OnPLS: Orthogonal Projections to Latent Structures in Multiblock and Path Model Data Analysis
OnPLS is a Python package for multiblock data analysis with prefiltering of unique and locally joint variation.
The reference environment for OnPLS is Ubuntu 14.04 LTS with Python 2.7.6 or Python 3.4.3 and Numpy 1.8.2.
Unless you already have Numpy installed, you need to install it:
$ sudo apt-get install python-numpy
or
$ sudo apt-get install python3-numpy
In order to run the tests, you may also need to install Nose:
$ sudo apt-get install python-nose
or
$ sudo apt-get install python3-nose
Downloading the latest development version
Clone the Github repository
$ git clone https://github.com/tomlof/OnPLS.git
Preferably, you would fork it first and clone your own repository.
Add OnPLS to your Python path:
$ export $PYTHONPATH=$PYTHONPATH:/directory/to/OnPLS
Stable reseases with setup scripts will be included in future versions.
You are now ready to use your fresh installation of OnPLS!
A simple example of the usage:
import numpy as np
import OnPLS
np.random.seed(42)
n, p_1, p_2, p_3 = 4, 3, 4, 5
t = np.sort(np.random.randn(n, 1), axis=0)
p1 = np.sort(np.random.randn(p_1, 1), axis=0)
p2 = np.sort(np.random.randn(p_2, 1), axis=0)
p3 = np.sort(np.random.randn(p_3, 1), axis=0)
X1 = np.dot(t, p1.T) + 0.1 * np.random.randn(n, p_1)
X2 = np.dot(t, p2.T) + 0.1 * np.random.randn(n, p_2)
X3 = np.dot(t, p3.T) + 0.1 * np.random.randn(n, p_3)
# Define the connections between blocks
predComp = [[0, 1, 1], [1, 0, 1], [1, 1, 0]]
# Define the numbers of non-global components
orthComp = [1, 1, 1]
# Create the estimator
onpls = OnPLS.estimators.OnPLS(predComp, orthComp)
# Fit a model
onpls.fit([X1, X2, X3])
# Perform prediction of all matrices from all connected matrices
Xhat = onpls.predict([X1, X2, X3])
# Compute prediction score
score = onpls.score([X1, X2, X3])
cv_scores = OnPLS.resampling.cross_validation(onpls, [X1, X2, X3], cv_rounds=4)