test_tucker_hooi.py

import pytest
import logging
from numpy import allclose
from numpy.random import randn
from scipy.sparse import rand as sprand
from sktensor import tucker
from sktensor.core import ttm
from sktensor.dtensor import dtensor, unfolded_dtensor
from sktensor.sptensor import unfolded_sptensor
#from sktensor.rotation import orthomax

logging.basicConfig(level=logging.INFO)


def normalize(X):
   return X / X.sum(axis=0)


def disabled_test_factorization():
    I, J, K, rank = 10, 20, 75, 5
    A = orthomax(randn(I, rank))
    B = orthomax(randn(J, rank))
    C = orthomax(randn(K, rank))

    core_real = dtensor(randn(rank, rank, rank))
    T = core_real.ttm([A, B, C])
    core, U = tucker.hooi(T, rank)

    assert allclose(T, ttm(core, U))
    assert allclose(A, orthomax(U[0]))
    assert allclose(B, orthomax(U[1]))
    assert allclose(C, orthomax(U[2]))
    assert allclose(core_real, core)


def disabled_test_factorization_sparse():
    I, J, K, rank = 10, 20, 75, 5
    Tmat = sprand(I, J * K, 0.1).tocoo()
    T = unfolded_sptensor((Tmat.data, (Tmat.row, Tmat.col)), None, 0, [], (I, J, K)).fold()
    core, U = tucker.hooi(T, rank, maxIter=20)

    Tmat = Tmat.toarray()
    T = unfolded_dtensor(Tmat, 0, (I, J, K)).fold()
    core2, U2 = tucker.hooi(T, rank, maxIter=20)

    assert allclose(core2, core)
    for i in range(len(U)):
        assert allclose(U2[i], U[i])