spark git commit: [SPARK-18326][SPARKR][ML] Review SparkR ML wrappers API for 2.1
Repository: spark
Updated Branches:
refs/heads/master 82253617f -> 97255497d
[SPARK-18326][SPARKR][ML] Review SparkR ML wrappers API for 2.1
## What changes were proposed in this pull request?
Reviewing SparkR ML wrappers API for 2.1 release, mainly two issues:
* Remove ```probabilityCol``` from the argument list of ```spark.logit``` and
```spark.randomForest```. Since it was used when making prediction and should
be an argument of ```predict```, and we will work on this at
[SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) in the next
release cycle.
* Fix ```spark.als``` params to make it consistent with MLlib.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang
Closes #16169 from yanboliang/spark-18326.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/97255497
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/97255497
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/97255497
Branch: refs/heads/master
Commit: 97255497d885f0f8ccfc808e868bc8aa5e4d1063
Parents: 8225361
Author: Yanbo Liang
Authored: Wed Dec 7 20:23:28 2016 -0800
Committer: Yanbo Liang
Committed: Wed Dec 7 20:23:28 2016 -0800
--
R/pkg/R/mllib.R | 23 +---
R/pkg/inst/tests/testthat/test_mllib.R | 4 ++--
.../spark/ml/r/LogisticRegressionWrapper.scala | 4 +---
.../r/RandomForestClassificationWrapper.scala | 2 --
4 files changed, 13 insertions(+), 20 deletions(-)
--
http://git-wip-us.apache.org/repos/asf/spark/blob/97255497/R/pkg/R/mllib.R
--
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 074e9cb..632e4ad 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -733,7 +733,6 @@ setMethod("predict", signature(object = "KMeansModel"),
#' excepting that at most one value may be 0. The class with
largest value p/t is predicted, where p
#' is the original probability of that class and t is the
class's threshold.
#' @param weightCol The weight column name.
-#' @param probabilityCol column name for predicted class conditional
probabilities.
#' @param ... additional arguments passed to the method.
#' @return \code{spark.logit} returns a fitted logistic regression model
#' @rdname spark.logit
@@ -772,7 +771,7 @@ setMethod("predict", signature(object = "KMeansModel"),
setMethod("spark.logit", signature(data = "SparkDataFrame", formula =
"formula"),
function(data, formula, regParam = 0.0, elasticNetParam = 0.0,
maxIter = 100,
tol = 1E-6, family = "auto", standardization = TRUE,
- thresholds = 0.5, weightCol = NULL, probabilityCol =
"probability") {
+ thresholds = 0.5, weightCol = NULL) {
formula <- paste(deparse(formula), collapse = "")
if (is.null(weightCol)) {
@@ -784,7 +783,7 @@ setMethod("spark.logit", signature(data = "SparkDataFrame",
formula = "formula")
as.numeric(elasticNetParam),
as.integer(maxIter),
as.numeric(tol), as.character(family),
as.logical(standardization),
as.array(thresholds),
-as.character(weightCol),
as.character(probabilityCol))
+as.character(weightCol))
new("LogisticRegressionModel", jobj = jobj)
})
@@ -1425,7 +1424,7 @@ setMethod("predict", signature(object =
"GaussianMixtureModel"),
#' @param userCol column name for user ids. Ids must be (or can be coerced
into) integers.
#' @param itemCol column name for item ids. Ids must be (or can be coerced
into) integers.
#' @param rank rank of the matrix factorization (> 0).
-#' @param reg regularization parameter (>= 0).
+#' @param regParam regularization parameter (>= 0).
#' @param maxIter maximum number of iterations (>= 0).
#' @param nonnegative logical value indicating whether to apply nonnegativity
constraints.
#' @param implicitPrefs logical value indicating whether to use implicit
preference.
@@ -1464,21 +1463,21 @@ setMethod("predict", signature(object =
"GaussianMixtureModel"),
#'
#' # set other arguments
#' modelS <- spark.als(df, "rating", "user", "item", rank = 20,
-#' reg = 0.1, nonnegative = TRUE)
+#' regParam = 0.1, nonnegative = TRUE)
#' statsS <- summary(modelS)
#' }
#' @note spark.als since 2.1.0
setMethod("spark.als", signature(data = "SparkDataFrame"),
function(data, ratingCol = "rating", userCol = "user", itemCol =
"item",
- rank = 10, reg = 0.1, maxIter = 10, nonnegative = FALSE,
+ rank = 10, regParam = 0.1, maxIter = 10, nonnegative =
spark git commit: [SPARK-18326][SPARKR][ML] Review SparkR ML wrappers API for 2.1
Repository: spark
Updated Branches:
refs/heads/branch-2.1 ab865cfd9 -> 1c3f1da82
[SPARK-18326][SPARKR][ML] Review SparkR ML wrappers API for 2.1
## What changes were proposed in this pull request?
Reviewing SparkR ML wrappers API for 2.1 release, mainly two issues:
* Remove ```probabilityCol``` from the argument list of ```spark.logit``` and
```spark.randomForest```. Since it was used when making prediction and should
be an argument of ```predict```, and we will work on this at
[SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) in the next
release cycle.
