Class/Object

org.apache.spark.ml.clustering

LocalLDAModel

Related Docs: object LocalLDAModel | package clustering

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class LocalLDAModel extends LDAModel

Local (non-distributed) model fitted by LDA.

This model stores the inferred topics only; it does not store info about the training dataset.

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@Since( "1.6.0" )
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  1. LocalLDAModel
  2. LDAModel
  3. MLWritable
  4. LDAParams
  5. HasCheckpointInterval
  6. HasSeed
  7. HasMaxIter
  8. HasFeaturesCol
  9. Model
  10. Transformer
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
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  1. final def !=(arg0: Any): Boolean

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    Definition Classes
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  2. final def ##(): Int

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  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final val checkpointInterval: IntParam

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    Param for set checkpoint interval (>= 1) or disable checkpoint (-1).

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations.

    Definition Classes
    HasCheckpointInterval
  7. final def clear(param: Param[_]): LocalLDAModel.this.type

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  8. def clone(): AnyRef

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def copy(extra: ParamMap): LocalLDAModel

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    Creates a copy of this instance with the same UID and some extra 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().

    Definition Classes
    LocalLDAModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.6.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  12. def describeTopics(): DataFrame

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    Definition Classes
    LDAModel
    Annotations
    @Since( "1.6.0" )
  13. def describeTopics(maxTermsPerTopic: Int): DataFrame

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    Return the topics described by their top-weighted terms.

    Return the topics described by their top-weighted terms.

    maxTermsPerTopic

    Maximum number of terms to collect for each topic. Default value of 10.

    returns

    Local DataFrame with one topic per Row, with columns:

    • "topic": IntegerType: topic index
    • "termIndices": ArrayType(IntegerType): term indices, sorted in order of decreasing term importance
    • "termWeights": ArrayType(DoubleType): corresponding sorted term weights
    Definition Classes
    LDAModel
    Annotations
    @Since( "1.6.0" )
  14. final val docConcentration: DoubleArrayParam

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    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, 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 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.
    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  15. final def eq(arg0: AnyRef): Boolean

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    AnyRef
  16. def equals(arg0: Any): Boolean

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  17. def estimatedDocConcentration: Vector

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    Value for docConcentration estimated from data.

    Value for docConcentration estimated from data. If Online LDA was used and optimizeDocConcentration was set to false, then this returns the fixed (given) value for the docConcentration parameter.

    Definition Classes
    LDAModel
    Annotations
    @Since( "2.0.0" )
  18. def explainParam(param: Param[_]): String

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    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  19. def explainParams(): String

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    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  20. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap

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    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  22. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  24. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  25. final def getCheckpointInterval: Int

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    Definition Classes
    HasCheckpointInterval
  26. final def getClass(): Class[_]

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  27. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  28. def getDocConcentration: Array[Double]

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  29. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  30. def getK: Int

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  31. def getKeepLastCheckpoint: Boolean

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    Definition Classes
    LDAParams
    Annotations
    @Since( "2.0.0" )
  32. def getLearningDecay: Double

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  33. def getLearningOffset: Double

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  34. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  35. def getOldDocConcentration: Vector

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    Get docConcentration used by spark.mllib LDA

    Get docConcentration used by spark.mllib LDA

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    Definition Classes
    LDAParams
  36. def getOldTopicConcentration: Double

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    Get topicConcentration used by spark.mllib LDA

    Get topicConcentration used by spark.mllib LDA

    Attributes
    protected
    Definition Classes
    LDAParams
  37. def getOptimizeDocConcentration: Boolean

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  38. def getOptimizer: String

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  39. final def getOrDefault[T](param: Param[T]): T

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    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  40. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  41. final def getSeed: Long

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    Definition Classes
    HasSeed
  42. def getSubsamplingRate: Double

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  43. def getTopicConcentration: Double

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  44. def getTopicDistributionCol: String

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    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  45. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  46. def hasParam(paramName: String): Boolean

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    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  47. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  48. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  49. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Logging
  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging
  51. final def isDefined(param: Param[_]): Boolean

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    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  52. def isDistributed: Boolean

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    Indicates whether this instance is of type DistributedLDAModel

    Indicates whether this instance is of type DistributedLDAModel

    Definition Classes
    LocalLDAModelLDAModel
    Annotations
    @Since( "1.6.0" )
  53. final def isInstanceOf[T0]: Boolean

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    Any
  54. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  55. def isTraceEnabled(): Boolean

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    Logging
  56. final val k: IntParam

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    Param for the number of topics (clusters) to infer.

    Param for the number of topics (clusters) to infer. Must be > 1. Default: 10.

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  57. final val keepLastCheckpoint: BooleanParam

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    For EM optimizer only: optimizer = "em".

    For EM optimizer only: 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

    Definition Classes
    LDAParams
    Annotations
    @Since( "2.0.0" )
  58. final val learningDecay: DoubleParam

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    For Online optimizer only: optimizer = "online".

    For Online optimizer only: 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.

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  59. final val learningOffset: DoubleParam

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    For Online optimizer only: optimizer = "online".

    For Online optimizer only: 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.

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  60. def log: Logger

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    Logging
  61. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  62. def logDebug(msg: ⇒ String): Unit

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    Logging
  63. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  64. def logError(msg: ⇒ String): Unit

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  65. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  66. def logInfo(msg: ⇒ String): Unit

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    Logging
  67. def logLikelihood(dataset: Dataset[_]): Double

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    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 the Online LDA paper (Hoffman et al., 2010).

