[SYSTEMDS-291] Extended eval function calls (named/unnamed list args)

This patch improves the existing eval function calls by support for
named and unnamed list inputs. During the function call these list
arguments are expanded and ordered on demand (if the the function
doesn't have a signature of a single list argument).

Furthermore, this patch integrates the tests for gridSearch, and makes a
couple of smaller improvements to list data types (e.g., list append,
proper function return handling with unknown value types).
11 files changed
tree: 7b6ba4ac232fecbf4e97ff919a93930857a9b7ec
  1. .github/
  2. bin/
  3. conf/
  4. dev/
  5. docker/
  6. docs/
  7. scripts/
  8. src/
  9. .gitattributes
  10. .gitignore
  11. CONTRIBUTING.md
  12. LICENSE
  13. NOTICE
  14. pom.xml
  15. README.md
README.md

SystemDS

Overview: SystemDS is a versatile system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.

Documentation: SystemDS Documentation

Status and Build: SystemDS is still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn -DskipTests clean package.

Status

License Build Documentation Component Test Application Test Function Test Python Test