This repository contains tensor completion algorithms alternating minimization, coordinate minimization, stochastic gradient descent, and quasi-Newton algorithm for least squares loss and Poisson loss implemented using the Python interface to Cyclops.
Usage of these codes requires installation of the Cyclops library and its python extension. The code currently uses this version.
To benchmark and test the codes run combined_test.py, which maybe used as follows.
python ./combined_test.py --help
usage: combined_test.py [-h] [--I int] [--J int] [--K int] [--R int]
[--num-iter-ALS-implicit int]
[--num-iter-ALS-explicit int] [--num-iter-CCD int]
[--num-iter-SGD int] [--time-limit float]
[--obj-freq-CCD int] [--obj-freq-SGD int]
[--err-thresh float] [--sp-fraction float]
[--use-sparse-rep int] [--block-size-ALS-implicit int]
[--block-size-ALS-explicit int]
[--regularization-ALS float]
[--regularization-CCD float]
[--regularization-SGD float] [--learning-rate float]
[--sample-frac-SGD float] [--function-tensor int]
[--use-CCD-TTTP int] [--tensor-file str]
optional arguments:
-h, --help show this help message and exit
--I int Input tensor size in first dimension (default: 64)
--J int Input tensor size in second dimension (default: 64)
--K int Input tensor size in third dimension (default: 64)
--R int Input CP decomposition rank (default: 10)
--num-iter-ALS-implicit int
Number of iterations (sweeps) to run ALS with implicit
CG (default: 10)
--num-iter-ALS-explicit int
Number of iterations (sweeps) to run ALS with explicit
CG (default: 10)
--num-iter-CCD int Number of iterations (updates to each column of each
matrix) for which to run CCD (default: 10)
--num-iter-SGD int Number of iteration, each iteration computes
subgradients from --sample-frac-SGD of the total
number of nonzeros in tensor (default: 10)
--time-limit float Number of seconds after which to terminate tests for
either ALS, SGD, or CCD if number of iterations is not
exceeded (default: 30)
--obj-freq-CCD int Number of iterations after which to calculate
objective (time for objective calculation not included
in time limit) for CCD (default: 1)
--obj-freq-SGD int Number of iterations after which to calculate
objective (time for objective calculation not included
in time limit) for SGD (default: 1)
--err-thresh float Residual norm threshold at which to halt if number of
iterations does not expire (default 1.E-5)
--sp-fraction float sparsity (default: .1)
--use-sparse-rep int whether to store tensor as sparse (default: 1, i.e.
True)
--block-size-ALS-implicit int
block-size for implicit ALS (default: 0, meaning to
use a single block)
--block-size-ALS-explicit int
block-size for explicit ALS (default: 0, meaning to
use a single block)
--regularization-ALS float
regularization for ALS (default: 0.00001)
--regularization-CCD float
regularization for CCD (default: 0.00001)
--regularization-SGD float
regularization for SGD (default: 0.00001)
--learning-rate float
learning rate for SGD (default: 0.01)
--sample-frac-SGD float
sample size as fraction of total number of nonzeros
for SGD (default: 0.01)
--function-tensor int
whether to use function tensor as test problem
(default: 0, i.e. False, use explicit low CP-rank
sampled tensor)
--use-CCD-TTTP int whether to use TTTP for CCD contractions (default: 1,
i.e. Yes)
--tensor-file str Filename from which to read tensor (default: None, use
model problem)
To execute with MPI (for example with 4 processes), use mpirun -np 4 python ./combined_test.py ....
Some scripts to obtain and preprocess various test datasets are provided in the data/ directory.