    WARNING: If this model is an instance of DistributedLDAModel (produced when optimizer is set to "em"), this involves collecting a large topicsMatrix to the driver. This implementation may be changed in the future.

    dataset

    test corpus to use for calculating log likelihood

    returns

    variational lower bound on the log likelihood of the entire corpus

    Definition Classes
    LDAModel
    Annotations
    @Since( "2.0.0" )
  68. def logName: String

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    protected
    Definition Classes
    Logging
  69. def logPerplexity(dataset: Dataset[_]): Double

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    Calculate an upper bound on perplexity.

    Calculate an upper bound on perplexity. (Lower is better.) See Equation (16) in the Online LDA paper (Hoffman et al., 2010).

    WARNING: If this model is an instance of DistributedLDAModel (produced when optimizer is set to "em"), this involves collecting a large topicsMatrix to the driver. This implementation may be changed in the future.

    dataset

    test corpus to use for calculating perplexity

    returns

    Variational upper bound on log perplexity per token.

    Definition Classes
    LDAModel
    Annotations
    @Since( "2.0.0" )
  70. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  71. def logTrace(msg: ⇒ String): Unit

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    Logging
  72. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  73. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  74. final val maxIter: IntParam

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    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  75. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  76. final def notify(): Unit

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    Definition Classes
    AnyRef
  77. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  78. final val optimizeDocConcentration: BooleanParam

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    For Online optimizer only (currently): optimizer = "online".

    For Online optimizer only (currently): 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

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  79. final val optimizer: Param[String]

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    Optimizer or inference algorithm used to estimate the LDA model.

    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
    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  80. lazy val params: Array[Param[_]]

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    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  81. var parent: Estimator[LDAModel]

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    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  82. def save(path: String): Unit

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    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  83. final val seed: LongParam

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    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  84. final def set(paramPair: ParamPair[_]): LocalLDAModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  85. final def set(param: String, value: Any): LocalLDAModel.this.type

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    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  86. final def set[T](param: Param[T], value: T): LocalLDAModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  87. final def setDefault(paramPairs: ParamPair[_]*): LocalLDAModel.this.type

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    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

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    protected
    Definition Classes
    Params
  88. final def setDefault[T](param: Param[T], value: T): LocalLDAModel.this.type

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    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  89. def setFeaturesCol(value: String): LocalLDAModel.this.type

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    The features for LDA should be a Vector representing the word counts in a document.

    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).

    Definition Classes
    LDAModel
    Annotations
    @Since( "1.6.0" )
  90. def setParent(parent: Estimator[LDAModel]): LDAModel

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  91. def setSeed(value: Long): LocalLDAModel.this.type

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    Definition Classes
    LDAModel
    Annotations
    @Since( "1.6.0" )
  92. def setTopicDistributionCol(value: String): LocalLDAModel.this.type

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    Definition Classes
    LDAModel
    Annotations
    @Since( "2.2.0" )
  93. final val subsamplingRate: DoubleParam

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    For Online optimizer only: optimizer = "online".

    For Online optimizer only: 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 org.apache.spark.mllib.clustering.OnlineLDAOptimizer.

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

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  94. final val supportedOptimizers: Array[String]

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    Supported values for Param optimizer.

    Supported values for Param optimizer.

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  95. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  96. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  97. final val topicConcentration: DoubleParam

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    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.

    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.
    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  98. final val topicDistributionCol: Param[String]

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    Output column with estimates of the topic mixture distribution for each document (often called "theta" in the literature).

    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.

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  99. def topicsMatrix: Matrix

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    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: If this model is actually a DistributedLDAModel instance produced by the Expectation-Maximization ("em") optimizer, then this method could involve collecting a large amount of data to the driver (on the order of vocabSize x k).

    Definition Classes
    LDAModel
    Annotations
    @Since( "2.0.0" )
  100. def transform(dataset: Dataset[_]): DataFrame

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    Transforms the input dataset.

    Transforms the input dataset.

    WARNING: If this model is an instance of DistributedLDAModel (produced when optimizer is set to "em"), this involves collecting a large topicsMatrix to the driver. This implementation may be changed in the future.

    Definition Classes
    LDAModelTransformer
    Annotations
    @Since( "2.0.0" )
  101. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  102. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  103. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    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.

    Definition Classes
    LDAModelPipelineStage
    Annotations
    @Since( "1.6.0" )
  104. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  105. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    LDAModelIdentifiable
    Annotations
    @Since( "1.6.0" )
  106. def validateAndTransformSchema(schema: StructType): StructType

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    Validates and transforms the input schema.

    Validates and transforms the input schema.

    schema

    input schema

    returns

    output schema

    Attributes
    protected
    Definition Classes
    LDAParams
  107. val vocabSize: Int

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    Vocabulary size (number of terms or words in the vocabulary)

    Vocabulary size (number of terms or words in the vocabulary)

    Definition Classes
    LDAModel
    Annotations
    @Since( "1.6.0" )
  108. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  109. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  110. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  111. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    LocalLDAModelMLWritable
    Annotations
    @Since( "1.6.0" )

Inherited from LDAModel

Inherited from MLWritable

Inherited from LDAParams

Inherited from HasCheckpointInterval

Inherited from HasSeed

Inherited from HasMaxIter

Inherited from HasFeaturesCol

Inherited from Model[LDAModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter getters