| commit | 6eb07d57b538e6e9cf33daf465380379eda45aac | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Mon Sep 14 19:10:58 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Mon Sep 14 19:11:12 2020 +0200 |
| tree | 77958f81fcf2928dfe0d667ae65d4d9752dc1338 | |
| parent | ea37c48ec001a65e83d209b3fd05ed3d257f655d [diff] |
[SYSTEMDS-2641] Improved slicefinder builtin (pruning, maxL constraint) This patch improves the slice finding builtin function by a new estimation of score upper bounds by solving for slice sizes in the interval [minSup, ss] and incorporating the maximum tuple errors per slices (also monotonically decreasing) into the score computation. The tighter score upper bounds ultimately improve the pruning effectiveness. However, there are still datasets where full enumeration is impossible (e.g., dozens of correlated columns, whose subsets yield slices of very large size). For such cases, we now also provide a maxL constraint where users can specify to enumerate e.g., up to level 3 or 4 (conjunctions of 3/4 predicates). With this extension our slice finding algorithm can now also handle such problematic datasets. Finally, we had to modified the related tests (added column of expected results) because the output schema of scores changes from a 3 to 4 column matrix.
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.
Quick Start Install, Quick Start and Hello World
Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
Issue Tracker Jira Dashboard
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 clean package -P distribution.
