| Name | badgers JSON |
| Version |
0.0.10
JSON |
| download |
| home_page | None |
| Summary | Badgers: bad data generators |
| upload_time | 2024-12-20 21:10:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | BSD 3-Clause License Copyright (c) 2023, the badgers developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
bad data
data quality
data science
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
scikit-learn
networkx
pandas
scipy
tox
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mkdocstrings
mkdocstrings-python
mkdocs-gen-files
mkdocs-material
mkdocs-literate-nav
mkdocs-jupyter
jupyterlab
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|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Badgers: bad data generators
[badgers](https://github.com/Fraunhofer-IESE/badgers) is a python library for generating bad data (more precisely to augment existing data with data quality deficits such as outliers, missing values, noise, etc.). It is based upon a simple API and provides a set of generators object that can generate data quality deficits from existing data.
A word of caution: badgers is still in an early development stage. Although the core structure of the package and the `generate(X,y)` signature are not expected to change, some API details (like attributes names) are likely to change.
The full documentation is hosted here: [https://fraunhofer-iese.github.io/badgers/](https://fraunhofer-iese.github.io/badgers/).
For a quick-start, you can install `badgers` with pip:
```bash
pip install badgers
```
Import badgers as any other library and start using it:
```python
from sklearn.datasets import make_blobs
from badgers.generators.tabular_data.noise import GaussianNoiseGenerator
X, y = make_blobs()
trf = GaussianNoiseGenerator(noise_std=0.5)
Xt, yt = trf.generate(X,y)
```
More examples are available in the [tutorials](https://fraunhofer-iese.github.io/badgers/tutorials/Imbalance-Tabular-Data/) section.
The API documentation is also available in the [API](https://fraunhofer-iese.github.io/badgers/reference/badgers/) section.
Interested developers will find relevant information in the [CONTRIBUTING.md](CONTRIBUTING.md) page.
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"description": "# Badgers: bad data generators\n\n[badgers](https://github.com/Fraunhofer-IESE/badgers) is a python library for generating bad data (more precisely to augment existing data with data quality deficits such as outliers, missing values, noise, etc.). It is based upon a simple API and provides a set of generators object that can generate data quality deficits from existing data.\n\nA word of caution: badgers is still in an early development stage. Although the core structure of the package and the `generate(X,y)` signature are not expected to change, some API details (like attributes names) are likely to change.\n\n\nThe full documentation is hosted here: [https://fraunhofer-iese.github.io/badgers/](https://fraunhofer-iese.github.io/badgers/).\n\nFor a quick-start, you can install `badgers` with pip:\n\n```bash\npip install badgers\n```\n\nImport badgers as any other library and start using it:\n\n```python\nfrom sklearn.datasets import make_blobs\nfrom badgers.generators.tabular_data.noise import GaussianNoiseGenerator\n\nX, y = make_blobs()\ntrf = GaussianNoiseGenerator(noise_std=0.5)\nXt, yt = trf.generate(X,y)\n```\n\nMore examples are available in the [tutorials](https://fraunhofer-iese.github.io/badgers/tutorials/Imbalance-Tabular-Data/) section.\n\nThe API documentation is also available in the [API](https://fraunhofer-iese.github.io/badgers/reference/badgers/) section.\n\nInterested developers will find relevant information in the [CONTRIBUTING.md](CONTRIBUTING.md) page. ",
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