DiProPerm
author: Iain Carmichael_
Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.
Overview
This package implements Direction-Projection-Permutation for High Dimensional
Hypothesis Tests (DiPoPerm). For details see Wei et al, 2016 (paper link, arxiv link). DiProPerm "rigorously assesses whether a binary linear classifier is detecting statistically significant differences between two high-dimensional distributions."
Wei, S., Lee, C., Wichers, L., & Marron, J. S. (2016). Direction-projection-permutation for high-dimensional hypothesis tests. Journal of Computational and Graphical Statistics, 25(2), 549-569.
Installation
The diproperm package can be installed via pip or github. This package is currently only tested in python 3.6.
::
pip install diproperm
::
git clone https://github.com/idc9/diproperm.git
python setup.py install
Example
.. code:: python
from sklearn.datasets import make_blobs
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
from diproperm.DiProPerm import DiProPerm
# toy binary class dataset (two isotropic Gaussians)
X, y = make_blobs(n_samples=100, n_features=2, centers=2, cluster_std=2)
# DiProPerm with mean difference classifier, mean difference summary
# statistic, and 1000 permutation samples.
dpp = DiProPerm(B=1000, stat='md', clf='md')
dpp.fit(X, y)
dpp.test_stats_['md']
.. code:: python
{'Z': 11.704865481794599,
'cutoff_val': 1.2678333596648679,
'obs': 4.542253375623943,
'pval': 0.0,
'rejected': True}
.. code:: python
dpp.hist('md')
.. image:: doc/figures/dpp_hist.png
For more example code see these example notebooks_.
Help and Support
Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.
Documentation ^^^^^^^^^^^^^
The source code is located on github: https://github.com/idc9/diproperm
Testing ^^^^^^^
Testing is done using nose.
Contributing ^^^^^^^^^^^^
We welcome contributions to make this a stronger package: data examples, bug fixes, spelling errors, new features, etc.
.. _Iain Carmichael: https://idc9.github.io/ .. _paper link: https://www.tandfonline.com/doi/abs/10.1080/10618600.2015.1027773 .. _arxiv link: https://arxiv.org/pdf/1304.0796.pdf .. _these example notebooks: https://github.com/idc9/diproperm/tree/master/doc
Metadata
Release files for diproperm 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| diproperm-0.0.3.tar.gz | 11.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| diproperm-0.0.3-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 22.2 kB
Release files / diproperm-0.0.3.tar.gz
| Download URL | diproperm-0.0.3.tar.gz |
|---|---|
| Size | 11.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / diproperm-0.0.3-py2.py3-none-any.whl
| Download URL | diproperm-0.0.3-py2.py3-none-any.whl |
|---|---|
| Size | 11.1 kB |
| Tags | Python 2 Python 3 |
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twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/40.5.0 requests-toolbelt/0.8.0 tqdm/4.19.4 CPython/3.6.1
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