spark git commit: [MINOR][SPARKR][ML] Joint coefficients with intercept for SparkR linear SVM summary.

2017-05-23 Thread yliang 
Repository: spark
Updated Branches:
  refs/heads/master 442287ae2 -> ad09e4ca0


[MINOR][SPARKR][ML] Joint coefficients with intercept for SparkR linear SVM 
summary.

## What changes were proposed in this pull request?
Joint coefficients with intercept for SparkR linear SVM summary.

## How was this patch tested?
Existing tests.

Author: Yanbo Liang 

Closes #18035 from yanboliang/svm-r.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/ad09e4ca
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/ad09e4ca
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/ad09e4ca

Branch: refs/heads/master
Commit: ad09e4ca045715d053a672c2ba23f598f06085d8
Parents: 442287a
Author: Yanbo Liang 
Authored: Tue May 23 16:16:14 2017 +0800
Committer: Yanbo Liang 
Committed: Tue May 23 16:16:14 2017 +0800

--
 R/pkg/R/mllib_classification.R  | 38 
 .../tests/testthat/test_mllib_classification.R  |  3 +-
 .../apache/spark/ml/r/LinearSVCWrapper.scala| 12 +--
 3 files changed, 26 insertions(+), 27 deletions(-)
--


http://git-wip-us.apache.org/repos/asf/spark/blob/ad09e4ca/R/pkg/R/mllib_classification.R
--
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
index 4db9cc3..306a9b8 100644
--- a/R/pkg/R/mllib_classification.R
+++ b/R/pkg/R/mllib_classification.R
@@ -46,15 +46,16 @@ setClass("MultilayerPerceptronClassificationModel", 
representation(jobj = "jobj"
 #' @note NaiveBayesModel since 2.0.0
 setClass("NaiveBayesModel", representation(jobj = "jobj"))
 
-#' linear SVM Model
+#' Linear SVM Model
 #'
-#' Fits an linear SVM model against a SparkDataFrame. It is a binary 
classifier, similar to svm in glmnet package
+#' Fits a linear SVM model against a SparkDataFrame, similar to svm in e1071 
package.
+#' Currently only supports binary classification model with linear kernel.
 #' Users can print, make predictions on the produced model and save the model 
to the input path.
 #'
 #' @param data SparkDataFrame for training.
 #' @param formula A symbolic description of the model to be fitted. Currently 
only a few formula
 #'operators are supported, including '~', '.', ':', '+', and 
'-'.
-#' @param regParam The regularization parameter.
+#' @param regParam The regularization parameter. Only supports L2 
regularization currently.
 #' @param maxIter Maximum iteration number.
 #' @param tol Convergence tolerance of iterations.
 #' @param standardization Whether to standardize the training features before 
fitting the model. The coefficients
@@ -111,10 +112,10 @@ setMethod("spark.svmLinear", signature(data = 
"SparkDataFrame", formula = "formu
 new("LinearSVCModel", jobj = jobj)
   })
 
-#  Predicted values based on an LinearSVCModel model
+#  Predicted values based on a LinearSVCModel model
 
 #' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns the predicted values based on an 
LinearSVCModel.
+#' @return \code{predict} returns the predicted values based on a 
LinearSVCModel.
 #' @rdname spark.svmLinear
 #' @aliases predict,LinearSVCModel,SparkDataFrame-method
 #' @export
@@ -124,13 +125,12 @@ setMethod("predict", signature(object = "LinearSVCModel"),
 predict_internal(object, newData)
   })
 
-#  Get the summary of an LinearSVCModel
+#  Get the summary of a LinearSVCModel
 
-#' @param object an LinearSVCModel fitted by \code{spark.svmLinear}.
+#' @param object a LinearSVCModel 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
@@ -138,22 +138,14 @@ setMethod("predict", signature(object = "LinearSVCModel"),
 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

spark git commit: [MINOR][SPARKR][ML] Joint coefficients with intercept for SparkR linear SVM summary.

