Metadata-Version: 2.1
Name: mbpls
Version: 1.0.5a1
Summary: An implementation of the most common partial least squares algorithm as multi-block methods
Home-page: https://github.com/b0nsaii/MBPLS
Author: Andreas Baum, Laurent Vermue
Author-email: <andba@dtu.dk>, <lauve@dtu.dk>
License: new BSD
Description: # Partial Least Squares Package
        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 syntax and its model selection toolbox.   
        
        ## 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
        ```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 Sqaures (MB-PLS) prediction modeling
        ```python
        import numpy as np
        from mbpls.mbpls import MBPLS
        
        num_samples = 40
        num_features_x1 = 200
        num_features_x2 = 250
        
        mbpls = MBPLS(n_components=3)
        x1 = np.random.rand(num_samples, num_features_x1)
        x2 = np.random.rand(num_samples, num_features_x2)
        
        y = np.random.rand(num_samples, 1)
        
        mbpls.fit([x1, x2],y)
        mbpls.plot(num_components=3)
        
        y_pred = mbpls.predict([x1, x2])
        ```
        
        More elaborate (real-world) examples can be found at https://github.com/b0nsaii/MBPLS/tree/Package_OOP/examples
        
        
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 :: 3 - Alpha
Requires-Python: >=3.5
Description-Content-Type: text/markdown