* Fix ```spark.als``` params to make it consistent with MLlib.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang
Closes #16169 from yanboliang/spark-18326.
(cherry picked from commit 97255497d885f0f8ccfc808e868bc8aa5e4d1063)
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/1c3f1da8
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/1c3f1da8
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/1c3f1da8
Branch: refs/heads/branch-2.1
Commit: 1c3f1da82356426b6b550fee67e66dc82eaf1c85
Parents: ab865cf
Author: Yanbo Liang
Authored: Wed Dec 7 20:23:28 2016 -0800
Committer: Yanbo Liang
Committed: Wed Dec 7 20:23:45 2016 -0800
--
R/pkg/R/mllib.R | 23 +---
R/pkg/inst/tests/testthat/test_mllib.R | 4 ++--
.../spark/ml/r/LogisticRegressionWrapper.scala | 4 +---
.../r/RandomForestClassificationWrapper.scala | 2 --
4 files changed, 13 insertions(+), 20 deletions(-)
--
http://git-wip-us.apache.org/repos/asf/spark/blob/1c3f1da8/R/pkg/R/mllib.R
--
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 074e9cb..632e4ad 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -733,7 +733,6 @@ setMethod("predict", signature(object = "KMeansModel"),
#' excepting that at most one value may be 0. The class with
largest value p/t is predicted, where p
#' is the original probability of that class and t is the
class's threshold.
#' @param weightCol The weight column name.
-#' @param probabilityCol column name for predicted class conditional
probabilities.
#' @param ... additional arguments passed to the method.
#' @return \code{spark.logit} returns a fitted logistic regression model
#' @rdname spark.logit
@@ -772,7 +771,7 @@ setMethod("predict", signature(object = "KMeansModel"),
setMethod("spark.logit", signature(data = "SparkDataFrame", formula =
"formula"),
function(data, formula, regParam = 0.0, elasticNetParam = 0.0,
maxIter = 100,
tol = 1E-6, family = "auto", standardization = TRUE,
- thresholds = 0.5, weightCol = NULL, probabilityCol =
"probability") {
+ thresholds = 0.5, weightCol = NULL) {
formula <- paste(deparse(formula), collapse = "")
if (is.null(weightCol)) {
@@ -784,7 +783,7 @@ setMethod("spark.logit", signature(data = "SparkDataFrame",
formula = "formula")
as.numeric(elasticNetParam),
as.integer(maxIter),
as.numeric(tol), as.character(family),
as.logical(standardization),
as.array(thresholds),
-as.character(weightCol),
as.character(probabilityCol))
+as.character(weightCol))
new("LogisticRegressionModel", jobj = jobj)
})
@@ -1425,7 +1424,7 @@ setMethod("predict", signature(object =
"GaussianMixtureModel"),
#' @param userCol column name for user ids. Ids must be (or can be coerced
into) integers.
#' @param itemCol column name for item ids. Ids must be (or can be coerced
into) integers.
#' @param rank rank of the matrix factorization (> 0).
-#' @param reg regularization parameter (>= 0).
+#' @param regParam regularization parameter (>= 0).
#' @param maxIter maximum number of iterations (>= 0).
#' @param nonnegative logical value indicating whether to apply nonnegativity
constraints.
#' @param implicitPrefs logical value indicating whether to use implicit
preference.
@@ -1464,21 +1463,21 @@ setMethod("predict", signature(object =
"GaussianMixtureModel"),
#'
#' # set other arguments
#' modelS <- spark.als(df, "rating", "user", "item", rank = 20,
-#' reg = 0.1, nonnegative = TRUE)
+#' regParam = 0.1, nonnegative = TRUE)
#' statsS <- summary(modelS)
#' }
#' @note spark.als since 2.1.0
setMethod("spark.als", signature(data = "SparkDataFrame"),
function(data, ratingCol = "rating", userCol = "user", itemCol =
"item",
- rank = 10, reg = 0.1, maxI
