org.apache.spark.ml.clustering
LocalLDAModel
Related Docs:
object LocalLDAModel
| package clustering
org.apache.spark.ml.clustering
An alias for getOrDefault().
An alias for getOrDefault().
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.
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
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().
Copies param values from this instance to another instance for params shared by them.
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.
the target instance, which should work with the same set of default Params as this source instance
extra params to be copied to the target's paramMap
the target instance with param values copied
Default implementation of copy with extra 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.
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:
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:
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. See 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.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.
Param for features column name.
Param for features column name.
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.
Get docConcentration used by spark.mllib LDA
Get docConcentration used by spark.mllib LDA
Get topicConcentration 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.
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.
Indicates whether this instance is of type DistributedLDAModel
Indicates whether this instance is of type DistributedLDAModel
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Param for the number of topics (clusters) to infer.
Param for the number of topics (clusters) to infer. Must be > 1. Default: 10.
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 and
DistributedLDAModel.deleteCheckpointFiles for removing remaining checkpoints.
Default: true
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.
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.
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 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.
test corpus to use for calculating perplexity
Variational upper bound on log perplexity per token.
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
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
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:
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.
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.
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).
Param for random seed.
Param for random seed.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets default values for a list of params.
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.
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Sets a default value for a param.
Sets a default value for a param.
param to set the default value. Make sure that this param is initialized before this method gets called.
the default value
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).
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
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.
Supported values for Param optimizer.
Supported values for Param optimizer.
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.
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
:: DeveloperApi ::
:: DeveloperApi ::
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.
:: DeveloperApi ::
:: DeveloperApi ::
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.
An immutable unique ID for the object and its derivatives.
Validates and transforms the input schema.
Validates and transforms the input schema.
input schema
output schema
Vocabulary size (number of terms or words in the vocabulary)
Vocabulary size (number of terms or words in the vocabulary)
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
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.
Local (non-distributed) model fitted by LDA.
This model stores the inferred topics only; it does not store info about the training dataset.