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From felixche...@apache.org
Subject spark git commit: [SPARK-20906][SPARKR] Add API doc example for Constrained Logistic Regression
Date Wed, 24 Jan 2018 17:38:00 GMT
Repository: spark
Updated Branches:
  refs/heads/master 0ec95bb7d -> e18d6f532


[SPARK-20906][SPARKR] Add API doc example for Constrained Logistic Regression

## What changes were proposed in this pull request?

doc only changes

## How was this patch tested?

manual

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #20380 from felixcheung/rclrdoc.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/e18d6f53
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/e18d6f53
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/e18d6f53

Branch: refs/heads/master
Commit: e18d6f5326e0d9ea03d31de5ce04cb84d3b8ab37
Parents: 0ec95bb
Author: Felix Cheung <felixcheung_m@hotmail.com>
Authored: Wed Jan 24 09:37:54 2018 -0800
Committer: Felix Cheung <felixcheung@apache.org>
Committed: Wed Jan 24 09:37:54 2018 -0800

----------------------------------------------------------------------
 R/pkg/R/mllib_classification.R                    | 15 ++++++++++++++-
 R/pkg/tests/fulltests/test_mllib_classification.R | 10 +++++-----
 2 files changed, 19 insertions(+), 6 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/e18d6f53/R/pkg/R/mllib_classification.R
----------------------------------------------------------------------
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
index 7cd072a..f6e9b13 100644
--- a/R/pkg/R/mllib_classification.R
+++ b/R/pkg/R/mllib_classification.R
@@ -279,11 +279,24 @@ function(object, path, overwrite = FALSE) {
 #' savedModel <- read.ml(path)
 #' summary(savedModel)
 #'
-#' # multinomial logistic regression
+#' # binary logistic regression against two classes with
+#' # upperBoundsOnCoefficients and upperBoundsOnIntercepts
+#' ubc <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4)
+#' model <- spark.logit(training, Species ~ .,
+#'                       upperBoundsOnCoefficients = ubc,
+#'                       upperBoundsOnIntercepts = 1.0)
 #'
+#' # multinomial logistic regression
 #' model <- spark.logit(training, Class ~ ., regParam = 0.5)
 #' summary <- summary(model)
 #'
+#' # multinomial logistic regression with
+#' # lowerBoundsOnCoefficients and lowerBoundsOnIntercepts
+#' lbc <- matrix(c(0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0), nrow = 2, ncol = 4)
+#' lbi <- as.array(c(0.0, 0.0))
+#' model <- spark.logit(training, Species ~ ., family = "multinomial",
+#'                      lowerBoundsOnCoefficients = lbc,
+#'                      lowerBoundsOnIntercepts = lbi)
 #' }
 #' @note spark.logit since 2.1.0
 setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"),

http://git-wip-us.apache.org/repos/asf/spark/blob/e18d6f53/R/pkg/tests/fulltests/test_mllib_classification.R
----------------------------------------------------------------------
diff --git a/R/pkg/tests/fulltests/test_mllib_classification.R b/R/pkg/tests/fulltests/test_mllib_classification.R
index ad47717..a46c47d 100644
--- a/R/pkg/tests/fulltests/test_mllib_classification.R
+++ b/R/pkg/tests/fulltests/test_mllib_classification.R
@@ -124,7 +124,7 @@ test_that("spark.logit", {
   # Petal.Width   0.42122607
   # nolint end
 
-  # Test multinomial logistic regression againt three classes
+  # Test multinomial logistic regression against three classes
   df <- suppressWarnings(createDataFrame(iris))
   model <- spark.logit(df, Species ~ ., regParam = 0.5)
   summary <- summary(model)
@@ -196,7 +196,7 @@ test_that("spark.logit", {
   #
   # nolint end
 
-  # Test multinomial logistic regression againt two classes
+  # Test multinomial logistic regression against two classes
   df <- suppressWarnings(createDataFrame(iris))
   training <- df[df$Species %in% c("versicolor", "virginica"), ]
   model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial")
@@ -208,7 +208,7 @@ test_that("spark.logit", {
   expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
   expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
 
-  # Test binomial logistic regression againt two classes
+  # Test binomial logistic regression against two classes
   model <- spark.logit(training, Species ~ ., regParam = 0.5)
   summary <- summary(model)
   coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
@@ -239,7 +239,7 @@ test_that("spark.logit", {
   prediction2 <- collect(select(predict(model2, df2), "prediction"))
   expect_equal(sort(prediction2$prediction), c("0.0", "0.0", "0.0", "0.0", "0.0"))
 
-  # Test binomial logistic regression againt two classes with upperBoundsOnCoefficients
+  # Test binomial logistic regression against two classes with upperBoundsOnCoefficients
   # and upperBoundsOnIntercepts
   u <- matrix(c(1.0, 0.0, 1.0, 0.0), nrow = 1, ncol = 4)
   model <- spark.logit(training, Species ~ ., upperBoundsOnCoefficients = u,
@@ -252,7 +252,7 @@ test_that("spark.logit", {
   expect_error(spark.logit(training, Species ~ ., upperBoundsOnCoefficients = as.array(c(1,
2)),
                            upperBoundsOnIntercepts = 1.0))
 
-  # Test binomial logistic regression againt two classes with lowerBoundsOnCoefficients
+  # Test binomial logistic regression against two classes with lowerBoundsOnCoefficients
   # and lowerBoundsOnIntercepts
   l <- matrix(c(0.0, -1.0, 0.0, -1.0), nrow = 1, ncol = 4)
   model <- spark.logit(training, Species ~ ., lowerBoundsOnCoefficients = l,


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