Metadata-Version: 1.1
Name: sliced
Version: 0.6.0
Summary: Toolbox for sufficient dimension reduction (SDR).
Home-page: https://joshloyal.github.io/sliced
Author: Joshua D. Loyal
Author-email: jloyal25@gmail.com
License: MIT
Download-URL: https://pypi.org/project/sliced/#files
Description-Content-Type: UNKNOWN
Description: .. -*- mode: rst -*-
        
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        .. _License: https://opensource.org/licenses/MIT
        
        .. _scikit-learn: https://github.com/scikit-learn/scikit-learn
        
        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_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:
        -----------
        .. [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.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
