Multiblock PLS

Multiblock PLS: Block dependent prediction modeling for Python - Published in JOSS (2019)

https://github.com/dtucomputestatisticsanddataanalysis/mbpls

Science Score: 98.0%

This score indicates how likely this project is to be science-related based on various indicators:

○
CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 4 DOI reference(s) in README and JOSS metadata
✓
Academic publication links
Links to: joss.theoj.org
✓
Committers with academic emails
1 of 3 committers (33.3%) from academic institutions
✓
Institutional organization owner
Organization dtucomputestatisticsanddataanalysis has institutional domain (www.compute.dtu.dk)
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords

bioinformatics chemometrics data-fusion data-integration data-science machine-learning metabolomics multivariate-analysis multivariate-statistics pattern-recognition subspace-learning supervised-learning

Scientific Fields

Engineering Computer Science - 60% confidence

Last synced: 6 months ago · JSON representation

Repository

(Multiblock) Partial Least Squares Regression for Python

Basic Info

Host: GitHub
Owner: DTUComputeStatisticsAndDataAnalysis
License: bsd-3-clause
Language: Python
Default Branch: master
Homepage: https://mbpls.readthedocs.io
Size: 16.6 MB

Statistics

Stars: 31
Watchers: 1
Forks: 7
Open Issues: 2
Releases: 4

Topics

Created about 8 years ago · Last pushed about 6 years ago

Metadata Files

Readme Contributing License Code of conduct

README.rst

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)

Owner

Name: DTU Compute Statistics and Data Analysis
Login: DTUComputeStatisticsAndDataAnalysis
Kind: organization

Website: https://www.compute.dtu.dk/english/research/research-sections/stat
Repositories: 5
Profile: https://github.com/DTUComputeStatisticsAndDataAnalysis

JOSS Publication

Multiblock PLS: Block dependent prediction modeling for Python

Published

February 10, 2019

DOI

10.21105/joss.01190

Volume 4, Issue 34, Page 1190

Authors

Andreas Baum

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads 324, DK-2800 Kgs. Lyngby, Denmark

Laurent Vermue

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads 324, DK-2800 Kgs. Lyngby, Denmark

Editor

Roman Valls Guimera

GitHub Events

Total

Watch event: 1

Last Year

Watch event: 1

Committers

Last synced: 7 months ago

All Time

Total Commits: 146
Total Committers: 3
Avg Commits per committer: 48.667
Development Distribution Score (DDS): 0.39

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Laurent Vermue	l****e	89
Andreas Baum	a**a@d**k	49
Laurent Vermue		8

Committer Domains (Top 20 + Academic)

dtu.dk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 9
Total pull requests: 3
Average time to close issues: 10 days
Average time to close pull requests: 2 days
Total issue authors: 4
Total pull request authors: 2
Average comments per issue: 0.67
Average comments per pull request: 0.0
Merged pull requests: 3
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

arokem (6)
sten2lu (1)
cwieder (1)
lcwheeler (1)

Pull Request Authors

lvermue (2)
b0nsaii (1)

Top Labels

Issue Labels

accepted (1)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 290 last-month

Total dependent packages: 2
Total dependent repositories: 2
Total versions: 15
Total maintainers: 2

pypi.org: mbpls

An implementation of the most common partial least squares algorithms as multi-block methods

Homepage: https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
Documentation: https://mbpls.readthedocs.io/
License: new BSD
Latest release: 1.0.4
published about 6 years ago

Versions: 15
Dependent Packages: 2
Dependent Repositories: 2
Downloads: 290 Last month

Rankings

Dependent packages count: 3.2%

Average: 11.4%

Dependent repos count: 11.5%

Stargazers count: 12.5%

Forks count: 12.6%

Downloads: 17.3%

Maintainers (2)

andba lauve

Last synced: 6 months ago

Dependencies

requirements.txt pypi

numpy >=1.13.3
pandas >=0.20.0
scikit-learn >=0.20.0
scipy >=1.0.0

setup.py pypi

numpy >=
pandas >=
scikit-learn >=
scipy >=

Multiblock PLS

Science Score: 98.0%

Keywords

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.rst

Owner

JOSS Publication

Multiblock PLS: Block dependent prediction modeling for Python

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: mbpls

Rankings

Maintainers (2)

Dependencies