public interface LDAParams extends Params, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInterval
| Modifier and Type | Method and Description |
|---|---|
DoubleArrayParam |
docConcentration()
Concentration parameter (commonly named "alpha") for the prior placed on documents'
distributions over topics ("theta").
|
double[] |
getDocConcentration() |
int |
getK() |
boolean |
getKeepLastCheckpoint() |
double |
getLearningDecay() |
double |
getLearningOffset() |
Vector |
getOldDocConcentration()
Get docConcentration used by spark.mllib LDA
|
LDAOptimizer |
getOldOptimizer() |
double |
getOldTopicConcentration()
Get topicConcentration used by spark.mllib LDA
|
boolean |
getOptimizeDocConcentration() |
String |
getOptimizer() |
double |
getSubsamplingRate() |
double |
getTopicConcentration() |
String |
getTopicDistributionCol() |
IntParam |
k()
Param for the number of topics (clusters) to infer.
|
BooleanParam |
keepLastCheckpoint()
For EM optimizer only:
optimizer = "em". |
DoubleParam |
learningDecay()
For Online optimizer only:
optimizer = "online". |
DoubleParam |
learningOffset()
For Online optimizer only:
optimizer = "online". |
BooleanParam |
optimizeDocConcentration()
For Online optimizer only (currently):
optimizer = "online". |
Param<String> |
optimizer()
Optimizer or inference algorithm used to estimate the LDA model.
|
DoubleParam |
subsamplingRate()
For Online optimizer only:
optimizer = "online". |
String[] |
supportedOptimizers()
Supported values for Param
optimizer. |
DoubleParam |
topicConcentration()
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
distributions over terms.
|
Param<String> |
topicDistributionCol()
Output column with estimates of the topic mixture distribution for each document (often called
"theta" in the literature).
|
StructType |
validateAndTransformSchema(StructType schema)
Validates and transforms the input schema.
|
featuresCol, getFeaturesColgetMaxIter, maxItercheckpointInterval, getCheckpointIntervalclear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoString, uidIntParam k()
int getK()
DoubleArrayParam docConcentration()
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.
double[] getDocConcentration()
Vector getOldDocConcentration()
DoubleParam topicConcentration()
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.
double getTopicConcentration()
double getOldTopicConcentration()
String[] supportedOptimizers()
optimizer.Param<String> optimizer()
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
String getOptimizer()
Param<String> topicDistributionCol()
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.
String getTopicDistributionCol()
DoubleParam learningOffset()
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.
double getLearningOffset()
DoubleParam learningDecay()
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.
double getLearningDecay()
DoubleParam subsamplingRate()
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
OnlineLDAOptimizer.
Default: 0.05, i.e., 5% of total documents.
double getSubsamplingRate()
BooleanParam optimizeDocConcentration()
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
boolean getOptimizeDocConcentration()
BooleanParam keepLastCheckpoint()
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
boolean getKeepLastCheckpoint()
StructType validateAndTransformSchema(StructType schema)
schema - input schemaLDAOptimizer getOldOptimizer()