Packages

class LDA extends Estimator[LDAModel] with LDAParams with 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 org.apache.spark.ml.feature.Tokenizer and org.apache.spark.ml.feature.CountVectorizer can be useful for converting text to word count vectors.

Annotations
@Since( "1.6.0" )
Source
LDA.scala
See also

Latent Dirichlet allocation (Wikipedia)

Linear Supertypes
DefaultParamsWritable, MLWritable, LDAParams, HasCheckpointInterval, HasSeed, HasMaxIter, HasFeaturesCol, Estimator[LDAModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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  1. LDA
  2. DefaultParamsWritable
  3. MLWritable
  4. LDAParams
  5. HasCheckpointInterval
  6. HasSeed
  7. HasMaxIter
  8. HasFeaturesCol
  9. Estimator
  10. PipelineStage
  11. Logging
  12. Params
  13. Serializable
  14. Serializable
  15. Identifiable
  16. AnyRef
  17. Any
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Instance Constructors

  1. new LDA()
    Annotations
    @Since( "1.6.0" )
  2. new LDA(uid: String)
    Annotations
    @Since( "1.6.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final val checkpointInterval: IntParam

    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. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.

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

    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
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. def copy(extra: ParamMap): LDA

    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
    LDA → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.6.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    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

    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. final val docConcentration: DoubleArrayParam

    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" )
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String

    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
  16. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

    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
  19. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  20. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. def fit(dataset: Dataset[_]): LDAModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    LDA → Estimator
    Annotations
    @Since( "2.0.0" )
  22. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[LDAModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  23. def fit(dataset: Dataset[_], paramMap: ParamMap): LDAModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  24. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): LDAModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  25. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  26. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  27. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  28. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

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

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

    Definition Classes
    HasFeaturesCol
  31. def getK: Int

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

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

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

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

    Definition Classes
    HasMaxIter
  36. def getOldDocConcentration: Vector

    Get docConcentration used by spark.mllib LDA

    Get docConcentration used by spark.mllib LDA

    Attributes
    protected
    Definition Classes
    LDAParams
  37. def getOldTopicConcentration: Double

    Get topicConcentration used by spark.mllib LDA

    Get topicConcentration used by spark.mllib LDA

    Attributes
    protected
    Definition Classes
    LDAParams
  38. def getOptimizeDocConcentration: Boolean

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

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

    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
  41. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  42. final def getSeed: Long

    Definition Classes
    HasSeed
  43. def getSubsamplingRate: Double

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

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

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

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

    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
  48. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. final def isDefined(param: Param[_]): Boolean

    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. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  54. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  55. final val k: IntParam

    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" )
  56. final val keepLastCheckpoint: BooleanParam

    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" )
  57. final val learningDecay: DoubleParam

    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" )
  58. final val learningOffset: DoubleParam

    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" )
  59. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  72. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  73. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  74. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  75. final val optimizeDocConcentration: BooleanParam

    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" )
  76. final val optimizer: Param[String]

    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" )
  77. lazy val params: Array[Param[_]]

    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.

  78. def save(path: String): Unit

    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( ... )
  79. final val seed: LongParam

    Param for random seed.

    Param for random seed.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    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
  82. final def set[T](param: Param[T], value: T): LDA.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  83. def setCheckpointInterval(value: Int): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  84. final def setDefault(paramPairs: ParamPair[_]*): LDA.this.type

    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.

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

    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[ml]
    Definition Classes
    Params
  86. def setDocConcentration(value: Double): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  87. def setDocConcentration(value: Array[Double]): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  88. def setFeaturesCol(value: String): LDA.this.type

    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).

    Annotations
    @Since( "1.6.0" )
  89. def setK(value: Int): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  90. def setKeepLastCheckpoint(value: Boolean): LDA.this.type

    Annotations
    @Since( "2.0.0" )
  91. def setLearningDecay(value: Double): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  92. def setLearningOffset(value: Double): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  93. def setMaxIter(value: Int): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  94. def setOptimizeDocConcentration(value: Boolean): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  95. def setOptimizer(value: String): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  96. def setSeed(value: Long): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  97. def setSubsamplingRate(value: Double): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  98. def setTopicConcentration(value: Double): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  99. def setTopicDistributionCol(value: String): LDA.this.type

    Annotations
    @Since( "1.6.0" )
  100. final val subsamplingRate: DoubleParam

    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" )
  101. final val supportedOptimizers: Array[String]

    Supported values for Param optimizer.

    Supported values for Param optimizer.

    Definition Classes
    LDAParams
    Annotations
    @Since( "1.6.0" )
  102. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  103. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  104. final val topicConcentration: DoubleParam

    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" )
  105. final val topicDistributionCol: Param[String]

    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" )
  106. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    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
    LDA → PipelineStage
    Annotations
    @Since( "1.6.0" )
  107. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: 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()
  108. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    LDA → Identifiable
    Annotations
    @Since( "1.6.0" )
  109. def validateAndTransformSchema(schema: StructType): StructType

    Validates and transforms the input schema.

    Validates and transforms the input schema.

    schema

    input schema

    returns

    output schema

    Attributes
    protected
    Definition Classes
    LDAParams
  110. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  111. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  112. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  113. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from LDAParams

Inherited from HasCheckpointInterval

Inherited from HasSeed

Inherited from HasMaxIter

Inherited from HasFeaturesCol

Inherited from Estimator[LDAModel]

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 setters

(expert-only) Parameter getters