class LocalLDAModel extends LDAModel with Serializable
Local LDA model. This model stores only the inferred topics.
- Annotations
- @Since("1.3.0")
- Source
- LDAModel.scala
- Alphabetic
- By Inheritance
- LocalLDAModel
- Serializable
- LDAModel
- Saveable
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- 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.
- Definition Classes
- LocalLDAModel → LDAModel
- Annotations
- @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.
- Definition Classes
- LDAModel
- Annotations
- @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.
- Definition Classes
- LocalLDAModel → LDAModel
- Annotations
- @Since("1.5.0")
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- 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 test-time computations.
- Attributes
- protected[spark]
- Definition Classes
- LocalLDAModel → LDAModel
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def getSeed: Long
Random seed for cluster initialization.
Random seed for cluster initialization.
- Annotations
- @Since("2.4.0")
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def k: Int
Number of topics
Number of topics
- Definition Classes
- LocalLDAModel → LDAModel
- Annotations
- @Since("1.3.0")
- def logLikelihood(documents: JavaPairRDD[Long, Vector]): Double
Java-friendly version of
logLikelihoodJava-friendly version of
logLikelihood- Annotations
- @Since("1.5.0")
- def logLikelihood(documents: RDD[(Long, Vector)]): Double
Calculates a lower bound on the log likelihood of the entire corpus.
Calculates a lower bound on the log likelihood of the entire corpus.
See Equation (16) in original Online LDA paper.
- documents
test corpus to use for calculating log likelihood
- returns
variational lower bound on the log likelihood of the entire corpus
- Annotations
- @Since("1.5.0")
- def logPerplexity(documents: JavaPairRDD[Long, Vector]): Double
Java-friendly version of
logPerplexityJava-friendly version of
logPerplexity- Annotations
- @Since("1.5.0")
- def logPerplexity(documents: RDD[(Long, Vector)]): Double
Calculate an upper bound on perplexity.
Calculate an upper bound on perplexity. (Lower is better.) See Equation (16) in original Online LDA paper.
- documents
test corpus to use for calculating perplexity
- returns
Variational upper bound on log perplexity per token.
- Annotations
- @Since("1.5.0")
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- 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.
- Definition Classes
- LocalLDAModel → Saveable
- Annotations
- @Since("1.5.0")
- def setSeed(seed: Long): LocalLDAModel.this.type
Set the random seed for cluster initialization.
Set the random seed for cluster initialization.
- Annotations
- @Since("2.4.0")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- 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
- LocalLDAModel → LDAModel
- Annotations
- @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 topicDistribution(document: Vector): Vector
Predicts the topic mixture distribution for a document (often called "theta" in the literature).
Predicts the topic mixture distribution for a document (often called "theta" in the literature). Returns a vector of zeros for an empty document.
Note this means to allow quick query for single document. For batch documents, please refer to
topicDistributions()to avoid overhead.- document
document to predict topic mixture distributions for
- returns
topic mixture distribution for the document
- Annotations
- @Since("2.0.0")
- def topicDistributions(documents: JavaPairRDD[Long, Vector]): JavaPairRDD[Long, Vector]
Java-friendly version of
topicDistributionsJava-friendly version of
topicDistributions- Annotations
- @Since("1.4.1")
- def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)]
Predicts the topic mixture distribution for each document (often called "theta" in the literature).
Predicts the topic mixture distribution for each document (often called "theta" in the literature). Returns a vector of zeros for an empty document.
This uses a variational approximation following Hoffman et al. (2010), where the approximate distribution is called "gamma." Technically, this method returns this approximation "gamma" for each document.
- documents
documents to predict topic mixture distributions for
- returns
An RDD of (document ID, topic mixture distribution for document)
- Annotations
- @Since("1.3.0")
- val topics: Matrix
- Annotations
- @Since("1.3.0")
- 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.
- Definition Classes
- LocalLDAModel → LDAModel
- Annotations
- @Since("1.3.0")
- def vocabSize: Int
Vocabulary size (number of terms or terms in the vocabulary)
Vocabulary size (number of terms or terms in the vocabulary)
- Definition Classes
- LocalLDAModel → LDAModel
- Annotations
- @Since("1.3.0")
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)