Spark 3.5.3 ScalaDoc - org.apache.spark.ml.clustering.DistributedLDAModel

final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

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

An alias for getOrDefault().

Attributes: protected
Definition Classes: Params

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def asInstanceOf[T0]: T0

Definition Classes: Any

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

final def clear(param: Param[_]): DistributedLDAModel.this.type

Clears the user-supplied value for the input param.

Definition Classes: Params

def clone(): AnyRef

Attributes: protected[lang]
Definition Classes: AnyRef
Annotations: @throws( ... ) @native()

def copy(extra: ParamMap): DistributedLDAModel

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: DistributedLDAModel → Model → Transformer → PipelineStage → Params
Annotations: @Since( "1.6.0" )

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.

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

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

def deleteCheckpointFiles(): Unit

Remove any remaining checkpoint files from training.

Annotations: @Since( "2.0.0" )
See also: getCheckpointFiles

def describeTopics(): DataFrame

Definition Classes: LDAModel
Annotations: @Since( "1.6.0" )

def describeTopics(maxTermsPerTopic: Int): DataFrame

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" )

final val docConcentration: DoubleArrayParam

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" )

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def estimatedDocConcentration: Vector

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" )

def explainParam(param: Param[_]): String

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

def explainParams(): String

Explains all params of this instance.

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

Definition Classes: Params

final def extractParamMap(): ParamMap

extractParamMap with no extra values.

Definition Classes: Params

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.

Definition Classes: Params

final val featuresCol: Param[String]

Param for features column name.

Definition Classes: HasFeaturesCol

def finalize(): Unit

Attributes: protected[lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

final def get[T](param: Param[T]): Option[T]

Optionally returns the user-supplied value of a param.

Definition Classes: Params

def getCheckpointFiles: Array[String]

If using checkpointing and LDA.keepLastCheckpoint is set to true, then there may be saved checkpoint files.

If using checkpointing and LDA.keepLastCheckpoint is set to true, then there may be saved checkpoint files. This method is provided so that users can manage those files.

Note that removing the checkpoints can cause failures if a partition is lost and is needed by certain DistributedLDAModel methods. Reference counting will clean up the checkpoints when this model and derivative data go out of scope.

returns: Checkpoint files from training

Annotations: @Since( "2.0.0" )

final def getCheckpointInterval: Int

Definition Classes: HasCheckpointInterval

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

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

Gets the default value of a parameter.

Definition Classes: Params

def getDocConcentration: Array[Double]

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

final def getFeaturesCol: String

Definition Classes: HasFeaturesCol

def getK: Int

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

def getKeepLastCheckpoint: Boolean

Definition Classes: LDAParams
Annotations: @Since( "2.0.0" )

def getLearningDecay: Double

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

def getLearningOffset: Double

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

final def getMaxIter: Int

Definition Classes: HasMaxIter

def getOldDocConcentration: Vector

Get docConcentration used by spark.mllib LDA

Attributes: protected
Definition Classes: LDAParams

def getOldTopicConcentration: Double

Get topicConcentration used by spark.mllib LDA

Attributes: protected
Definition Classes: LDAParams

def getOptimizeDocConcentration: Boolean

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

def getOptimizer: String

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

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

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

Gets a param by its name.

Definition Classes: Params

final def getSeed: Long

Definition Classes: HasSeed

def getSubsamplingRate: Double

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

def getTopicConcentration: Double

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

def getTopicDistributionCol: String

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

final def hasDefault[T](param: Param[T]): Boolean

Tests whether the input param has a default value set.

Definition Classes: Params

def hasParam(paramName: String): Boolean

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

Definition Classes: Params

def hasParent: Boolean

Indicates whether this Model has a corresponding parent.

Definition Classes: Model

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes: protected
Definition Classes: Logging

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes: protected
Definition Classes: Logging

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

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

Definition Classes: Params

def isDistributed: Boolean

Indicates whether this instance is of type DistributedLDAModel

Definition Classes: DistributedLDAModel → LDAModel
Annotations: @Since( "1.6.0" )

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

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

Checks whether a param is explicitly set.

