Toolbox for sufficient dimension reduction (SDR).
Project description
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:
Documentation / Website: https://joshloyal.github.io/sliced/
Example
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)
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:
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for sliced-0.7.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9d2489f1d2f4ebbb85bcd904a5587063d5c681fbdf7bc54ad02f05fc6e959c71 |
|
MD5 | da350038906df9d929784bd41b8c7399 |
|
BLAKE2b-256 | 5df33464f139556b4927f3ffd263aafeece33f74044cca363b2dd77af301223c |