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| HISTORY.md | 5 years ago | |
| HOW_TO_RELEASE.md | 5 years ago | |
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| README.md | 5 years ago | |
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README.md
Common code for D3M project
This package provides a core package for D3M project with common code available.
It contains standard interfaces, reference implementations, and utility implementations.
Installation
This package works with Python 3.6 and pip 19+. You need to have the following packages installed on the system (for Debian/Ubuntu):
libssl-devlibcurl4-openssl-devlibyaml-dev
You can install latest stable version from PyPI:
$ pip3 install d3m
To install latest development version:
$ pip3 install -e git+https://gitlab.com/datadrivendiscovery/d3m.git@devel#egg=d3m
When cloning a repository, clone it recursively to get also git submodules:
$ git clone --recursive https://gitlab.com/datadrivendiscovery/d3m.git
Changelog
See HISTORY.md for summary of changes to this package.
Documentation
Documentation for the package is available at https://docs.datadrivendiscovery.org/.
Contributing
See CODE_STYLE.md for our coding style and contribution guide. Please ensure any merge requests you open follow this guide.
Repository structure
master branch contains latest stable release of the package.
devel branch is a staging branch for the next release.
Releases are tagged.
About Data Driven Discovery Program
DARPA Data Driven Discovery (D3M) Program is researching ways to get machines to build
machine learning pipelines automatically. It is split into three layers:
TA1 (primitives), TA2 (systems which combine primitives automatically into pipelines
and executes them), and TA3 (end-users interfaces).
全栈的自动化机器学习系统，主要针对多变量时间序列数据的异常检测。TODS提供了详尽的用于构建基于机器学习的异常检测系统的模块，它们包括：数据处理（data processing），时间序列处理（ time series processing），特征分析（feature analysis)，检测算法（detection algorithms），和强化模块（ reinforcement module）。这些模块所提供的功能包括常见的数据预处理、时间序列数据的平滑或变换，从时域或频域中抽取特征、多种多样的检测算
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