2017-05-23 Thread yliang 
Repository: spark
Updated Branches:
  refs/heads/branch-2.2 06c985c1b -> dbb068f4f


[MINOR][SPARKR][ML] Joint coefficients with intercept for SparkR linear SVM 
summary.

## What changes were proposed in this pull request?
Joint coefficients with intercept for SparkR linear SVM summary.

## How was this patch tested?
Existing tests.

Author: Yanbo Liang 

Closes #18035 from yanboliang/svm-r.

(cherry picked from commit ad09e4ca045715d053a672c2ba23f598f06085d8)
Signed-off-by: Yanbo Liang 


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/dbb068f4
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/dbb068f4
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/dbb068f4

Branch: refs/heads/branch-2.2
Commit: dbb068f4f280fd48c991302f9e9728378926b1a2
Parents: 06c985c
Author: Yanbo Liang 
Authored: Tue May 23 16:16:14 2017 +0800
Committer: Yanbo Liang 
Committed: Tue May 23 16:16:29 2017 +0800

--
 R/pkg/R/mllib_classification.R  | 38 
 .../tests/testthat/test_mllib_classification.R  |  3 +-
 .../apache/spark/ml/r/LinearSVCWrapper.scala| 12 +--
 3 files changed, 26 insertions(+), 27 deletions(-)
--


http://git-wip-us.apache.org/repos/asf/spark/blob/dbb068f4/R/pkg/R/mllib_classification.R
--
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
index 4db9cc3..306a9b8 100644
--- a/R/pkg/R/mllib_classification.R
+++ b/R/pkg/R/mllib_classification.R
@@ -46,15 +46,16 @@ setClass("MultilayerPerceptronClassificationModel", 
representation(jobj = "jobj"
 #' @note NaiveBayesModel since 2.0.0
 setClass("NaiveBayesModel", representation(jobj = "jobj"))
 
-#' linear SVM Model
+#' Linear SVM Model
 #'
-#' Fits an linear SVM model against a SparkDataFrame. It is a binary 
classifier, similar to svm in glmnet package
+#' Fits a linear SVM model against a SparkDataFrame, similar to svm in e1071 
package.
+#' Currently only supports binary classification model with linear kernel.
 #' Users can print, make predictions on the produced model and save the model 
to the input path.
 #'
 #' @param data SparkDataFrame for training.
 #' @param formula A symbolic description of the model to be fitted. Currently 
only a few formula
 #'operators are supported, including '~', '.', ':', '+', and 
'-'.
-#' @param regParam The regularization parameter.
+#' @param regParam The regularization parameter. Only supports L2 
regularization currently.
 #' @param maxIter Maximum iteration number.
 #' @param tol Convergence tolerance of iterations.
 #' @param standardization Whether to standardize the training features before 
fitting the model. The coefficients
@@ -111,10 +112,10 @@ setMethod("spark.svmLinear", signature(data = 
"SparkDataFrame", formula = "formu
 new("LinearSVCModel", jobj = jobj)
   })
 
-#  Predicted values based on an LinearSVCModel model
+#  Predicted values based on a LinearSVCModel model
 
 #' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns the predicted values based on an 
LinearSVCModel.
+#' @return \code{predict} returns the predicted values based on a 
LinearSVCModel.
 #' @rdname spark.svmLinear
 #' @aliases predict,LinearSVCModel,SparkDataFrame-method
 #' @export
@@ -124,13 +125,12 @@ setMethod("predict", signature(object = "LinearSVCModel"),
 predict_internal(object, newData)
   })
 
-#  Get the summary of an LinearSVCModel
+#  Get the summary of a LinearSVCModel
 
-#' @param object an LinearSVCModel fitted by \code{spark.svmLinear}.
+#' @param object a LinearSVCModel 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
@@ -138,22 +138,14 @@ setMethod("predict", signature(object = "LinearSVCModel"),
 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)
-