sliced 0.3.0

.. -*- mode: rst -*-
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sliced
======
sliced is a python package offering a number of sufficient dimension reduction (SDR) techniques commonly used in high-dimensional datasets with a supervised target. It is compatible with scikit-learn_.


Algorithms supported:

- Sliced Inverse Regression (SIR) [1]_
- Sliced Average Variance Estimation (SAVE) [2]_

Documentation / Website: https://joshloyal.github.io/sliced/


Example
-------
Example that shows how to learn a one dimensional subspace from a dataset with ten features:

.. code-block:: python

from sliced.datasets import make_cubic
from sliced import SlicedInverseRegression

# load the 10-dimensional dataset
X, y = make_cubic(random_state=123)

# Set the options for SIR
sir = SlicedInverseRegression(n_components=1)

# fit the model
sir.fit(X, y)

# transform into the new subspace
X_sir = sir.transform(X)


Installation
------------

Dependencies
------------
sliced requires:

- Python (>= 2.7 or >= 3.4)
- NumPy (>= 1.8.2)
- SciPy (>= 0.13.3)
- Scikit-learn (>=0.17)

Additionally, to run examples, you need matplotlib(>=2.0.0).

Installation
------------
You need a working installation of numpy and scipy to install sliced. If you have a working installation of numpy and scipy, the easiest way to install sliced is using ``pip``::

pip install -U sliced

If you prefer, you can clone the repository and run the setup.py file. Use the following commands to get the copy from GitHub and install all the dependencies::

git clone https://github.com/joshloyal/sliced.git
cd sliced
pip install .

Or install using pip and GitHub::

pip install -U git+https://github.com/joshloyal/sliced.git

Testing
-------
After installation, you can use pytest to run the test suite via setup.py::

python setup.py test

References:
-----------
.. [1] : Li, K C. (1991)
"Sliced Inverse Regression for Dimension Reduction (with discussion)",
Journal of the American Statistical Association, 86, 316-342.
.. [2] : Shao, Y, Cook, RD and Weisberg, S (2007).
"Marginal Tests with Sliced Average Variance Estimation",
Biometrika, 94, 285-296.