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Description:

mbpls 1.0.4

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
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
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)