| commit | acfe3883a50b827e78db45d0db901a3f448add20 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Mon Apr 13 22:05:52 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Mon Apr 13 22:07:38 2020 +0200 |
| tree | fddac9c65f58dd1b4f4ff61eb9a74d2f91e7af95 | |
| parent | 4bbba4051e63e67a3a2366ee3f414f01cc7d0b93 [diff] |
[SYSTEMDS-118] New generic gridSearch builtin function This patch adds a new generic grid search function for hyper-parameter optimization of arbitrary ML algorithms and parameter combinations. This function takes train and eval functions by name as well as lists of parameter names and vectors of their values, and returns the parameter combination and model that gave the best results. So far hyper-parameter optimization is working, but the core training/scoring part needs additional features on list data types (e.g., list-list append, and eval fcalls with lists of unnamed and named parameters). Also, before it can be applied in practice it needs an integration with cross validation.
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.
