Package org.apache.spark.ml.clustering

Class LDA

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<LDAModel>
org.apache.spark.ml.clustering.LDA
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, LDAParams, Params, HasCheckpointInterval, HasFeaturesCol, HasMaxIter, HasSeed, DefaultParamsWritable, Identifiable, MLWritable
public class LDA extends Estimator<LDAModel> implements LDAParams, DefaultParamsWritable
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 Tokenizer and CountVectorizer can be useful for converting text to word count vectors.

See Also:

  • Constructor Details

    • LDA

      public LDA(String uid)

    • LDA

      public LDA()

  • Method Details

    • read

      public static MLReader<LDA> read()

    • load

      public static LDA load(String path)

    • k

      public final IntParam k()
      Description copied from interface: LDAParams
      Param for the number of topics (clusters) to infer. Must be &gt; 1. Default: 10.

      Specified by:
      k in interface LDAParams
      Returns:
      (undocumented)

    • docConcentration

      public final DoubleArrayParam docConcentration()
      Description copied from interface: LDAParams
      Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").

      This is the parameter to a Dirichlet distribution, where larger values mean more smoothing (more regularization).

      If not set by the user, then docConcentration is set automatically. If set to singleton vector [alpha], then alpha is replicated to a vector of length k in fitting. Otherwise, the LDAParams.docConcentration() vector must be length k. (default = automatic)

      Optimizer-specific parameter settings: - EM - Currently only supports symmetric distributions, so all values in the vector should be the same. - Values should be greater than 1.0 - default = uniformly (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows from Asuncion et al. (2009), who recommend a +1 adjustment for EM. - Online - Values should be greater than or equal to 0 - default = uniformly (1.0 / k), following the implementation from here.

      Specified by:
      docConcentration in interface LDAParams
      Returns:
      (undocumented)

    • topicConcentration

      public final DoubleParam topicConcentration()
      Description copied from interface: LDAParams
      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.

      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.

      If not set by the user, then topicConcentration is set automatically. (default = automatic)

      Optimizer-specific parameter settings: - EM - Value should be greater than 1.0 - default = 0.1 + 1, where 0.1 gives a small amount of smoothing and +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - Online - Value should be greater than or equal to 0 - default = (1.0 / k), following the implementation from here.

      Specified by:
      topicConcentration in interface LDAParams
      Returns:
      (undocumented)

    • supportedOptimizers

      public final String[] supportedOptimizers()
      Description copied from interface: LDAParams
      Supported values for Param LDAParams.optimizer().
      Specified by:
      supportedOptimizers in interface LDAParams

    • optimizer

      public final Param<String> optimizer()
      Description copied from interface: LDAParams
      Optimizer or inference algorithm used to estimate the LDA model. Currently supported (case-insensitive): - "online": Online Variational Bayes (default) - "em": Expectation-Maximization

      For details, see the following papers: - Online LDA: Hoffman, Blei and Bach. "Online Learning for Latent Dirichlet Allocation." Neural Information Processing Systems, 2010. See here - EM: Asuncion et al. "On Smoothing and Inference for Topic Models." Uncertainty in Artificial Intelligence, 2009. See here

      Specified by:
      optimizer in interface LDAParams
      Returns:
      (undocumented)

    • topicDistributionCol

      public final Param<String> topicDistributionCol()
      Description copied from interface: LDAParams
      Output column with estimates of 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.

      Specified by:
      topicDistributionCol in interface LDAParams
      Returns:
      (undocumented)

    • learningOffset

      public final DoubleParam learningOffset()
      Description copied from interface: LDAParams
      For Online optimizer only: LDAParams.optimizer() = "online".

      A (positive) learning parameter that downweights early iterations. Larger values make early iterations count less. This is called "tau0" in the Online LDA paper (Hoffman et al., 2010) Default: 1024, following Hoffman et al.

      Specified by:
      learningOffset in interface LDAParams
      Returns:
      (undocumented)

    • learningDecay

      public final DoubleParam learningDecay()
      Description copied from interface: LDAParams
      For Online optimizer only: LDAParams.optimizer() = "online".

      Learning rate, set as an exponential decay rate. This should be between (0.5, 1.0] to guarantee asymptotic convergence. This is called "kappa" in the Online LDA paper (Hoffman et al., 2010). Default: 0.51, based on Hoffman et al.

