Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/18035#discussion_r117465188
--- Diff: R/pkg/R/mllib_classification.R ---
@@ -124,39 +125,30 @@ setMethod("predict", signature(object =
"LinearSVCModel"),
predict_internal(object, newData)
})
-# Get the summary of an LinearSVCModel
+# Get the summary of a linear SVM model.
-#' @param object an LinearSVCModel fitted by \code{spark.svmLinear}.
+#' @param object a linear SVM model fitted by \code{spark.svmLinear}.
#' @return \code{summary} returns summary information of the fitted model,
which is a list.
#' The list includes \code{coefficients} (coefficients of the
fitted model),
-#' \code{intercept} (intercept of the fitted model),
\code{numClasses} (number of classes),
-#' \code{numFeatures} (number of features).
+#' \code{numClasses} (number of classes), \code{numFeatures}
(number of features).
#' @rdname spark.svmLinear
#' @aliases summary,LinearSVCModel-method
#' @export
#' @note summary(LinearSVCModel) since 2.2.0
setMethod("summary", signature(object = "LinearSVCModel"),
function(object) {
jobj <- object@jobj
- features <- callJMethod(jobj, "features")
- labels <- callJMethod(jobj, "labels")
- coefficients <- callJMethod(jobj, "coefficients")
- nCol <- length(coefficients) / length(features)
- coefficients <- matrix(unlist(coefficients), ncol = nCol)
- intercept <- callJMethod(jobj, "intercept")
+ features <- callJMethod(jobj, "rFeatures")
+ coefficients <- callJMethod(jobj, "rCoefficients")
+ coefficients <- as.matrix(unlist(coefficients))
+ colnames(coefficients) <- c("Estimate")
+ rownames(coefficients) <- unlist(features)
numClasses <- callJMethod(jobj, "numClasses")
numFeatures <- callJMethod(jobj, "numFeatures")
- if (nCol == 1) {
--- End diff --
ML ```LinearSVC``` only supports binary classification, and will not
support multiple classification in the future, so we can simplify here.
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