class DistributedLDAModel extends LDAModel
Distributed model fitted by LDA. This type of model is currently only produced by Expectation-Maximization (EM).
This model stores the inferred topics, the full training dataset, and the topic distribution for each training document.
- Annotations
- @Since("1.6.0")
- Source
- LDA.scala
- Grouped
- Alphabetic
- By Inheritance
- DistributedLDAModel
- LDAModel
- MLWritable
- LDAParams
- HasCheckpointInterval
- HasSeed
- HasMaxIter
- HasFeaturesCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
- 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 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")
- EM
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- 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 maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
- 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:
- Definition Classes
- LDAParams
- Annotations
- @Since("1.6.0")
- final val seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
- 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")
- 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")
- EM
- 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")
Members
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
- final def clear(param: Param[_]): DistributedLDAModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
- 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 deleteCheckpointFiles(): Unit
Remove any remaining checkpoint files from training.
Remove any remaining checkpoint files from training.
- Annotations
- @Since("2.0.0")
- See also
- 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.
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")
- 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.
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.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.
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 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
- 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 getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
- 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.
Gets a param by its name.
- Definition Classes
- Params
- 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
- 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
- def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
- 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
- def isDistributed: Boolean
Indicates whether this instance is of type DistributedLDAModel
Indicates whether this instance is of type DistributedLDAModel
- Definition Classes
- DistributedLDAModel → LDAModel
- Annotations
- @Since("1.6.0")
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- def logLikelihood(dataset: Dataset[_]): Double
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")
- 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)
Log probability of the current parameter estimate: log P(topics, topic distributions for docs | Dirichlet hyperparameters)
- 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.
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)
.Saves this ML instance to the input path, a shortcut of
write.save(path)
.- Definition Classes
- MLWritable
- Annotations
- @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
- final def set[T](param: Param[T], value: T): DistributedLDAModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
- def setParent(parent: Estimator[LDAModel]): LDAModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
- def setTopicDistributionCol(value: String): DistributedLDAModel.this.type
- Definition Classes
- LDAModel
- Annotations
- @Since("2.2.0")
- final val supportedOptimizers: Array[String]
Supported values for Param optimizer.
Supported values for Param optimizer.
- Definition Classes
- LDAParams
- Annotations
- @Since("1.6.0")
- 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")
- 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)
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.
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.
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
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.
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 byParam.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")
- 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
- LDAModel → Identifiable
- Annotations
- @Since("1.6.0")
- val vocabSize: Int
- Definition Classes
- LDAModel
- Annotations
- @Since("1.6.0")
- def write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- DistributedLDAModel → MLWritable
- Annotations
- @Since("1.6.0")
Parameter setters
- 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 setSeed(value: Long): DistributedLDAModel.this.type
- Definition Classes
- LDAModel
- Annotations
- @Since("1.6.0")
Parameter getters
- final def getCheckpointInterval: Int
- Definition Classes
- HasCheckpointInterval
- 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")
- final def getMaxIter: Int
- Definition Classes
- HasMaxIter
- def getOptimizer: String
- Definition Classes
- LDAParams
- Annotations
- @Since("1.6.0")
- 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")
(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.
- 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 andDistributedLDAModel.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".
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".
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")
- 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")
(expert-only) Parameter getters
- 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")
- def getOptimizeDocConcentration: Boolean
- Definition Classes
- LDAParams
- Annotations
- @Since("1.6.0")