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Thanks! Merged into master.
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@mengxr it looks great, I have no comment
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@yinxusen I checked the implementation in e1071 and found that it supports
both categorical and continuous features, which in MLlib we only support
categorical features.
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@mengxr @yanboliang Since the `ml.NaiveBayes` making the assumption that
its input data's label is 0-based indices, we should add a `StringIndexer` for
labels after `R
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
---
@@ -248,6 +248,18 @@ class RFormulaModel private[feature](
!columnN
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -141,3 +141,26 @@ test_that("kmeans", {
cluster <- summary.model$cluster
expect_equal(s
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
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@@ -248,6 +248,18 @@ class RFormulaModel private[feature](
!colum
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--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala ---
@@ -227,6 +233,35 @@ class NaiveBayesModel private[ml] (
Github user felixcheung commented on a diff in the pull request:
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--- Diff: R/pkg/R/mllib.R ---
@@ -71,14 +71,23 @@ setMethod("glm", signature(formula = "formula", family
= "ANY", data = "DataFram
#' @rdname predic
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@mengxr One more thing, could you take a look at
https://issues.apache.org/jira/browse/SPARK-13641? If we extract feature names
from the RFormulaModel transformed data
Github user yinxusen commented on the pull request:
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I'll try to extract raw labels.
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -141,3 +141,26 @@ test_that("kmeans", {
cluster <- summary.model$cluster
expect_equal(s
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -141,3 +141,26 @@ test_that("kmeans", {
cluster <- summary.model$cluster
expect_equal(sor
Github user mengxr commented on the pull request:
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About labels, I think we should output the raw labels as predictions
instead of the encoded indices. It is hard to extract the feature metadata in
SparkR.
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
@@ -141,3 +141,26 @@ test_that("kmeans", {
cluster <- summary.model$cluster
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--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala ---
@@ -227,6 +236,25 @@ class NaiveBayesModel private[ml] (
@
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
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cluster <- summary.model$cluster
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
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cluster <- summary.model$cluster
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--- Diff: R/pkg/inst/tests/testthat/test_mllib.R ---
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cluster <- summary.model$cluster
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--- Diff: R/pkg/R/generics.R ---
@@ -1168,3 +1168,7 @@ setGeneric("kmeans")
#' @rdname fitted
#' @export
setGeneric("fitted")
+
+#' @r
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--- Diff: R/pkg/R/generics.R ---
@@ -1168,3 +1168,7 @@ setGeneric("kmeans")
#' @rdname fitted
#' @export
setGeneric("fitted")
+
+#' @rdn
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--- Diff: R/pkg/R/mllib.R ---
@@ -192,3 +210,37 @@ setMethod("fitted", signature(object =
"PipelineModel"),
stop(paste("Unsupported model
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@felixcheung Thanks for the review.
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From SparkR test failure:
```
1. Error: naiveBayes
---
there is no package called 'mlbench'
1
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#' @rdname fitted
#' @export
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+
+#'
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retest this please
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retest it please
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@yanboliang @jkbradley
For this PR, I extract labels manually from labelCol. But I still don't
think it's good to make assumption first for labels to be 0-bas
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I think it works. I'll try to add it later.
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Github user yanboliang commented on the pull request:
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It's a good question! It's possible that the label of input dataset is not
0 based or not continuous. So we should use ```StringIndexer``` to index label
in [0, numL
Github user yinxusen commented on the pull request:
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I can see from the mllib.NaiveBayes that the labels are sorted. But how
about if it is not 0 based or not continuous? Say, `1.0, 3.0, 5.0, ...`. It has
no effect on tr
Github user yanboliang commented on the pull request:
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Labels of ML ```NaiveBayesModel``` are sorted(FYI #7284), so we do not need
to store it as member variable. Then it can pass the binary compatibility check.
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test it please
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GitHub user yinxusen opened a pull request:
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[SPARK-13449] Naive Bayes wrapper in SparkR
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13449
Add a Naive Bayes wrapper in Spar
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