Welcome to gingado!
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
`gingado` seeks to facilitate the use of machine learning in economic
and finance use cases, while promoting good practices. This package aims
to be suitable for beginners and advanced users alike. Use cases may
range from simple data retrievals to experimentation with machine
learning algorithms to more complex model pipelines used in production.
## Overview
`gingado` is a free, open source library built different
functionalities:
- [**data augmentation**](augmentation.html), to add more data from
official sources, improving the machine models being trained by the
user;
- **automatic** [**benchmark model**](benchmark.html), to assess
candidate models against a reasonably well-performant model;
- *(new!)* **relevant** [**datasets**](datasets.html), both real and
simulamed, to allow for easier model development and comparison;
- **support for** [**model documentation**](documentation.html), to
embed documentation and ethical considerations in the model
development phase; and
- [**utilities**](utils.html), including tools to allow for lagging
variables in a straightforward way.
Each of these functionalities builds on top of the previous one. They
can be used on a stand-alone basis, together, or even as part of a
larger pipeline from data input to model training to documentation!
<div>
> **Tip**
>
> New functionalities are planned over time, so consider checking
> frequently on `gingado` for the latest toolsets.
</div>
## Design principles
The choices made during development of `gingado` derive from the
following principles, in no particular order:
- **flexibility**: users can use `gingado` out of the box or build
custom processes on top of it;
- **compatibility**: `gingado` works well with other widely used
libraries in machine learning, such as `scikit-learn` and `pandas`;
and
- **responsibility**: `gingado` facilitates and promotes model
documentation, including ethical considerations, as part of the
machine learning development workflow.
## Acknowledgements
`gingado`’s API is inspired on the following libraries:
- `scikit-learn` (Buitinck et al. 2013)
- `keras` (website [here](https://keras.io/about/) and also, [this
essay](https://medium.com/s/story/notes-to-myself-on-software-engineering-c890f16f4e4d))
- `fastai` (Howard and Gugger 2020)
In addition, `gingado` is developed and maintained using
[`nbdev`](https://nbdev.fast.ai).
## Presentations, talks, papers
The most current version of the paper describing `gingado` is
[here](https://github.com/dkgaraujo/gingado_comms/blob/main/gingado.pdf).
The paper and other material about `gingado` (ie, slide decks, papers)
in [this dedicated
repository](https://github.com/dkgaraujo/gingado_comms). Interested
users are welcome to visit the repository and comment on the drafts or
slide decks, preferably by opening an
[issue](https://github.com/dkgaraujo/gingado_comms/issues). I also store
in this repository suggestions I receive as issues, so users can see
what others commented (anonymously unless requested) and comment along
as well!
## Install
To install `gingado`, simply run the following code on the terminal:
`$ pip install gingado`
## Citation
If you use this package in your work, please cite it as below:
Araujo, Douglas (2022): “*gingado*: A machine learning library for
economics and finance”, Irving Fisher Committee on Central Bank
Statistics Workshop on “Data science in central banking” - Part 2: Data
Science in Central Banking: Applications and tools.
## References
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-sklearnAPI" class="csl-entry">
Buitinck, Lars, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa,
Andreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. “API Design
for Machine Learning Software: Experiences from the Scikit-Learn
Project.” *CoRR* abs/1309.0238. <http://arxiv.org/abs/1309.0238>.
</div>
<div id="ref-fastaiAPI" class="csl-entry">
Howard, Jeremy, and Sylvain Gugger. 2020. “Fastai: A Layered API for
Deep Learning.” *Information* 11 (2).
<https://doi.org/10.3390/info11020108>.
