Spark ML -- Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
ml_lda(x, k = 10, max_iter = 20, doc_concentration = NULL, topic_concentration = NULL, subsampling_rate = 0.05, optimizer = "online", checkpoint_interval = 10, keep_last_checkpoint = TRUE, learning_decay = 0.51, learning_offset = 1024, optimize_doc_concentration = TRUE, seed = NULL, features_col = "features", topic_distribution_col = "topicDistribution", uid = random_string("lda_"), ...) ml_describe_topics(model, max_terms_per_topic = 10) ml_log_likelihood(model, dataset) ml_log_perplexity(model, dataset) ml_topics_matrix(model)
The number of clusters to create
The maximum number of iterations to use.
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta"). See details.
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
(For Online optimizer only) 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
Optimizer or inference algorithm used to estimate the LDA model. Supported: "online" for Online Variational Bayes (default) and "em" for Expectation-Maximization.
Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.
(Spark 2.0.0+) (For EM optimizer only) If using checkpointing, this indicates whether to keep the last checkpoint. If
(For Online optimizer only) 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) 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.
(For Online optimizer only) Indicates whether the
A random seed. Set this value if you need your results to be reproducible across repeated calls.
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by
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.
A character string used to uniquely identify the ML estimator.
Optional arguments; currently unused.
A fitted LDA model returned by
Maximum number of terms to collect for each topic. Default value of 10.
test corpus to use for calculating log likelihood or log perplexity
The object returned depends on the class of
spark_connection, the function returns an instance of a
ml_estimatorobject. The object contains a pointer to a Spark
Estimatorobject and can be used to compose
ml_pipeline, the function returns a
ml_pipelinewith the clustering estimator appended to the pipeline.
tbl_spark, an estimator is constructed then immediately fit with the input
tbl_spark, returning a clustering model.
formulais specified, the input
tbl_sparkis first transformed using a
RFormulatransformer before being fit by the estimator. The object returned in this case is a
ml_modelwhich is a wrapper of a
ml_pipeline_model. This signature does not apply to
ml_describe_topics returns a DataFrame with topics and their top-weighted terms.
ml_log_likelihood calculates a lower bound on the log likelihood of
the entire corpus
Terminology for LDA:
"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 (
features_col): LDA is given a collection of documents as input data, via the
features_col parameter. Each document is specified as a Vector of length
vocab_size, where each entry is the count for the corresponding term (word) in the document. Feature transformers such as
ft_count_vectorizer can be useful for converting text to word count vectors
This is the parameter to a Dirichlet distribution, where larger values mean more smoothing (more regularization). If not set by the user, then
doc_concentrationis set automatically. If set to singleton vector [alpha], then alpha is replicated to a vector of length k in fitting. Otherwise, the
doc_concentrationvector 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.
Values should be greater than or equal to 0
default = uniformly (1.0 / k), following the implementation from here
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
topic_concentrationis 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.
Value should be greater than or equal to 0
default = (1.0 / k), following the implementation from here.
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.
See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.