Github user wangmiao1981 commented on a diff in the pull request:
https://github.com/apache/spark/pull/16117#discussion_r90694765
--- Diff: R/pkg/R/mllib.R ---
@@ -817,44 +804,29 @@ setMethod("predict", signature(object =
"LogisticRegressionModel"),
# Get the summary of an LogisticRegressionModel
#' @param object an LogisticRegressionModel fitted by \code{spark.logit}
-#' @return \code{summary} returns the Binary Logistic regression results
of a given model as list,
-#' including roc, areaUnderROC, pr, fMeasureByThreshold,
precisionByThreshold,
-#' recallByThreshold, totalIterations, objectiveHistory. Note that
Multinomial logistic
-#' regression summary is not available now.
+#' @return \code{summary} returns coefficients matrix of the fitted model
#' @rdname spark.logit
#' @aliases summary,LogisticRegressionModel-method
#' @export
#' @note summary(LogisticRegressionModel) since 2.1.0
setMethod("summary", signature(object = "LogisticRegressionModel"),
function(object) {
jobj <- object@jobj
- is.loaded <- callJMethod(jobj, "isLoaded")
-
- if (is.loaded) {
- stop("Loaded model doesn't have training summary.")
+ features <- callJMethod(jobj, "rFeatures")
+ labels <- callJMethod(jobj, "labels")
+ coefficients <- callJMethod(jobj, "rCoefficients")
+ nCol <- length(coefficients) / length(features)
--- End diff --
Can we do the `nCol` calculation and column name on scala side? So, we
don't have to call `rFeatures` and `labels` on R side, which makes the logic
simpler.
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