Definition Classes: Params

def isTraceEnabled(): Boolean

Attributes: protected
Definition Classes: Logging

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" )

final val keepLastCheckpoint: BooleanParam

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" )

final val learningDecay: DoubleParam

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" )

final val learningOffset: DoubleParam

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" )

def log: Logger

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logDebug(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logError(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logInfo(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logLikelihood(dataset: Dataset[_]): Double

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" )

def logName: String

Attributes: protected
Definition Classes: Logging

def logPerplexity(dataset: Dataset[_]): Double

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" )

lazy val logPrior: Double

Log probability of the current parameter estimate: log P(topics, topic distributions for docs | Dirichlet hyperparameters)

Annotations: @Since( "1.6.0" )

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes: protected
Definition Classes: Logging

def logTrace(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logWarning(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

final val maxIter: IntParam

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

Definition Classes: HasMaxIter

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

final val optimizeDocConcentration: BooleanParam

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" )

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" )

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.

var parent: Estimator[LDAModel]

The parent estimator that produced this model.

Definition Classes: Model
Note: For ensembles' component Models, this value can be null.

def save(path: String): Unit

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

Definition Classes: MLWritable
Annotations: @Since( "1.6.0" ) @throws( ... )

final val seed: LongParam

Param for random seed.

Definition Classes: HasSeed

final def set(paramPair: ParamPair[_]): DistributedLDAModel.this.type

Sets a parameter in the embedded param map.

Attributes: protected
Definition Classes: Params

final def set(param: String, value: Any): DistributedLDAModel.this.type

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

Attributes: protected
Definition Classes: Params

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

Sets a parameter in the embedded param map.

Definition Classes: Params

final def setDefault(paramPairs: ParamPair[_]*): DistributedLDAModel.this.type

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

final def setDefault[T](param: Param[T], value: T): DistributedLDAModel.this.type

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

def setFeaturesCol(value: String): DistributedLDAModel.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).

Definition Classes: LDAModel
Annotations: @Since( "1.6.0" )

def setParent(parent: Estimator[LDAModel]): LDAModel

Sets the parent of this model (Java API).

Definition Classes: Model

def setSeed(value: Long): DistributedLDAModel.this.type

Definition Classes: LDAModel
Annotations: @Since( "1.6.0" )

def setTopicDistributionCol(value: String): DistributedLDAModel.this.type

Definition Classes: LDAModel
Annotations: @Since( "2.2.0" )

final val subsamplingRate: DoubleParam

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" )

final val supportedOptimizers: Array[String]

Supported values for Param optimizer.

Definition Classes: LDAParams
Annotations: @Since( "1.6.0" )

final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes: AnyRef

def toLocal: LocalLDAModel

Convert this distributed model to a local representation.

Convert this distributed model to a local representation. This discards info about the training dataset.

WARNING: This involves collecting a large topicsMatrix to the driver.

Annotations: @Since( "1.6.0" )

def toString(): String

Definition Classes: DistributedLDAModel → Identifiable → AnyRef → Any
Annotations: @Since( "3.0.0" )

final val topicConcentration: DoubleParam

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" )

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" )

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.

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" )

lazy val trainingLogLikelihood: Double

Log likelihood of the observed tokens in the training set, given the current parameter estimates: log P(docs | topics, topic distributions for docs, Dirichlet hyperparameters)

Notes:

This excludes the prior; for that, use logPrior.
Even with logPrior, this is NOT the same as the data log likelihood given the hyperparameters.
This is computed from the topic distributions computed during training. If you call logLikelihood() on the same training dataset, the topic distributions will be computed again, possibly giving different results.

Annotations: @Since( "1.6.0" )

def transform(dataset: Dataset[_]): DataFrame

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: LDAModel → Transformer
Annotations: @Since( "2.0.0" )

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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" )

def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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()

def transformSchema(schema: StructType): StructType

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: LDAModel → PipelineStage
Annotations: @Since( "1.6.0" )

def transformSchema(schema: StructType, logging: Boolean): StructType

:: 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()

val uid: String

An immutable unique ID for the object and its derivatives.

Definition Classes: LDAModel → Identifiable
Annotations: @Since( "1.6.0" )

def validateAndTransformSchema(schema: StructType): StructType

Validates and transforms the input schema.

schema: input schema
returns: output schema

Attributes: protected
Definition Classes: LDAParams

val vocabSize: Int

Definition Classes: LDAModel
Annotations: @Since( "1.6.0" )

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long, arg1: Int): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @throws( ... ) @native()

def write: MLWriter

Returns an MLWriter instance for this ML instance.

Definition Classes: DistributedLDAModel → MLWritable
Annotations: @Since( "1.6.0" )

Packages

DistributedLDAModel

Companion object DistributedLDAModel

class DistributedLDAModel extends LDAModel

Value Members

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

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter getters

Packages

DistributedLDAModel 

Companion object DistributedLDAModel

class DistributedLDAModel extends LDAModel

Value Members

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

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter getters

DistributedLDAModel