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https://issues.apache.org/jira/browse/SPARK-18213?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15635662#comment-15635662
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Wojciech Szymanski commented on SPARK-18213:
--------------------------------------------
Thanks for your opinion. Initially I was thinking about varargs based
constructor, since stage array is the only one attribute supported by pipeline.
{code}
// only Scala
val pipeline = new Pipeline(tokenizer, stopWordsRemover, countVectorizer)
{code}
Unfortunately, current Scala compiler does not support generating pure Java
varargs constructors with @varargs annotation.
Another option is companion object, but again, it wouldn't be convenient from
Java perspective.
{code}
// Scala
val pipeline = Pipeline(tokenizer, stopWordsRemover, countVectorizer)
// Java - ugly approach
Pipeline pipeline = Pipeline.apply(tokenizer, stopWordsRemover,
countVectorizer);
{code}
Last thing that comes to my mind is array based constructor, but on the other
hand it does not simplify much.
// Scala
val pipeline = new Pipeline(Array(tokenizer, stopWordsRemover, countVectorizer))
// Java
Pipeline pipeline = Pipeline.apply(new Pipeline[] {tokenizer, stopWordsRemover,
countVectorizer});
{code}
> Syntactic sugar over Pipeline API
> ---------------------------------
>
> Key: SPARK-18213
> URL: https://issues.apache.org/jira/browse/SPARK-18213
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.0.1
> Reporter: Wojciech Szymanski
> Priority: Minor
>
> Currently, creating ML Pipeline is based on very verbose setStages method as
> below:
> {code}
> val tokenizer = new RegexTokenizer()
> val stopWordsRemover = new StopWordsRemover()
> val countVectorizer = new CountVectorizer()
> val pipeline = new Pipeline().setStages(Array(tokenizer,
> stopWordsRemover, countVectorizer))
> {code}
> What about a bit of syntactic sugar over Pipeline API?
> {code}
> val tokenizer = new RegexTokenizer()
> val stopWordsRemover = new StopWordsRemover()
> val countVectorizer = new CountVectorizer()
> val pipeline = tokenizer + stopWordsRemover + countVectorizer
> {code}
> Production code changes in
> mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala:
> https://github.com/apache/spark/commit/181df64bf50081f3af5a84b567b677178c88524f#diff-5226e84dea43423760dc6300ddafb01b
> Scala example:
> https://github.com/apache/spark/commit/181df64bf50081f3af5a84b567b677178c88524f#diff-798e85dd9107565fabab1126f57e3d6e
> Java example:
> https://github.com/apache/spark/commit/181df64bf50081f3af5a84b567b677178c88524f#diff-69ac857220f21b5e1684444d80d6dffe
> Thanks in advance for your feedback.
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