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https://issues.apache.org/jira/browse/SPARK-9062?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14631027#comment-14631027
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yuhao yang commented on SPARK-9062:
-----------------------------------
This jira is trying to chain the tokenizer and Word2vec (and countVectorizer
and possible stopwordsRemover)
{code}
val texts = sc.textFile("/home/yuhao/workspace/DocSet/20_newsgroups/train.txt")
val df = texts.toDF("text")
val tokenizer = new RegexTokenizer()
.setInputCol("text")
.setOutputCol("tokens")
.setPattern("\\W+")
val w2v = new Word2Vec()
.setInputCol("tokens")
.setOutputCol("vector")
val pipeline = new Pipeline()
.setStages(Array(tokenizer, w2v))
val w2vModel = pipeline.fit(df)
val result = w2vModel.transform(df)
{code}
Currently it throws {code}java.lang.IllegalArgumentException: requirement
failed: Column tokens must be of type ArrayType(StringType,true) but was
actually ArrayType(StringType,false).{code}
And it seems {code}udf { sentence: Seq[String] => ...{code} is only compatible
with {code} new ArrayType(StringType, true){code}. I tried to change input type
of Word2Vec to new ArrayType(StringType, false), and the code above reports
{code}Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot
resolve 'UDF(tokens)' due to data type mismatch: argument 1 is expected to be
of type array<string>, however, 'tokens' is of type array<string>.;{code} in
current master code (1.4 don't report it)
> Change output type of Tokenizer to Array(String, true)
> ------------------------------------------------------
>
> Key: SPARK-9062
> URL: https://issues.apache.org/jira/browse/SPARK-9062
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: yuhao yang
> Priority: Minor
>
> Currently output type of Tokenizer is Array(String, false), which is not
> compatible with Word2Vec and Other transformers since their input type is
> Array(String, true). Seq[String] in udf will be treated as Array(String,
> true) by default.
> I'm also thinking for Nullable columns, maybe tokenizer should return
> Array(null) for null value in the input.
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