gingado


Namegingado JSON
Version 0.1.1 PyPI version JSON
download
home_pagehttps://github.com/dkgaraujo/gingado
SummaryA machine learning library for economics and finance
upload_time2022-09-23 06:52:00
maintainer
docs_urlNone
authorDouglas K. G. de Araujo
requires_python>=3.7
licenseApache Software License 2.0
keywords ai economics finance
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            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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