

#### getTotal

Get the total count of **all** entries.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

Total count

#### getTP

Number of elements _in_ the `category` class correctly predicted.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

True Positives

#### getFP

Number of elements that _aren't in_ the `category` class but predicted as such.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

False Positives

#### getFN

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

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

False Negatives

#### getTN

Number of elements that _aren't in_ the `category` class correctly predicted.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

True Negatives

#### getDiagonal

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

Returns

**[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)>**

Numbers in the diagonal

#### getTrue

Number of correct (truthful) predictions.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TP

#### getFalse

Number of incorrect predictions.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

FP + FN

#### getPositive

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

`category` class).

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TP + FN

#### getNegative

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

the `category` class).

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TN + FP

#### getPredPositive

Number of predicted "positive" elements (i.e. elements guessed as belonging to

the `category` class).

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TP + FN

#### getPredNegative

Number of predicted "negative" elements (i.e. elements guessed as not belonging

to the `category` class).

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TN + FP

#### getSupport

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

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category to look at

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

Support value

#### getAccuracy

Prediction accuracy for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMicroAccuracy

Micro-average of accuracy.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

(TP0 + ... + TPn + TN0 + ... + TNn) / (TP0 + ... + TPn + TN0 + ... + TNn + FP0 +

... + FPn + FN0 + ... + FNn)

#### getMacroAccuracy

Macro-average of accuracy.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

(A0 + ...+ An_1) / n

#### getWeightedAccuracy

Weighted accuracy.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getTotalPositiveRate

Predicition recall.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TP / (TP + FN)

#### getMicroRecall

Micro-average of recall.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMacroRecall

Macro-average of recall.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedRecall

Weighted recalll.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getPositivePredictiveValue

Prediction precision for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TP / (TP + FP)

#### getPositivePredictiveValue

Prediction F1 score for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMicroPrecision

Micro-average of the precision.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMacroPrecision

Macro-average of the precsion.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedPrecision

Weighted precision.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMicroF1

Micro-average of the F1 score.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

... + FPn))

#### getMacroF1

Macro-average of the F1 score.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedF1

Weighted F1.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

(F0*1 * s0 + ... + Fn*1 * sn) / Total

#### getFalseNegativeRate

Miss rates on predictions for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

FN / (TP + FN)

#### getMicroMissRate

Micro-average of the miss rate.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMacroMissRate

Macro-average of the miss rate.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedMissRate

Weighted miss rate.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getFalsePositiveRate

Fall out (false alarm) on predictions for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

FP / (FP + TN)

#### getMicroFallOut

Micro-average of the fall out.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMacroFallOut

Macro-average of the fall out.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedFallOut

Weighted fall out.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getTrueNegativeRate

Specificity on predictions for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

TN / (FP + TN)

#### getMicroSpecificity

Micro-average of the specificity.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMacroSpecificity

Macro-average of the specificity.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedSpecificity

Weighted specificity.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getPrevalence

Prevalence on predictions for `category`.

##### Parameters

- `category`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Class/category considered as positive

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getMicroPrevalence

Micro-average of the prevalence.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

... + FPn + FN0 + ... + FNn)

#### getMacroPrevalence

Macro-average of the prevalence.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### getWeightedPrevalence

Weighted prevalence.

Returns

**[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)**

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

#### toString

Textual tabular representation of the confusion matrix.

##### Parameters

- `opt`

  **[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

  Options (optional, default `{}`)

  - `opt.split`

    **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

    Split the classes in half (→ 2 matrices) (optional, default `false`)

  - `opt.clean`

    **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

    Remove empty column/row pairs (optional, default `false`)

  - `opt.colours`

    **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

    Colourize cells (optional, default `true`)

  - `opt.maxValue` (optional, default `100`)

##### Examples

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

````javascript

```

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](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

String representation

#### toTable

`console.table` version of `confusionMatrix.toString()`.

##### Parameters

- `opt`

  **[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)**

  Options (optional, default `{}`)

  - `opt.split`

    **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

    Split the classes in half (→ 2 matrices) (optional, default `false`)

  - `opt.clean`

    **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

    Remove empty column/row pairs (optional, default `false`)

  - `opt.colours`

    **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)**

    Colourize cells (optional, default `true`)

  - `opt.maxValue` (optional, default `100`)

#### getShortStats

##### Parameters

- `type`

  **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

  Type of stats (`micro`/`macro`/`weighted` average) (optional, default

  `'micro'`)

Returns

**[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)**

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

#### getStats

Returns **{total:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number),

correctPredictions:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number),

incorrectPredictions:

[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number),

classes:

[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>,

microAvg:

[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object),

macroAvg:

[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object),

results:

[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)}**

(Long) statistics

#### fromData

Creates a confusion matrix from the `actual` and `predictions` classes.

##### Parameters

- `actual`

  **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**

  Actual classes

- `predictions`

  **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**

  Predicted classes

- `classes`

  **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)<[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)>**

  Classes/categories to use (optional, default `[]`)

Returns **[ConfusionMatrix](#confusionmatrix)** Filled confusion matrix

## Contributors

  

    

      
_{Maximilian Berkmann}
💻 📖 🤔 🚧 📦 🚇 ⚠️ 🛡️

      
_{Angel Aviel Domaoan}
💻 🚧 👀

      
_Dependabot
🛡️

      
_{Gregor Martynus}
👀

    

  

This project follows the

[all-contributors](https://github.com/all-contributors/all-contributors)

specification. Contributions of any kind welcome!
ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/all-contributors/ac-learn

Awesome Lists containing this project

README
