Inspired by sklearn.datasets.make_classification, which in turn is based on work for the NIPS 2003 feature selection challenge [1] - targeting linear classifiers. Here the focus is on generating more complex, nonlinear datasets appropriate for use with deep learning/black box models which 'need' nonlinearity - otherwise you would/should use a simpler model.
Ideally, the method would provide a concise specification to generate tabular data with sensible defaults. The specification should provide knobs that the end user can dial up or down to see it's downstream impact.
Copulas are a model for specifying the joint probability p(x1, x2, ..., xn) given a correlation structure along with specifications for the marginal distribution of each feature. The current implementation uses a multivariate normal distribution with specified covariance matrix. Future work can expand this choice to other multivariate distributions.
| name | type | default | description |
|---|---|---|---|
| n_samples | int | (default=100) | The number of samples. |
| n_informative | int | (default=2) | The number of informative features - these should all be represented in the symbolic expression used to generate y_reg |
| n_nuisance | int | (default=0) | The number of nuisance features - these should not be included in the symbolic expression - and hence have no role in the DGP. |
| n_clases | int | (default=2) | the number of classes |
| dist | list | a list of the marginal distributions to apply to the features/columns | |
| cov | matrix | a square numpy array with dimensions (??? x ???) - should be n_total where n_total=n_informative + n_nuisance | |
| expr | str | an expression providing y = f(X) | |
| sig_k | float | (default=1.0) | the steepness of the sigmoid used in mapping y_reg to y_prob |
| sig_x0 | float | (default=None) | the center point of the sigmoid used in mappying y_reg to y_prob |
| p_thresh | float | (default=0.5) | threshold probability that determines boundary between classes |
| noise_level_x | float | (default=0.0) | level of Gaussian white noise to apply to X |
| noise_level_y | float | (default=0.0) | level of Gaussian white noise to apply to y_label (like flip_y) |
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e .
Test/Lint Dependencies
pip install -r requirements-test.txtTo run tests:
make test_localTo install pre-commit hooks, run the following commands:
pre-commit install
pre-commit run
If you use this library in your work, please cite our paper:
@inproceedings{barr:2020,
author = {Brian Barr and Ke Xu and Claudio Silva and Enrico Bertini and Robert Reilly and C. Bayan Bruss and Jason D. Wittenbach},
title = {{Towards Ground Truth Explainability on Tabular Data}},
year = {2020},
maintitle = {International Conference on Machine Learning},
booktitle = {2020 ICML Workshop on Human Interpretability in Machine Learning (WHI 2020)},
date = {2020-07-17},
pages = {362-367},
}
If you have tabular data, and want to fit a copula from it, consider this python library: copulas Quick visual tutorial of copulas and probability integral transform.
To run the examples, you should run:
$ python -m pip install pandas pytest pytest-cov seaborn shap tensorflow "DataProfiler[full]"[1] Guyon, “Design of experiments for the NIPS 2003 variable selection benchmark”, 2003.