cookiecutter data science

CookieCutter数据科学

用于进行和共享数据科学工作的逻辑，合理标准化但灵活的项目结构。

CookieCutter数据科学（CCD）是设置包含最佳实践的数据科学项目模板的工具。要了解有关CCD的理念的更多信息，请访问项目主页。

CookieCutter Data Science V2已从V1变化。现在，它需要安装新的CookieCutter-Data-Science Python软件包，该软件包扩展了CookieCutter模板实用程序的功能。使用提供的ccds命令行程序而不是cookiecutter 。

安装

CookieCutter数据科学V2需要Python 3.8+。由于这是一个交叉项目的实用程序应用程序，因此我们建议将其与PIPX安装。安装命令选项：

 # With pipx from PyPI (recommended)
pipx install cookiecutter-data-science

# With pip from PyPI
pip install cookiecutter-data-science

# With conda from conda-forge (coming soon)
# conda install cookiecutter-data-science -c conda-forge 

开始一个新项目

要启动一个新项目，请运行：

ccds

结果目录结构

新项目的目录结构将看起来像这样（取决于您选择的设置）：

 ├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         {{ cookiecutter.module_name }} and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── {{ cookiecutter.module_name }}   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes {{ cookiecutter.module_name }} a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations   

使用V1

如果要使用旧的V1项目模板，则需要安装CookieCutter-Data-Science软件包或CookieCutter软件包。然后，将任一个命令行程序与-c v1选项：

ccds https://github.com/drivendataorg/cookiecutter-data-science -c v1
# or equivalently
cookiecutter https://github.com/drivendataorg/cookiecutter-data-science -c v1

贡献

我们欢迎捐款！有关指南，请参见文档。

安装开发要求

pip install -r dev-requirements.txt

运行测试

pytest tests