org.apache.spark.ml.clustering
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
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.
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:
Param for features column name.
Param for features column name.
Param for the number of topics (clusters) to infer.
Param for the number of topics (clusters) to infer. Must be > 1. Default: 10.
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
Optimizer or inference algorithm used to estimate the LDA model.
Optimizer or inference algorithm used to estimate the LDA model. Currently supported (case-insensitive):
For details, see the following papers:
Param for random seed.
Param for random seed.
Fraction of the corpus to be sampled and used in each iteration of mini-batch gradient descent, in range (0, 1].
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 >= 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.
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:
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.
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.
defaultCopy()
Indicates whether this instance is of type DistributedLDAModel
Indicates whether this instance is of type DistributedLDAModel
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
Return the topics described by their top-weighted terms.
Return the topics described by their top-weighted terms.
Maximum number of terms to collect for each topic. Default value of 10.
Local DataFrame with one topic per Row, with columns:
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.
Explains a param.
Explains a param.
input param, must belong to this instance.
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Explains all params of this instance.
Explains all params of this instance.
explainParam()
extractParamMap with no extra values.
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.
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 < user-supplied values < extra.
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Gets the default value of a parameter.
Gets the default value of a parameter.
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.
Gets a param by its name.
Gets a param by its name.
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Tests whether this instance contains a param with a given name.
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.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
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.
test corpus to use for calculating log likelihood
variational lower bound on the log likelihood of the entire corpus
Calculate an upper bound bound on perplexity.
Calculate an upper bound 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.
test corpus to use for calculating perplexity
Variational upper bound on log perplexity per token.
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.
Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
The parent estimator that produced this model.
The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.
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).
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
Supported values for Param optimizer.
Supported values for Param optimizer.
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).
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.
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
input dataset
additional parameters, overwrite embedded params
transformed dataset
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
input dataset
the first param pair, overwrite embedded params
other param pairs, overwrite embedded params
transformed dataset
Derives the output schema from the input schema.
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
Validates parameter values stored internally.
Validates parameter values stored internally. Raise an exception if any parameter value is invalid.
This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.
Vocabulary size (number of terms or terms in the vocabulary)
Vocabulary size (number of terms or terms in the vocabulary)
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).
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.
Learning rate, set as an exponential decay rate.
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.
A (positive) learning parameter that downweights early iterations.
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.
Indicates whether the docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training.
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
Model fitted by LDA.