Raise red flags on machine learning datasets.
Project description
Redflag
🚩 redflag aims to be an automatic safety net for machine learning datasets. The vision is to accept input of a Pandas DataFrame or NumPy ndarray (one for each of the input X and target y in a machine learning task). redflag will provide an analysis of each feature, and of the target, including aspects such as class imbalance, outliers, anomalous data patterns, threats to the IID assumption, and so on. The goal is to complement other projects like pandas-profiling and greatexpectations.
This project is very rough and does not do much yet. The API will very likely change without warning. Please consider contributing!
Installation
You can install this package with pip:
pip install redflag
For developers, there are pip options for installing tests, docs and dev dependencies, e.g. pip install redflag[dev] to install all testing and documentation packages.
Example
redflag is currently just a collection of functions. Most of the useful ones take a single column of data (e.g. a 1D NumPy array) and run a single test. For example, we can do some outlier detection. The get_outliers() function returns the indices of data points that are considered outliers:
>>> import redflag as rf
>>> data = [-3, -2, -2, -1, 0, 0, 0, 1, 2, 2, 3]
>>> rf.get_outliers(data)
array([], dtype=int64)
>>> rf.get_outliers(3 * data + [100])
array([33])
See the documentation, and specifically the notebook Basic_usage.ipynb for several other basic examples.
Documentation
Contributing
Please see CONTRIBUTING.md. There is also a section in the documentation about Development.
Testing
You can run the tests (requires pytest and pytest-cov) with
pytest
Most of the tests are doctests, but pytest will run them using the settings in setup.cfg.
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