Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/14229#discussion_r74675724
--- Diff: R/pkg/R/mllib.R ---
@@ -299,6 +307,92 @@ setMethod("summary", signature(object =
"NaiveBayesModel"),
return(list(apriori = apriori, tables = tables))
})
+# Returns posterior probabilities from a Latent Dirichlet Allocation model
produced by spark.lda()
+
+#' @param newData A SparkDataFrame for testing
+#' @return \code{spark.posterior} returns a SparkDataFrame containing
posterior probabilities
+#' vectors named "topicDistribution"
+#' @rdname spark.lda
+#' @aliases spark.lda,spark.posterior,LDAModel-method,SparkDataFrame
+#' @export
+#' @note spark.posterior(LDAModel) since 2.1.0
+setMethod("spark.posterior", signature(object = "LDAModel", newData =
"SparkDataFrame"),
+ function(object, newData) {
+ return(dataFrame(callJMethod(object@jobj, "transform",
newData@sdf)))
+ })
+
+# Returns the summary of a Latent Dirichlet Allocation model produced by
\code{spark.lda}
+
+#' @param object A Latent Dirichlet Allocation model fitted by
\code{spark.lda}
+#' @return \code{summary} returns a list containing
+#' \code{docConcentration}, concentration parameter commonly named
\code{alpha} for the
+#' prior placed on documents distributions over topics
\code{theta};
+#' \code{topicConcentration}, concentration parameter commonly
named \code{beta} or
+#' \code{eta} for the prior placed on topic distributions over
terms;
+#' \code{logLikelihood}, log likelihood of the entire corpus;
+#' \code{logPerplexity}, log perplexity;
+#' \code{isDistributed}, TRUE for distribuetd model while FALSE
for local model;
+#' \code{vocabSize}, number of terms in the corpus;
+#' \code{topics}, top 10 terms and their weights of all topics;
+#' \code{vocabulary}, whole terms of the training corpus, NULL if
libsvm format file used as
+#' training set.
+#' @rdname spark.lda
+#' @aliases summary,spark.lda,LDAModel-method
+#' @export
+#' @note summary(LDAModel) since 2.1.0
+setMethod("summary", signature(object = "LDAModel"),
+ function(object, ...) {
+ jobj <- object@jobj
+ docConcentration <- callJMethod(jobj, "docConcentration")
+ topicConcentration <- callJMethod(jobj, "topicConcentration")
+ logLikelihood <- callJMethod(jobj, "logLikelihood")
+ logPerplexity <- callJMethod(jobj, "logPerplexity")
+ isDistributed <- callJMethod(jobj, "isDistributed")
+ vocabSize <- callJMethod(jobj, "vocabSize")
+ topics <- dataFrame(callJMethod(jobj, "topics"))
+ vocabulary <- callJMethod(jobj, "vocabulary")
+ return(list(docConcentration = unlist(docConcentration),
+ topicConcentration = topicConcentration,
+ logLikelihood = logLikelihood, logPerplexity =
logPerplexity,
+ isDistributed = isDistributed, vocabSize =
vocabSize,
+ topics = topics,
+ vocabulary = unlist(vocabulary)))
+ })
+
+# Returns the log perplexity of a Latent Dirichlet Allocation model
produced by \code{spark.lda}
+
+#' @return \code{spark.perplexity} returns the log perplexity of given
SparkDataFrame, or the log
+#' perplexity of the training data if missing argument "data".
+#' @rdname spark.lda
+#" @aliases spark.perplexity,spark.lda,LDAModel-method
+#' @export
+#' @note summary(LDAModel) since 2.1.0
+setMethod("spark.perplexity", signature(object = "LDAModel"),
+ function(object, newData) {
+ return(ifelse(missing(newData), callJMethod(object@jobj,
"logPerplexity"),
+ callJMethod(object@jobj, "computeLogPerplexity",
newData@sdf)))
+ })
+
+# Saves the Latent Dirichlet Allocation model to the input path.
+
+#' @param path The directory where the model is saved
+#' @param overwrite Overwrites or not if the output path already exists.
Default is FALSE
+#' which means throw exception if the output path exists.
+#'
+#' @rdname spark.lda
+#' @aliases write.ml,LDAModel-method,character-method
--- End diff --
only one `-method`
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