From 111a62474a2fb7f4e7f19fcfb8efaae37aa40400 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 12 Apr 2016 11:34:40 -0700
Subject: [PATCH] [SPARK-14147][ML][SPARKR] SparkR predict should not output
feature column
## What changes were proposed in this pull request?
SparkR does not support type of vector which is the default type of feature column in ML. R predict also does not output intermediate feature column. So SparkR ```predict``` should not output feature column. In this PR, I only fix this issue for ```naiveBayes``` and ```survreg```. ```kmeans``` has the right code route already and ```glm``` will be fixed at SparkRWrapper refactor(#12294).
## How was this patch tested?
No new tests.
cc mengxr shivaram
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #11958 from yanboliang/spark-14147.
---
.../org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala | 2 +-
.../main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala | 4 +++-
2 files changed, 4 insertions(+), 2 deletions(-)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
index 2ae411555f..7835468626 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.scala
@@ -44,7 +44,7 @@ private[r] class AFTSurvivalRegressionWrapper private (
}
def transform(dataset: Dataset[_]): DataFrame = {
- pipeline.transform(dataset)
+ pipeline.transform(dataset).drop(aftModel.getFeaturesCol)
}
}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala
index 2cd709d2ee..b17207e99b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/NaiveBayesWrapper.scala
@@ -37,7 +37,9 @@ private[r] class NaiveBayesWrapper private (
lazy val tables: Array[Double] = naiveBayesModel.theta.toArray.map(math.exp)
def transform(dataset: Dataset[_]): DataFrame = {
- pipeline.transform(dataset).drop(PREDICTED_LABEL_INDEX_COL)
+ pipeline.transform(dataset)
+ .drop(PREDICTED_LABEL_INDEX_COL)
+ .drop(naiveBayesModel.getFeaturesCol)
}
}
--
GitLab