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:
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
| [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. |