I’m using CrossValidator in pyspark (spark 1.4.1). I’ve seen in the class Estimator that all 'fit' are done sequentially. You can check the method _fit in CrossValidator class for the current implementation:
https://spark.apache.org/docs/1.4.1/api/python/_modules/pyspark/ml/tuning.html In the scala api <https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala#L67-L79> there is this comment: * Fits multiple models to the input data with multiple sets of parameters. * The default implementation uses a for loop on each parameter map. * Subclasses could override this to optimize multi-model training. Is it possible to parallelize CrossValidator on nFolds and numModels so that is faster? The times in comparison to R glmnet are not competitive, at least for dataframes under 3.5 million rows… Thanks! Julia. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/CrossValidator-speed-for-loop-on-each-parameter-map-tp24795.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
