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| common_primitives | 5 years ago | |
| pipeline_runs | 5 years ago | |
| pipelines | 5 years ago | |
| sklearn-wrap | 5 years ago | |
| tests | 5 years ago | |
| HISTORY.md | 5 years ago | |
| HOW_TO_MANAGE.md | 5 years ago | |
| LICENSE.txt | 5 years ago | |
| MANIFEST.in | 5 years ago | |
| README.md | 5 years ago | |
| add.sh | 5 years ago | |
| entry_points.ini | 5 years ago | |
| git-add.sh | 5 years ago | |
| git-check.sh | 5 years ago | |
| list_primitives.py | 5 years ago | |
| run_pipelines.sh | 5 years ago | |
| run_tests.py | 5 years ago | |
| setup.cfg | 5 years ago | |
| setup.py | 5 years ago | |
A common set of primitives for D3M project, maintained together.
It contains example primitives, various glue primitives, and other primitives performers
contributed.
This package works on Python 3.6+ and pip 19+.
This package additional dependencies which are specified in primitives' metadata,
but if you are manually installing the package, you have to first run, for Ubuntu:
$ apt-get install build-essential libopenblas-dev libcap-dev ffmpeg
$ pip3 install python-prctl
To install common primitives from inside a cloned repository, run:
$ pip3 install -e .
When cloning a repository, clone it recursively to get also git submodules:
$ git clone --recursive https://gitlab.com/datadrivendiscovery/common-primitives.git
See HISTORY.md for summary of changes to this package.
master branch contains latest code of common primitives made against the latest stable
release of the d3m core package (its master branch).
devel branch contains latest code of common primitives made against the
future release of the d3m core package (its devel branch).
Releases are tagged but they are not done
regularly. Each primitive has its own versions as well, which are not related to package versions.
Generally is the best to just use the latest code available in master or devel
branches (depending which version of the core package you are using).
For each commit to this repository, tests run automatically in the
GitLab CI.
If you don't want to wait for the GitLab CI test results and run the tests locally,
you can install and use the GitLab runner in your system.
With the local GitLab runner, you can run the tests defined in the .gitlab-ci.yml
file of this repository, such as:
$ gitlab-runner exec docker style_check
$ gitlab-runner exec docker type_check
You can also just try to run tests available under /tests by running:
$ python3 run_tests.py
Feel free to contribute more primitives to this repository. The idea is that we build
a common set of primitives which can help both as an example, but also to have shared
maintenance of some primitives, especially glue primitives.
All primitives are written in Python 3 and are type checked using
mypy, so typing annotations are required.
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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