What template are you using? If it is one of the templates in the Apache repos, you may want to file a bug report. If PIO supports Spark 2.x, the Apache Templates should also IMHO.
From: Daniel O' Shaughnessy <[email protected]> Reply: [email protected] <[email protected]> Date: January 30, 2018 at 9:09:49 AM To: [email protected] <[email protected]> Subject: Re: Using Dataframe API vs. RDD API? Hi Shane, You need to use PAlgorithm instead of P2Algorithm and save/load the spark context accordingly. This way you can use spark context in the predict function. There are examples of using PAlgorithm on the predictionio Site. It’s slightly more complicated but not too bad! On Tue, 30 Jan 2018 at 17:06, Shane Johnson <[email protected]> wrote: Thanks team! We are close to having our models working with the Dataframe API. One additional roadblock we are hitting is the fundamental difference in the RDD based API vs the Dataframe API. It seems that the old mllib API would allow a simple vector to get predictions where in the new ml API a dataframe is required. This presents a challenge as the predict function in PredictionIO does not have a spark context. Any ideas how to overcome this? Am I thinking through this correctly or are there other ways to get predictions with the new ml Dataframe API without having a dataframe as input? Best, Shane Shane Johnson | 801.360.3350 LinkedIn | Facebook 2018-01-08 20:37 GMT-10:00 Donald Szeto <[email protected]>: We do have work-in-progress for DataFrame API tracked at https://issues.apache.org/jira/browse/PIO-71. Chan, it would be nice if you could create a branch on your personal fork if you want to hand it off to someone else. Thanks! On Fri, Jan 5, 2018 at 2:02 PM, Pat Ferrel <[email protected]> wrote: Yes and I do not recommend that because the EventServer schema is not a developer contract. It may change at any time. Use the conversion method and go through the PIO API to get the RDD then convert to DF for now. I’m not sure what PIO uses to get an RDD from Postgres but if they do not use something like the lib you mention, a PR would be nice. Also if you have an interest in adding the DF APIs to the EventServer contributions are encouraged. Committers will give some guidance I’m sure—once that know more than me on the subject. If you want to donate some DF code, create a Jira and we’ll easily find a mentor to make suggestions. There are many benefits to this including not having to support a fork of PIO through subsequent versions. Also others are interested in this too. On Jan 5, 2018, at 7:39 AM, Daniel O' Shaughnessy <[email protected]> wrote: ....Should have mentioned that I used org.apache.spark.rdd.JdbcRDD to read in the RDD from a postgres DB initially. This was you don't need to use an EventServer! On Fri, 5 Jan 2018 at 15:37 Daniel O' Shaughnessy <[email protected]> wrote: Hi Shane, I've successfully used : import org.apache.spark.ml.classification.{ RandomForestClassificationModel, RandomForestClassifier } with pio. You can access feature importance through the RandomForestClassifier also. Very simple to convert RDDs to DFs as Pat mentioned, something like: val RDD_2_DF = sqlContext.createDataFrame(yourRDD).toDF("col1", "col2") On Thu, 4 Jan 2018 at 23:10 Pat Ferrel <[email protected]> wrote: Actually there are libs that will read DFs from HBase https://svn.apache.org/repos/asf/hbase/hbase.apache.org/trunk/_chapters/spark.html This is out of band with PIO and should not be used IMO because the schema of the EventStore is not guaranteed to remain as-is. The safest way is to translate or get DFs integrated to PIO. I think there is an existing Jira that request Spark ML support, which assumes DFs. On Jan 4, 2018, at 12:25 PM, Pat Ferrel <[email protected]> wrote: Funny you should ask this. Yes, we are working on a DF based Universal Recommender but you have to convert the RDD into a DF since PIO does not read out data in the form of a DF (yet). This is a fairly simple step of maybe one line of code but would be better supported in PIO itself. The issue is that the EventStore uses libs that may not read out DFs, but RDDs. This is certainly the case with Elasticsearch, which provides an RDD lib. I haven’t seen one from them that read out DFs though it would make a lot of sense for ES especially. So TLDR; yes, just convert the RDD into a DF for now. Also please add a feature request as a PIO Jira ticket to look into this. I for one would +1 On Jan 4, 2018, at 11:55 AM, Shane Johnson <[email protected]> wrote: Hello group, Happy new year! Does anyone have a working example or template using the DataFrame API vs. the RDD based APIs. We are wanting to migrate to using the new DataFrame APIs to take advantage of the Feature Importance function for our Regression Random Forest Models. We are wanting to move from import org.apache.spark.mllib.tree.RandomForest import org.apache.spark.mllib.tree.model.RandomForestModel import org.apache.spark.mllib.util.MLUtils to import org.apache.spark.ml.regression.{RandomForestRegressionModel, RandomForestRegressor} Is this something that should be fairly straightforward by adjusting parameters and calling new classes within DASE or is it much more involved development. Thank You! Shane Johnson | 801.360.3350 LinkedIn | Facebook
