[
https://issues.apache.org/jira/browse/SPARK-22974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
William Zhang updated SPARK-22974:
----------------------------------
Description:
If CountVectorModel transforms columns, the output column will not have
attributes attached to them. If later on, those columns are used in Interaction
transformer, an exception will be thrown:
{quote}"org.apache.spark.SparkException: Vector attributes must be defined for
interaction."
{quote}
To reproduce it:
{quote}import org.apache.spark.ml.feature._
import org.apache.spark.sql.functions._
val df = spark.createDataFrame(Seq(
(0, Array("a", "b", "c"), Array("1", "2")),
(1, Array("a", "b", "b", "c", "a", "d"), Array("1", "2", "3"))
)).toDF("id", "words", "nums")
val cvModel: CountVectorizerModel = new CountVectorizer()
.setInputCol("nums")
.setOutputCol("features2")
.setVocabSize(4)
.setMinDF(0)
.fit(df)
]val cvm = new CountVectorizerModel(Array("a", "b", "c"))
.setInputCol("words")
.setOutputCol("features1")
val df1 = cvm.transform(df)
val df2 = cvModel.transform(df1)
val interaction = new Interaction().setInputCols(Array("features1",
"features2")).setOutputCol("features")
val df3 = interaction.transform(df2)
{quote}
was:
If CountVectorModel transforms columns, the output column will not have
attributes attached to them. If later on, those columns are used in Interaction
transformer, an exception will be thrown:
{quote}"org.apache.spark.SparkException: Vector attributes must be defined for
interaction."
{quote}
To reproduce it:
{quote}import org.apache.spark.ml.feature._
import org.apache.spark.sql.functions._
import org.apache.spark.ml.linalg.{SparseVector, Vector}
val df = spark.createDataFrame(Seq(
(0, Array("a", "b", "c"), Array("1", "2")),
(1, Array("a", "b", "b", "c", "a", "d"), Array("1", "2", "3"))
)).toDF("id", "words", "nums")
val cvModel: CountVectorizerModel = new CountVectorizer()
.setInputCol("nums")
.setOutputCol("features2")
.setVocabSize(4)
.setMinDF(0)
.fit(df)
]val cvm = new CountVectorizerModel(Array("a", "b", "c"))
.setInputCol("words")
.setOutputCol("features1")
val df1 = cvm.transform(df)
val df2 = cvModel.transform(df1)
val interaction = new Interaction().setInputCols(Array("features1",
"features2")).setOutputCol("features")
val df3 = interaction.transform(df2)
{quote}
> CountVectorModel does not attach attributes to output column
> ------------------------------------------------------------
>
> Key: SPARK-22974
> URL: https://issues.apache.org/jira/browse/SPARK-22974
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.2.1
> Reporter: William Zhang
>
> If CountVectorModel transforms columns, the output column will not have
> attributes attached to them. If later on, those columns are used in
> Interaction transformer, an exception will be thrown:
> {quote}"org.apache.spark.SparkException: Vector attributes must be defined
> for interaction."
> {quote}
> To reproduce it:
> {quote}import org.apache.spark.ml.feature._
> import org.apache.spark.sql.functions._
> val df = spark.createDataFrame(Seq(
> (0, Array("a", "b", "c"), Array("1", "2")),
> (1, Array("a", "b", "b", "c", "a", "d"), Array("1", "2", "3"))
> )).toDF("id", "words", "nums")
> val cvModel: CountVectorizerModel = new CountVectorizer()
> .setInputCol("nums")
> .setOutputCol("features2")
> .setVocabSize(4)
> .setMinDF(0)
> .fit(df)
> ]val cvm = new CountVectorizerModel(Array("a", "b", "c"))
> .setInputCol("words")
> .setOutputCol("features1")
>
> val df1 = cvm.transform(df)
> val df2 = cvModel.transform(df1)
> val interaction = new Interaction().setInputCols(Array("features1",
> "features2")).setOutputCol("features")
> val df3 = interaction.transform(df2)
> {quote}
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