A linear projection technique for finding a 2D view that capture interpretable pattern of the given function in a high-dimensional domain. The function can be univariate or multivariate, continuous (regression) or discrete (classification). The details of the method can be found in the corresponding paper: https://arxiv.org/pdf/1909.11804.pdf
tensorflow (<=1.15.0), numpy
fpp.py - function preserving projection class
fpp_example.ipynb - fpp usage examples
Circle_in_5D_cube.npy - synthetic dataset where a 2D circle pattern of the function exists in a 5D space.
Circle_in_30D.npy - synthetic dataset where a 2D circle pattern of the function exists in a 30D space.
# X - function domain
# f - function range
# epoches - training epoches
# batchSize - traiing batch size
# proj_mat - projection matrix
# embedding - the 2D embedding coordinate
# loss - the loss on the entire training dataset
###### regression task #####
model = fpp()
model.setup(X, f)
model.train()
proj_mat, embedding, loss, _ = model.eval()
###### classification task #####
model = fpp()
model.setupMultiClass(X, f) #f should be a one-hot encoding of the class
model.train(epoches, batchSize)
proj_mat, embedding, loss, _ = model.eval()
Reviewed and released under LLNL-CODE-791217
Author(s): Shusen Liu ([email protected]), Rushil Anirudh ([email protected])