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| author | Xiangrui Meng <meng@databricks.com> | 2016-06-21 08:31:15 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2016-06-21 08:31:15 -0700 |
| commit | 4f83ca1059a3b580fca3f006974ff5ac4d5212a1 (patch) | |
| tree | 0d1c12a4c1f67574acdad034bc909e17117fa10b /R/pkg/inst | |
| parent | f3a768b7b96f00f33d2fe4e6c0bf4acf373ad4f4 (diff) | |
| download | spark-4f83ca1059a3b580fca3f006974ff5ac4d5212a1.tar.gz spark-4f83ca1059a3b580fca3f006974ff5ac4d5212a1.tar.bz2 spark-4f83ca1059a3b580fca3f006974ff5ac4d5212a1.zip | |
[SPARK-15177][.1][R] make SparkR model params and default values consistent with MLlib
## What changes were proposed in this pull request?
This PR is a subset of #13023 by yanboliang to make SparkR model param names and default values consistent with MLlib. I tried to avoid other changes from #13023 to keep this PR minimal. I will send a follow-up PR to improve the documentation.
Main changes:
* `spark.glm`: epsilon -> tol, maxit -> maxIter
* `spark.kmeans`: default k -> 2, default maxIter -> 20, default initMode -> "k-means||"
* `spark.naiveBayes`: laplace -> smoothing, default 1.0
## How was this patch tested?
Existing unit tests.
Author: Xiangrui Meng <meng@databricks.com>
Closes #13801 from mengxr/SPARK-15177.1.
Diffstat (limited to 'R/pkg/inst')
| -rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R index c8c5ef2476..753da81760 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -288,7 +288,7 @@ test_that("spark.kmeans", { take(training, 1) - model <- spark.kmeans(data = training, ~ ., k = 2) + model <- spark.kmeans(data = training, ~ ., k = 2, maxIter = 10, initMode = "random") sample <- take(select(predict(model, training), "prediction"), 1) expect_equal(typeof(sample$prediction), "integer") expect_equal(sample$prediction, 1) @@ -363,7 +363,7 @@ test_that("spark.naiveBayes", { t <- as.data.frame(Titanic) t1 <- t[t$Freq > 0, -5] df <- suppressWarnings(createDataFrame(t1)) - m <- spark.naiveBayes(df, Survived ~ .) + m <- spark.naiveBayes(df, Survived ~ ., smoothing = 0.0) s <- summary(m) expect_equal(as.double(s$apriori[1, "Yes"]), 0.5833333, tolerance = 1e-6) expect_equal(sum(s$apriori), 1) |