</div>
</div>
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"description": "Welcome to gingado!\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n`gingado` seeks to facilitate the use of machine learning in economic\nand finance use cases, while promoting good practices. This package aims\nto be suitable for beginners and advanced users alike. Use cases may\nrange from simple data retrievals to experimentation with machine\nlearning algorithms to more complex model pipelines used in production.\n\n## Overview\n\n`gingado` is a free, open source library built different\nfunctionalities:\n\n- [**data augmentation**](augmentation.html), to add more data from\n official sources, improving the machine models being trained by the\n user;\n\n- **automatic** [**benchmark model**](benchmark.html), to assess\n candidate models against a reasonably well-performant model;\n\n- *(new!)* **relevant** [**datasets**](datasets.html), both real and\n simulamed, to allow for easier model development and comparison;\n\n- **support for** [**model documentation**](documentation.html), to\n embed documentation and ethical considerations in the model\n development phase; and\n\n- [**utilities**](utils.html), including tools to allow for lagging\n variables in a straightforward way.\n\nEach of these functionalities builds on top of the previous one. They\ncan be used on a stand-alone basis, together, or even as part of a\nlarger pipeline from data input to model training to documentation!\n\n<div>\n\n> **Tip**\n>\n> New functionalities are planned over time, so consider checking\n> frequently on `gingado` for the latest toolsets.\n\n</div>\n\n## Design principles\n\nThe choices made during development of `gingado` derive from the\nfollowing principles, in no particular order:\n\n- **flexibility**: users can use `gingado` out of the box or build\n custom processes on top of it;\n\n- **compatibility**: `gingado` works well with other widely used\n libraries in machine learning, such as `scikit-learn` and `pandas`;\n and\n\n- **responsibility**: `gingado` facilitates and promotes model\n documentation, including ethical considerations, as part of the\n machine learning development workflow.\n\n## Acknowledgements\n\n`gingado`\u2019s API is inspired on the following libraries:\n\n- `scikit-learn` (Buitinck et al. 2013)\n\n- `keras` (website [here](https://keras.io/about/) and also, [this\n essay](https://medium.com/s/story/notes-to-myself-on-software-engineering-c890f16f4e4d))\n\n- `fastai` (Howard and Gugger 2020)\n\nIn addition, `gingado` is developed and maintained using\n[`nbdev`](https://nbdev.fast.ai).\n\n## Presentations, talks, papers\n\nThe most current version of the paper describing `gingado` is\n[here](https://github.com/dkgaraujo/gingado_comms/blob/main/gingado.pdf).\nThe paper and other material about `gingado` (ie, slide decks, papers)\nin [this dedicated\nrepository](https://github.com/dkgaraujo/gingado_comms). Interested\nusers are welcome to visit the repository and comment on the drafts or\nslide decks, preferably by opening an\n[issue](https://github.com/dkgaraujo/gingado_comms/issues). I also store\nin this repository suggestions I receive as issues, so users can see\nwhat others commented (anonymously unless requested) and comment along\nas well!\n\n## Install\n\nTo install `gingado`, simply run the following code on the terminal:\n\n`$ pip install gingado`\n\n## Citation\n\nIf you use this package in your work, please cite it as below:\n\nAraujo, Douglas (2022): \u201c*gingado*: A machine learning library for\neconomics and finance\u201d, Irving Fisher Committee on Central Bank\nStatistics Workshop on \u201cData science in central banking\u201d - Part 2: Data\nScience in Central Banking: Applications and tools.\n\n## References\n\n<div id=\"refs\" class=\"references csl-bib-body hanging-indent\">\n\n<div id=\"ref-sklearnAPI\" class=\"csl-entry\">\n\nBuitinck, Lars, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa,\nAndreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. \u201cAPI Design\nfor Machine Learning Software: Experiences from the Scikit-Learn\nProject.\u201d *CoRR* abs/1309.0238. <http://arxiv.org/abs/1309.0238>.\n\n</div>\n\n<div id=\"ref-fastaiAPI\" class=\"csl-entry\">\n\nHoward, Jeremy, and Sylvain Gugger. 2020. \u201cFastai: A Layered API for\nDeep Learning.\u201d *Information* 11 (2).\n<https://doi.org/10.3390/info11020108>.\n\n</div>\n\n</div>\n\n\n",
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