An alias for getOrDefault().

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.

Clears the user-supplied value for the input param.

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().

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.

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.

Remove any remaining checkpoint files from training.

Return the topics described by their top-weighted terms.

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.

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.

Explains a param.

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

extractParamMap with no extra values.

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.

Param for features column name.

Optionally returns the user-supplied value of a param.

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.

Gets the default value of a parameter.

Get docConcentration used by spark.mllib LDA

Get topicConcentration used by spark.mllib LDA

Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Gets a param by its name.

Tests whether the input param has a default value set.

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

Indicates whether this Model has a corresponding parent.

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

Indicates whether this instance is of type DistributedLDAModel

Checks whether a param is explicitly set.

Param for the number of topics (clusters) to infer. Must be > 1. Default: 10.

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

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.

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.

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.

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.

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

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

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

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

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.

The parent estimator that produced this model.

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

Param for random seed.

Sets a parameter in the embedded param map.

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

Sets a parameter in the embedded param map.

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.

Sets a default value for a param.

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).

Sets the parent of this model (Java API).

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.

Supported values for Param optimizer.

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.

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.

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.

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).

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.

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.

Transforms the dataset with provided parameter map as additional parameters.

Transforms the dataset with optional parameters

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.

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.

An immutable unique ID for the object and its derivatives.

Validates and transforms the input schema.

Returns an MLWriter instance for this ML instance.