Right now you are correct that Spark ML APIs do not support predicting on a single instance (whether Vector for the models or a Row for a pipeline).
See https://issues.apache.org/jira/browse/SPARK-10413 and https://issues.apache.org/jira/browse/SPARK-16431 (duplicate) for some discussion. There may be movement in the short term to support the single Vector case. But anything for pipelines is not immediately on the horizon I'd say. N On Thu, 1 Sep 2016 at 15:21 Aseem Bansal <asmbans...@gmail.com> wrote: > I understand from a theoretical perspective that the model itself is not > distributed. Thus it can be used for making predictions for a vector or a > RDD. But speaking in terms of the APIs provided by spark 2.0.0 when I > create a model from a large data the recommended way is to use the ml > library for fit. I have the option of getting a > http://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/classification/NaiveBayesModel.html > or wrapping it as > http://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/PipelineModel.html > > Both of these do not have any method which supports Vectors. How do I > bridge this gap in the API from my side? Is there anything in Spark's API > which I have missed? Or do I need to extract the parameters and use another > library for the predictions for a single row? > > On Thu, Sep 1, 2016 at 6:38 PM, Sean Owen <so...@cloudera.com> wrote: > >> How the model is built isn't that related to how it scores things. >> Here we're just talking about scoring. NaiveBayesModel can score >> Vector which is not a distributed entity. That's what you want to use. >> You do not want to use a whole distributed operation to score one >> record. This isn't related to .ml vs .mllib APIs. >> >> On Thu, Sep 1, 2016 at 2:01 PM, Aseem Bansal <asmbans...@gmail.com> >> wrote: >> > I understand your point. >> > >> > Is there something like a bridge? Is it possible to convert the model >> > trained using Dataset<Row> (i.e. the distributed one) to the one which >> uses >> > vectors? In Spark 1.6 the mllib packages had everything as per vectors >> and >> > that should be faster as per my understanding. But in many spark blogs >> we >> > saw that spark is moving towards the ml package and mllib package will >> be >> > phased out. So how can someone train using huge data and then use it on >> a >> > row by row basis? >> > >> > Thanks for your inputs. >> > >> > On Thu, Sep 1, 2016 at 6:15 PM, Sean Owen <so...@cloudera.com> wrote: >> >> >> >> If you're trying to score a single example by way of an RDD or >> >> Dataset, then no it will never be that fast. It's a whole distributed >> >> operation, and while you might manage low latency for one job at a >> >> time, consider what will happen when hundreds of them are running at >> >> once. It's just huge overkill for scoring a single example (but, >> >> pretty fine for high-er latency, high throughput batch operations) >> >> >> >> However if you're scoring a Vector locally I can't imagine it's that >> >> slow. It does some linear algebra but it's not that complicated. Even >> >> something unoptimized should be fast. >> >> >> >> On Thu, Sep 1, 2016 at 1:37 PM, Aseem Bansal <asmbans...@gmail.com> >> wrote: >> >> > Hi >> >> > >> >> > Currently trying to use NaiveBayes to make predictions. But facing >> >> > issues >> >> > that doing the predictions takes order of few seconds. I tried with >> >> > other >> >> > model examples shipped with Spark but they also ran in minimum of >> 500 ms >> >> > when I used Scala API. With >> >> > >> >> > Has anyone used spark ML to do predictions for a single row under 20 >> ms? >> >> > >> >> > I am not doing premature optimization. The use case is that we are >> doing >> >> > real time predictions and we need results 20ms. Maximum 30ms. This >> is a >> >> > hard >> >> > limit for our use case. >> > >> > >> > >
