ml_lda
Spark ML -- Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA), a topic model designed for text documents.
Usage
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)
Arguments
- x
A
spark_connection
,ml_pipeline
, or atbl_spark
.- k
The number of clusters to create
- max_iter
The maximum number of iterations to use.
- doc_concentration
Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta"). See details.
- topic_concentration
Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
- subsampling_rate
(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
max_iter
so the entire corpus is used. Specifically, set both so that maxIterations * miniBatchFraction greater than or equal to 1.- optimizer
Optimizer or inference algorithm used to estimate the LDA model. Supported: "online" for Online Variational Bayes (default) and "em" for Expectation-Maximization.
- checkpoint_interval
Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.
- keep_last_checkpoint
(Spark 2.0.0+) (For EM optimizer only) 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.- learning_decay
(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.
- learning_offset
(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.
- optimize_doc_concentration
(For Online optimizer only) Indicates whether the
doc_concentration
(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
- seed
A random seed. Set this value if you need your results to be reproducible across repeated calls.
- features_col
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
ft_r_formula
.- topic_distribution_col
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.
- uid
A character string used to uniquely identify the ML estimator.
- ...
Optional arguments; currently unused.
- model
A fitted LDA model returned by
ml_lda()
.- max_terms_per_topic
Maximum number of terms to collect for each topic. Default value of 10.
- dataset
test corpus to use for calculating log likelihood or log perplexity
Details
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_tokenizer
and ft_count_vectorizer
can be useful for converting text to word count vectors
Value
The object returned depends on the class of x
.
spark_connection
: Whenx
is aspark_connection
, the function returns an instance of aml_estimator
object. The object contains a pointer to a SparkEstimator
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the clustering estimator appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, an estimator is constructed then immediately fit with the inputtbl_spark
, returning a clustering model.tbl_spark
, withformula
orfeatures
specified: Whenformula
is specified, the inputtbl_spark
is first transformed using aRFormula
transformer before being fit by the estimator. The object returned in this case is aml_model
which is a wrapper of aml_pipeline_model
. This signature does not apply toml_lda()
.
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
Parameter details
doc_concentration
This is the parameter to a Dirichlet distribution, where larger values mean more smoothing (more regularization). If not set by the user, then doc_concentration
is set automatically. If set to singleton vector [alpha], then alpha is replicated to a vector of length k in fitting. Otherwise, the doc_concentration
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
topic_concentration
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_concentration
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.
topic_distribution_col
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 Also
See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.
Other ml clustering algorithms: ml_bisecting_kmeans
,
ml_gaussian_mixture
,
ml_kmeans