Metadata-Version: 2.1
Name: mbpls
Version: 1.0.2
Summary: An implementation of the most common partial least squares algorithms as multi-block methods
Home-page: https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
Author: Andreas Baum, Laurent Vermue
Author-email: <andba@dtu.dk>, <lauve@dtu.dk>
License: new BSD
Description: Multiblock Partial Least Squares Package
        ========================================
        
        .. image:: https://img.shields.io/pypi/v/mbpls.svg
            :target: https://pypi.python.org/pypi/mbpls
            :alt: Pypi Version
        .. image:: https://travis-ci.com/DTUComputeStatisticsAndDataAnalysis/MBPLS.svg?branch=master
           :target: https://travis-ci.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
           :alt: Build Status
        .. image:: https://img.shields.io/pypi/l/mbpls.svg
            :target: https://pypi.python.org/pypi/mbpls/
            :alt: License
        .. image:: https://readthedocs.org/projects/mbpls/badge/?version=latest
            :target: https://mbpls.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        .. image:: http://joss.theoj.org/papers/10.21105/joss.01190/status.svg
           :target: https://doi.org/10.21105/joss.01190
           :alt: JOSS Paper DOI
        
        An easy to use Python package for (Multiblock) Partial Least Squares
        prediction modelling of univariate or multivariate outcomes. Four state
        of the art algorithms have been implemented and optimized for robust
        performance on large data matrices. The package has been designed to be
        able to handle missing data, such that application is straight forward
        using the commonly known Scikit-learn API and its model selection
        toolbox.
        
        The documentation is available at https://mbpls.readthedocs.io
        and elaborate (real-world) Jupyter Notebook examples can be found at
        https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS/tree/master/examples
        
        This package can be cited using the following reference. 
        
        *Baum et al., (2019). Multiblock PLS: Block dependent prediction modeling for Python. Journal of Open Source Software, 4(34), 1190*
        
        
        
        Installation
        ------------
        
        -  | Install the package for Python3 using the following command. Some
             dependencies might require an upgrade (scikit-learn, numpy and
             scipy).
           | ``$ pip install mbpls``
        
        -  | Now you can import the MBPLS class by typing
           | ``from mbpls.mbpls import MBPLS``
        
        Quick Start
        -----------
        
        Use the mbpls package for Partial Least Squares (PLS) prediction modeling
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from mbpls.mbpls import MBPLS
        
           num_samples = 40
           num_features = 200
        
           # Generate random data matrix X
           x = np.random.rand(num_samples, num_features)
        
           # Generate random reference vector y
           y = np.random.rand(num_samples,1)
        
           # Establish prediction model using 2 latent variables (components)
           pls = MBPLS(n_components=2)
           pls.fit(x,y)
           y_pred = pls.predict(x)
        
        The mbpls package for Multiblock Partial Least Squares (MB-PLS) prediction modeling
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from mbpls.mbpls import MBPLS
        
           num_samples = 40
           num_features_x1 = 200
           num_features_x2 = 250
        
           # Generate two random data matrices X1 and X2 (two blocks)
           x1 = np.random.rand(num_samples, num_features_x1)
           x2 = np.random.rand(num_samples, num_features_x2)
        
           # Generate random reference vector y
           y = np.random.rand(num_samples, 1)
        
           # Establish prediction model using 3 latent variables (components)
           mbpls = MBPLS(n_components=3)
           mbpls.fit([x1, x2],y)
           y_pred = mbpls.predict([x1, x2])
        
           # Use built-in plot method for exploratory analysis of multiblock pls models
           mbpls.plot(num_components=3)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
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 :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=3.5
Description-Content-Type: text/x-rst
Provides-Extra: extras
Provides-Extra: tests
Provides-Extra: docs
