abstract class LDAModel extends Saveable
Latent Dirichlet Allocation (LDA) model.
This abstraction permits for different underlying representations, including local and distributed data structures.
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- LDAModel.scala
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- abstract def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])]
Return the topics described by weighted terms.
Return the topics described by weighted terms.
- maxTermsPerTopic
Maximum number of terms to collect for each topic.
- returns
Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.
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- abstract def docConcentration: Vector
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
This is the parameter to a Dirichlet distribution.
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- @Since("1.5.0")
- abstract def gammaShape: Double
Shape parameter for random initialization of variational parameter gamma.
Shape parameter for random initialization of variational parameter gamma. Used for variational inference for perplexity and other test-time computations.
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- abstract def k: Int
Number of topics
Number of topics
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- abstract def save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using
Loader.load.- sc
Spark context used to save model data.
- path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
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- abstract def topicConcentration: Double
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
This is the parameter to a symmetric Dirichlet distribution.
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- @Since("1.5.0")
- Note
The topics' distributions over terms are called "beta" in the original LDA paper by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
- abstract def topicsMatrix: Matrix
Inferred topics, where each topic is represented by a distribution over terms.
Inferred topics, where each topic is represented by a distribution over terms. This is a matrix of size vocabSize x k, where each column is a topic. No guarantees are given about the ordering of the topics.
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- abstract def vocabSize: Int
Vocabulary size (number of terms or terms in the vocabulary)
Vocabulary size (number of terms or terms in the vocabulary)
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- def describeTopics(): Array[(Array[Int], Array[Double])]
Return the topics described by weighted terms.
Return the topics described by weighted terms.
WARNING: If vocabSize and k are large, this can return a large object!
- returns
Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.
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