      Specified by:
      learningDecay in interface LDAParams
      Returns:
      (undocumented)

    • subsamplingRate

      public final DoubleParam subsamplingRate()
      Description copied from interface: LDAParams
      For Online optimizer only: LDAParams.optimizer() = "online".

      Fraction of the corpus to be sampled and used in each iteration of mini-batch gradient descent, in range (0, 1].

      Note that this should be adjusted in synch with LDA.maxIter so the entire corpus is used. Specifically, set both so that maxIterations * miniBatchFraction greater than or equal to 1.

      Note: This is the same as the miniBatchFraction parameter in OnlineLDAOptimizer.

      Default: 0.05, i.e., 5% of total documents.

      Specified by:
      subsamplingRate in interface LDAParams
      Returns:
      (undocumented)

    • optimizeDocConcentration

      public final BooleanParam optimizeDocConcentration()
      Description copied from interface: LDAParams
      For Online optimizer only (currently): LDAParams.optimizer() = "online".

      Indicates whether the docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training. Setting this to true will make the model more expressive and fit the training data better. Default: false

      Specified by:
      optimizeDocConcentration in interface LDAParams
      Returns:
      (undocumented)

    • keepLastCheckpoint

      public final BooleanParam keepLastCheckpoint()
      Description copied from interface: LDAParams
      For EM optimizer only: LDAParams.optimizer() = "em".

      If using checkpointing, this indicates whether to keep the last checkpoint. If false, then the checkpoint will be deleted. Deleting the checkpoint can cause failures if a data partition is lost, so set this bit with care. Note that checkpoints will be cleaned up via reference counting, regardless.

      See DistributedLDAModel.getCheckpointFiles for getting remaining checkpoints and DistributedLDAModel.deleteCheckpointFiles for removing remaining checkpoints.

      Default: true

      Specified by:
      keepLastCheckpoint in interface LDAParams
      Returns:
      (undocumented)

    • checkpointInterval

      public final IntParam checkpointInterval()
      Description copied from interface: HasCheckpointInterval
      Param for set checkpoint interval (&gt;= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.
      Specified by:
      checkpointInterval in interface HasCheckpointInterval
      Returns:
      (undocumented)

    • seed

      public final LongParam seed()
      Description copied from interface: HasSeed
      Param for random seed.
      Specified by:
      seed in interface HasSeed
      Returns:
      (undocumented)

    • maxIter

      public final IntParam maxIter()
      Description copied from interface: HasMaxIter
      Param for maximum number of iterations (&gt;= 0).
      Specified by:
      maxIter in interface HasMaxIter
      Returns:
      (undocumented)

    • featuresCol

      public final Param<String> featuresCol()
      Description copied from interface: HasFeaturesCol
      Param for features column name.
      Specified by:
      featuresCol in interface HasFeaturesCol
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • setFeaturesCol

      public LDA setFeaturesCol(String value)
      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).

      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • setMaxIter

      public LDA setMaxIter(int value)

    • setSeed

      public LDA setSeed(long value)

    • setCheckpointInterval

      public LDA setCheckpointInterval(int value)

    • setK

      public LDA setK(int value)

    • setDocConcentration

      public LDA setDocConcentration(double[] value)

    • setDocConcentration

      public LDA setDocConcentration(double value)

    • setTopicConcentration

      public LDA setTopicConcentration(double value)

    • setOptimizer

      public LDA setOptimizer(String value)

    • setTopicDistributionCol

      public LDA setTopicDistributionCol(String value)

    • setLearningOffset

      public LDA setLearningOffset(double value)

    • setLearningDecay

      public LDA setLearningDecay(double value)

    • setSubsamplingRate

      public LDA setSubsamplingRate(double value)

    • setOptimizeDocConcentration

      public LDA setOptimizeDocConcentration(boolean value)

    • setKeepLastCheckpoint

      public LDA setKeepLastCheckpoint(boolean value)

    • copy

      public LDA copy(ParamMap extra)
      Description copied from interface: Params
      Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Estimator<LDAModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • fit

      public LDAModel fit(Dataset<?> dataset)
      Description copied from class: Estimator
      Fits a model to the input data.
      Specified by:
      fit in class Estimator<LDAModel>
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      Description copied from class: PipelineStage
      Check transform validity and derive the output schema from the input schema.

      We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

      Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

      Specified by:
      transformSchema in class PipelineStage
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)