commit | e8153caf8107374548178344261ed21c9f77275a | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Tue Nov 10 13:11:33 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Tue Nov 10 13:11:47 2020 +0100 |
tree | e2104182f5c9a33242df8790e9aaaf7b062252f5 | |
parent | 96a17e1ff7ffca8381c95beb6b92333b6e66f2e9 [diff] |
[SYSTEMDS-2722] New built-in function for train/test splitting This patch introduces a new dml-bodied builtin function for common train/test splitting of feature matrices and labels. We support two types: contiguous (ranges of rows) and sampled (uniform selection of rows without replacement).
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.
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source