DistributedLDAModel (Spark 3.5.3 JavaDoc)

Object
- org.apache.spark.mllib.clustering.LDAModel
- - org.apache.spark.mllib.clustering.DistributedLDAModel

All Implemented Interfaces:

Saveable
```
public class DistributedLDAModel
extends LDAModel
```
Distributed LDA model. This model stores the inferred topics, the full training dataset, and the topic distributions.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`scala.Tuple2<int[],double[]>[]`	`describeTopics(int maxTermsPerTopic)` Return the topics described by weighted terms.
`Vector`	`docConcentration()` Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
`JavaRDD<scala.Tuple3<Long,int[],int[]>>`	`javaTopicAssignments()`
`JavaPairRDD<Long,Vector>`	`javaTopicDistributions()` Java-friendly version of `topicDistributions`
`JavaRDD<scala.Tuple3<Long,int[],double[]>>`	`javaTopTopicsPerDocument(int k)` Java-friendly version of `topTopicsPerDocument`
`int`	`k()` Number of topics
`static DistributedLDAModel`	`load(SparkContext sc, String path)`
`double`	`logLikelihood()`
`double`	`logPrior()`
`void`	`save(SparkContext sc, String path)` Save this model to the given path.
`LocalLDAModel`	`toLocal()` Convert model to a local model.
`scala.Tuple2<long[],double[]>[]`	`topDocumentsPerTopic(int maxDocumentsPerTopic)` Return the top documents for each topic
`RDD<scala.Tuple3<Object,int[],int[]>>`	`topicAssignments()`
`double`	`topicConcentration()` Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
`RDD<scala.Tuple2<Object,Vector>>`	`topicDistributions()` For each document in the training set, return the distribution over topics for that document ("theta_doc").
`Matrix`	`topicsMatrix()` Inferred topics, where each topic is represented by a distribution over terms.
`RDD<scala.Tuple3<Object,int[],double[]>>`	`topTopicsPerDocument(int k)` For each document, return the top k weighted topics for that document and their weights.
`int`	`vocabSize()` Vocabulary size (number of terms or terms in the vocabulary)

Methods inherited from class org.apache.spark.mllib.clustering.LDAModel
describeTopics

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - load
```
public static DistributedLDAModel load(SparkContext sc,
                                       String path)
```
  - k
```
public int k()
```
    Description copied from class: LDAModel
    
    Number of topics
    
    Specified by:
    
    k in class LDAModel
  - vocabSize
```
public int vocabSize()
```
    Description copied from class: LDAModel
    
    Vocabulary size (number of terms or terms in the vocabulary)
    
    Specified by:
    
    vocabSize in class LDAModel
  - docConcentration
```
public Vector docConcentration()
```
    Description copied from class: LDAModel
    
    Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
    This is the parameter to a Dirichlet distribution.
    
    Specified by:
    
    docConcentration in class LDAModel
    
    Returns:
    
    (undocumented)
  - topicConcentration
```
public double topicConcentration()
```
    Description copied from class: LDAModel
    
    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.
    
    Specified by:
    
    topicConcentration in class LDAModel
    
    Returns:
    
    (undocumented)
  - toLocal
```
public LocalLDAModel toLocal()
```
    Convert model to a local model. The local model stores the inferred topics but not the topic distributions for training documents.
    
    Returns:
    
    (undocumented)
  - topicsMatrix
```
public Matrix topicsMatrix()
```
    Description copied from class: LDAModel
    
    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.
    
    Specified by:
    
    topicsMatrix in class LDAModel
    
    Returns:
    
    (undocumented)
  - describeTopics
```
public scala.Tuple2<int[],double[]>[] describeTopics(int maxTermsPerTopic)
```
    Description copied from class: LDAModel
    
    Return the topics described by weighted terms.
    
    Specified by:
    
    describeTopics in class LDAModel
    
    Parameters:
    
    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.
  - topDocumentsPerTopic
```
public scala.Tuple2<long[],double[]>[] topDocumentsPerTopic(int maxDocumentsPerTopic)
```
    Return the top documents for each topic
    
    Parameters:
    
    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.
  - topicAssignments
```
public RDD<scala.Tuple3<Object,int[],int[]>> topicAssignments()
```
  - javaTopicAssignments
```
public JavaRDD<scala.Tuple3<Long,int[],int[]>> javaTopicAssignments()
```
  - logLikelihood
```
public double logLikelihood()
```
  - logPrior
```
public double logPrior()
```
  - topicDistributions
```
public RDD<scala.Tuple2<Object,Vector>> topicDistributions()
```
    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
  - javaTopicDistributions
```
public JavaPairRDD<Long,Vector> javaTopicDistributions()
```
    Java-friendly version of topicDistributions
    
    Returns:
    
    (undocumented)
  - topTopicsPerDocument
```
public RDD<scala.Tuple3<Object,int[],double[]>> topTopicsPerDocument(int k)
```
    For each document, return the top k weighted topics for that document and their weights.
    
    Parameters:
    
    k - (undocumented)
    
    Returns:
    
    RDD of (doc ID, topic indices, topic weights)
  - javaTopTopicsPerDocument
```
public JavaRDD<scala.Tuple3<Long,int[],double[]>> javaTopTopicsPerDocument(int k)
```
    Java-friendly version of topTopicsPerDocument
    
    Parameters:
    
    k - (undocumented)
    
    Returns:
    
    (undocumented)
  - save
```
public void save(SparkContext sc,
                 String path)
```
    Description copied from interface: Saveable
    
    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.
    
    Parameters:
    
    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.

Class DistributedLDAModel

Method Summary

Methods inherited from class org.apache.spark.mllib.clustering.LDAModel

Methods inherited from class Object

Method Detail

load

k

vocabSize

docConcentration

topicConcentration

toLocal

topicsMatrix

describeTopics

topDocumentsPerTopic

topicAssignments

javaTopicAssignments

logLikelihood

logPrior

topicDistributions

javaTopicDistributions

topTopicsPerDocument

javaTopTopicsPerDocument

save