class DistributedLDAModel extends LDAModel
Distributed LDA model. This model stores the inferred topics, the full training dataset, and the topic distributions.
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 @Since( "1.3.0" )
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 LDAModel
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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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 DistributedLDAModel → LDAModel
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 @Since( "1.3.0" )

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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 LDAModel
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 @Since( "1.3.0" )

val
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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 DistributedLDAModel → LDAModel
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 @Since( "1.5.0" )

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val
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 testtime computations.
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 protected[clustering]
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 DistributedLDAModel → LDAModel

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def
javaTopTopicsPerDocument(k: Int): JavaRDD[(Long, Array[Int], Array[Double])]
Javafriendly version of topTopicsPerDocument
Javafriendly version of topTopicsPerDocument
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 @Since( "1.5.0" )

lazy val
javaTopicAssignments: JavaRDD[(Long, Array[Int], Array[Int])]
Javafriendly version of topicAssignments
Javafriendly version of topicAssignments
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 @Since( "1.5.0" )

def
javaTopicDistributions: JavaPairRDD[Long, Vector]
Javafriendly version of topicDistributions
Javafriendly version of topicDistributions
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 @Since( "1.4.1" )

val
k: Int
Number of topics
Number of topics
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 DistributedLDAModel → LDAModel
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 @Since( "1.3.0" )

lazy val
logLikelihood: Double
Log likelihood of the observed tokens in the training set, given the current parameter estimates: log P(docs  topics, topic distributions for docs, alpha, eta)
Log likelihood of the observed tokens in the training set, given the current parameter estimates: log P(docs  topics, topic distributions for docs, alpha, eta)
Note:
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 @Since( "1.3.0" )

lazy val
logPrior: Double
Log probability of the current parameter estimate: log P(topics, topic distributions for docs  alpha, eta)
Log probability of the current parameter estimate: log P(topics, topic distributions for docs  alpha, eta)
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 @Since( "1.3.0" )

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def
save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
 humanreadable (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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 DistributedLDAModel → Saveable
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 @Since( "1.5.0" )

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def
toLocal: LocalLDAModel
Convert model to a local model.
Convert model to a local model. The local model stores the inferred topics but not the topic distributions for training documents.
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 @Since( "1.3.0" )

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def
topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])]
Return the top documents for each topic
Return the top documents for each topic
 maxDocumentsPerTopic
Maximum number of documents to collect for each topic.
 returns
Array over topics. Each element represent as a pair of matching arrays: (IDs for the documents, weights of the topic in these documents). For each topic, documents are sorted in order of decreasing topic weights.
 Annotations
 @Since( "1.5.0" )

def
topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])]
For each document, return the top k weighted topics for that document and their weights.
For each document, return the top k weighted topics for that document and their weights.
 returns
RDD of (doc ID, topic indices, topic weights)
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 @Since( "1.5.0" )

lazy val
topicAssignments: RDD[(Long, Array[Int], Array[Int])]
Return the top topic for each (doc, term) pair.
Return the top topic for each (doc, term) pair. I.e., for each document, what is the most likely topic generating each term?
 returns
RDD of (doc ID, assignment of top topic index for each term), where the assignment is specified via a pair of zippable arrays (term indices, topic indices). Note that terms will be omitted if not present in the document.
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 @Since( "1.5.0" )

val
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.
 Definition Classes
 DistributedLDAModel → LDAModel
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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.

def
topicDistributions: RDD[(Long, Vector)]
For each document in the training set, return the distribution over topics for that document ("theta_doc").
For each document in the training set, return the distribution over topics for that document ("theta_doc").
 returns
RDD of (document ID, topic distribution) pairs
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 @Since( "1.3.0" )

lazy val
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.
WARNING: This matrix is collected from an RDD. Beware memory usage when vocabSize, k are large.
 Definition Classes
 DistributedLDAModel → LDAModel
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 @Since( "1.3.0" )

val
vocabSize: Int
Vocabulary size (number of terms or terms in the vocabulary)
Vocabulary size (number of terms or terms in the vocabulary)
 Definition Classes
 DistributedLDAModel → LDAModel
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 @Since( "1.3.0" )

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