thanks for sharing the summary - this is very nice. While looking over the example, I had the following questions:
1) Output handling: It would be great to see an example how the results of Algorithm.execute() are consumed. Do you intend to hand out our binary matrix representation or MLContext's Matrix from which the user then requests specific output formats? Also if there are multiple Algorithm instances, how is the MLContext (with its internal state of lazily evaluated intermediates) reused? 2) Scala-breeze prototyping: How do you intend to support operations that are not supported in breeze? Examples are removeEmpty, table, aggregate, rowIndexMax, quantile/centralmoment, cummin/cummax, and DNN operations? 3) Frame data type and operations: Do you also intend to add a frame type and its operations? I think for this initial prototype it is not necessarily required but please make the scope explicit. Regards, Matthias From: [email protected] To: [email protected] Date: 09/23/2016 04:36 PM Subject: Proof of Concept: Embedded Scala DSL As discussed in the related Jira (SYSTEMML-451) I have started to implement a prototype/proof of concept for an embedded DSL in Scala. I have summarized the current approach in a short document that you can find on github together with the code: https://github.com/fschueler/emma/blob/sysml-dsl/emma-sysml-dsl/README.md Please note that current development happens in the Emma project but will move to an independent module in the SystemML project once the necessary additions to Emma are merged. By having the DSL in a separate module, we can include Scala and Emma dependencies only for the users that actually want to use the Scala DSL. The current code serves as a proof of concept to discuss further development with the SystemML community. I especially welcome input from SystemML Scala users on the usability of the API design. Next steps will include the translation from Scala code to DML with support of all features currently supported in DML, including control flow structures. Also, a coherent way of executing the generated scripts from Scala and the interaction with outside data formats (such as Spark Dataframes) will be integrated. I am happy to answer your questions and discuss the described approach here! Felix
