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

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_directions=1) # fit the model sir.fit(X, y) # transform into the new subspace X_sir = sir.transform(X)

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).

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 

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

python setup.py test