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
- Grouped
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- LDA
- DefaultParamsWritable
- MLWritable
- LDAParams
- HasCheckpointInterval
- HasSeed
- HasMaxIter
- HasFeaturesCol
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- PipelineStage
- Logging
- Params
- Serializable
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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
-
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
-
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" )
-
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
-
def
fit(dataset: Dataset[_]): LDAModel
Fits a model to the input data.
-
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" )
-
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" )
-
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()
-
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
-
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
-
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
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
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.
-
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( ... )
-
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
-
final
val
supportedOptimizers: Array[String]
Supported values for Param optimizer.
Supported values for Param optimizer.
- Definition Classes
- LDAParams
- Annotations
- @Since( "1.6.0" )
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
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
- LDA → 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
- LDA → Identifiable
- 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
- DefaultParamsWritable → MLWritable
Parameter setters
-
def
setCheckpointInterval(value: Int): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setDocConcentration(value: Double): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setDocConcentration(value: Array[Double]): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
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" )
-
def
setK(value: Int): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setMaxIter(value: Int): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setOptimizer(value: String): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setSeed(value: Long): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setSubsamplingRate(value: Double): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setTopicConcentration(value: Double): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setTopicDistributionCol(value: String): LDA.this.type
- 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 setters
-
def
setKeepLastCheckpoint(value: Boolean): LDA.this.type
- Annotations
- @Since( "2.0.0" )
-
def
setLearningDecay(value: Double): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setLearningOffset(value: Double): LDA.this.type
- Annotations
- @Since( "1.6.0" )
-
def
setOptimizeDocConcentration(value: Boolean): LDA.this.type
- 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" )