commit | c29ac3eeb6bc9590bdc89b77bf2616ee9ca133c8 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Fri Jan 29 19:34:56 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Fri Jan 29 19:35:10 2021 +0100 |
tree | 47530ecaced6c25fc5f9d5a2b672ed0acc5d3b9d | |
parent | aa09b5c3d3b5d221426fd06871b6690e1297ee9e [diff] |
[SYSTEMDS-2509] Fix missing binning support in spark transformencode So far, the distributed spark transformencode only supported recoding, dummy coding, mv imputation, and NaN omission but lacked proper support for binning which required similar to recoding dedicated meta data handling for bin boundaries. This patch adds the missing support while preserving the general structure of the spark transformencode instruction. Furthermore, this patch also cleans up the incorrectly updated scripts for federated lmPredict.
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