def test_missingness(algname):
# Random data tensor.
shape = (15, 16, 17)
rank = 3
if algname == "mcp_als":
X = tt.randn_ktensor(shape, rank=rank, random_state=data_seed).full()
elif algname == "ncp_hals":
X = tt.rand_ktensor(shape, rank=rank, random_state=data_seed).full()
# Random missingness mask.
mask = np.random.binomial(1, .5, size=X.shape).astype(bool)
# Create second tensor with corrupted entries.
Y = X.copy()
Y[~mask] = 999.
# Algorithm fitting options.
options = dict(rank=rank,
mask=mask,
verbose=False,
tol=1e-6,
random_state=alg_seed)
# Fit decompositions for both X and Y.
resultX = getattr(tt, algname)(X, **options)
resultY = getattr(tt, algname)(Y, **options)
# Test that learning curves are identical.
assert np.allclose(resultX.obj_hist, resultY.obj_hist)
# Test that final factors are identical.
for uX, uY in zip(resultX.factors, resultY.factors):
assert np.allclose(uX, uY)
def test_objective_decreases(algname, shape, rank):
# Generate data. If algorithm is made for nonnegative tensor decomposition
# then generate nonnegative data.
if algname in ['ncp_hals, ncp_bcd']:
X = tt.rand_ktensor(shape, rank=rank, random_state=data_seed).full()
else:
X = tt.randn_ktensor(shape, rank=rank, random_state=data_seed).full()
# Fit model.
f = getattr(tt, algname)
result = f(X, rank=rank, verbose=False, tol=1e-6, random_state=alg_seed)
# Test that objective function monotonically decreases.
assert np.all(np.diff(result.obj_hist) < obj_decreased_tol)
def test_objective_decreases(algname, shape, rank):
# Generate data. If algorithm is made for nonnegative tensor decomposition
# then generate nonnegative data.
if algname in ['ncp_hals, ncp_bcd']:
X = tt.rand_ktensor(shape, rank=rank, random_state=data_seed).full()
else:
X = tt.randn_ktensor(shape, rank=rank, random_state=data_seed).full()
# Algorithm fitting options.
options = dict(rank=rank, verbose=False, tol=1e-6, random_state=alg_seed)
# Add special options for particular algorithms.
if algname == 'mcp_als':
options['mask'] = np.ones_like(X).astype(bool)
# Fit model.
result = getattr(tt, algname)(X, **options)
# Test that objective function monotonically decreases.
assert np.all(np.diff(result.obj_hist) < obj_decreased_tol)
"""
Created on Thu Feb 14 14:24:14 2019
===============================================================
Examples of CPD
===============================================================
@author: Yongjie
"""
import tensortools as tt
import numpy as np
import matplotlib.pyplot as plt
# Make dataset.
I, J, K, R = 100, 100, 100, 4
X = tt.rand_ktensor((I, J, K), rank=R)
# Add noise.
Xn = np.maximum(0, X.full() + .1 * np.random.randn(I, J, K))
# Fit ensemble of unconstrained tensor decompositions.
methods = (
'cp_als',
'ncp_bcd',
'ncp_hals',
)
ensembles = {}
for m in methods:
ensembles[m] = tt.Ensemble(fit_method=m, fit_options=dict(tol=1e-4))
ensembles[m].fit(Xn, ranks=range(1, 9), replicates=3)