[
https://issues.apache.org/jira/browse/SPARK-20711?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
zhengruifeng updated SPARK-20711:
---------------------------------
Description:
{code}
scala> val summarizer = new MultivariateOnlineSummarizer()
summarizer: org.apache.spark.mllib.stat.MultivariateOnlineSummarizer =
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
scala> summarizer.add(Vectors.dense(Double.NaN, -10.0))
res20: summarizer.type =
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
scala> summarizer.add(Vectors.dense(Double.NaN, 2.0))
res21: summarizer.type =
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
scala> summarizer.min
res22: org.apache.spark.mllib.linalg.Vector = [1.7976931348623157E308,-10.0]
scala> summarizer.max
res23: org.apache.spark.mllib.linalg.Vector = [-1.7976931348623157E308,2.0]
{code}
For a feature only containing {{Double.NaN}}, the returned max is
{{Double.MinValue}} and the min is {{Double.MaxValue}}.
{code}
import org.apache.spark.ml.stat._
val df = spark.createDataFrame(Seq(
(1, 2.3, Vectors.dense(Double.NaN, 0.0, 0.0)),
(2, 6.7, Vectors.dense(Double.NaN, 0.2, 0.0))
)).toDF("id", "num", "features")
df.select(Summarizer.metrics("mean").summary(col("features"))).head
res2: org.apache.spark.sql.Row = [[WrappedArray(NaN, 0.1, 0.0)]]
df.select(Summarizer.metrics("min").summary(col("features"))).head
res3: org.apache.spark.sql.Row = [[WrappedArray(1.7976931348623157E308, 0.0,
0.0)]]
{code}
was:
{code}
scala> val summarizer = new MultivariateOnlineSummarizer()
summarizer: org.apache.spark.mllib.stat.MultivariateOnlineSummarizer =
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
scala> summarizer.add(Vectors.dense(Double.NaN, -10.0))
res20: summarizer.type =
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
scala> summarizer.add(Vectors.dense(Double.NaN, 2.0))
res21: summarizer.type =
org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
scala> summarizer.min
res22: org.apache.spark.mllib.linalg.Vector = [1.7976931348623157E308,-10.0]
scala> summarizer.max
res23: org.apache.spark.mllib.linalg.Vector = [-1.7976931348623157E308,2.0]
{code}
For a feature only containing {{Double.NaN}}, the returned max is
{{Double.MinValue}} and the min is {{Double.MaxValue}}.
> MultivariateOnlineSummarizer incorrect min/max for NaN value
> ------------------------------------------------------------
>
> Key: SPARK-20711
> URL: https://issues.apache.org/jira/browse/SPARK-20711
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.2.0
> Reporter: zhengruifeng
> Priority: Minor
>
> {code}
> scala> val summarizer = new MultivariateOnlineSummarizer()
> summarizer: org.apache.spark.mllib.stat.MultivariateOnlineSummarizer =
> org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
> scala> summarizer.add(Vectors.dense(Double.NaN, -10.0))
> res20: summarizer.type =
> org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
> scala> summarizer.add(Vectors.dense(Double.NaN, 2.0))
> res21: summarizer.type =
> org.apache.spark.mllib.stat.MultivariateOnlineSummarizer@2ac58d
> scala> summarizer.min
> res22: org.apache.spark.mllib.linalg.Vector = [1.7976931348623157E308,-10.0]
> scala> summarizer.max
> res23: org.apache.spark.mllib.linalg.Vector = [-1.7976931348623157E308,2.0]
> {code}
> For a feature only containing {{Double.NaN}}, the returned max is
> {{Double.MinValue}} and the min is {{Double.MaxValue}}.
> {code}
> import org.apache.spark.ml.stat._
> val df = spark.createDataFrame(Seq(
> (1, 2.3, Vectors.dense(Double.NaN, 0.0, 0.0)),
> (2, 6.7, Vectors.dense(Double.NaN, 0.2, 0.0))
> )).toDF("id", "num", "features")
> df.select(Summarizer.metrics("mean").summary(col("features"))).head
> res2: org.apache.spark.sql.Row = [[WrappedArray(NaN, 0.1, 0.0)]]
> df.select(Summarizer.metrics("min").summary(col("features"))).head
> res3: org.apache.spark.sql.Row = [[WrappedArray(1.7976931348623157E308, 0.0,
> 0.0)]]
> {code}
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