The PrefInt package provides a way of integrating data via preference learning. For more details, please check our online manuscript: https://arxiv.org/abs/1910.11516
We have provided a new repository DPDI that integrating data via neural network with higher efficiency. Please refer to: PrefIntNN
Python version: 3.7
!The code was recently rewrote from python 2.7 to python 3.7.
For generating pairwise preference via traindata and add preference with new point:
This is for adding single new datapoint during iteration in optimization.
For adding more datapoints in one step, please extend the traindata to regenerate the pairs.
from PrefInt.preference_generate import generate_pair
GN = generate_pair(traindata)
prefs = GN.firstgen()
newprefs = GN.addnew(newdata)For training the pairwise preference:
from PrefInt.ibo.gaussianprocess import PrefGaussianProcess
from PrefInt.ibo.gaussianprocess.kernel import GaussianKernel_iso
kernel = GaussianKernel_iso(np.array([18.0]))
GP = PrefGaussianProcess(kernel)
GP.addPreferences(prefs)For more examples, please check examples and utility.
The PrefInt package is licensed under the MIT "Expat" License