JSDoc: Class: LDA

Class: LDA

eclairjs/ml/clustering.LDA

Latent Dirichlet Allocation (LDA), a topic model designed for text documents. Terminology: - "term" = "word": an element of the vocabulary - "token": instance of a term appearing in a document - "topic": multinomial distribution over terms representing some concept - "document": one piece of text, corresponding to one row in the input data Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. Input data (featuresCol): LDA is given a collection of documents as input data, via the featuresCol parameter. Each document is specified as a Vector of length vocabSize, where each entry is the count for the corresponding term (word) in the document. Feature transformers such as [[org.apache.spark.ml.feature.Tokenizer]] and CountVectorizer can be useful for converting text to word count vectors.

Constructor

new LDA(uidopt)

Parameters:

Name	Type	Attributes	Description
`uid`	string	<optional>

Source:

ml/clustering/LDA.js, line 52

See:

Latent Dirichlet allocation(Wikipedia)

Extends

module:eclairjs/ml.Estimator

Methods

(static) load(path) → {LDA}

Parameters:

Name	Type	Description
`path`	string

Source:

ml/clustering/LDA.js, line 347

Returns:

Type: LDA

copy(extra) → {module:eclairjs/mllib/clustering.LDA}

Parameters:

Name	Type	Description
`extra`	module:eclairjs/ml/param.ParamMap

Overrides:

module:eclairjs/ml.Estimator#copy

Source:

ml/clustering/LDA.js, line 276

Returns:

Type: module:eclairjs/mllib/clustering.LDA

extractParamMap() → {module:eclairjs/ml/param.ParamMap}

Inherited From:

module:eclairjs/ml.Estimator#extractParamMap

Source:

ml/Estimator.js, line 70

Returns:

Type: module:eclairjs/ml/param.ParamMap

fit(dataset) → {module:eclairjs/mllib/clustering.LDAModel}

Parameters:

Name	Type	Description
`dataset`	module:eclairjs/sql.Dataset

Overrides:

module:eclairjs/ml.Estimator#fit

Source:

ml/clustering/LDA.js, line 308

Returns:

Type: module:eclairjs/mllib/clustering.LDAModel

setCheckpointInterval(value) → {module:eclairjs/mllib/clustering.LDA}

Parameters:

Name	Type	Description
`value`	number

Source:

ml/clustering/LDA.js, line 126

Returns:

Type: module:eclairjs/mllib/clustering.LDA

setDocConcentration(value) → {module:eclairjs/mllib/clustering.LDA}

Parameters:

Name	Type	Description
`value`	Array.<number> \| number

Source:

ml/clustering/LDA.js, line 156

Returns:

Type: module:eclairjs/mllib/clustering.LDA

setFeaturesCol(value) → {module:eclairjs/mllib/clustering.LDA}

The features for LDA should be a Vector representing the word counts in a document. The vector should be of length vocabSize, with counts for each term (word).