def test_init():
I, J = 10, 9
values_K = [1, 2, 4, 5]
R = 2 * numpy.ones((I, J))
M = numpy.ones((I, J))
priors = {'alpha': 3, 'beta': 4, 'lambdaU': 5, 'lambdaV': 6}
initUV = 'exp'
iterations = 11
linesearch = LineSearch(classifier, values_K, R, M, priors, initUV,
iterations)
assert linesearch.I == I
assert linesearch.J == J
assert numpy.array_equal(linesearch.values_K, values_K)
assert numpy.array_equal(linesearch.R, R)
assert numpy.array_equal(linesearch.M, M)
assert linesearch.priors == priors
assert linesearch.iterations == iterations
assert linesearch.initUV == initUV
assert linesearch.all_performances == {
'BIC': [],
'AIC': [],
'loglikelihood': [],
'MSE': [],
'ELBO': []
}
def test_search():
# Check whether we get no exceptions...
I, J = 10, 9
values_K = [1, 2, 4, 5]
R = 2 * numpy.ones((I, J))
R[0, 0] = 1
M = numpy.ones((I, J))
priors = {'alpha': 3, 'beta': 4, 'lambdaU': 5, 'lambdaV': 6}
initUV = 'exp'
iterations = 1
linesearch = LineSearch(classifier, values_K, R, M, priors, initUV,
iterations)
linesearch.search()
def test_all_values():
I, J = 10, 9
values_K = [1, 2, 4, 5]
R = 2 * numpy.ones((I, J))
M = numpy.ones((I, J))
priors = {'alpha': 3, 'beta': 4, 'lambdaU': 5, 'lambdaV': 6}
initUV = 'exp'
iterations = 11
linesearch = LineSearch(classifier, values_K, R, M, priors, initUV,
iterations)
linesearch.all_performances = {
'BIC': [10, 9, 8, 7],
'AIC': [11, 13, 12, 14],
'loglikelihood': [16, 15, 18, 17]
}
assert numpy.array_equal(linesearch.all_values('BIC'), [10, 9, 8, 7])
assert numpy.array_equal(linesearch.all_values('AIC'), [11, 13, 12, 14])
assert numpy.array_equal(linesearch.all_values('loglikelihood'),
[16, 15, 18, 17])
with pytest.raises(AssertionError) as error:
linesearch.all_values('FAIL')
assert str(error.value) == "Unrecognised metric name: FAIL."
classifier = bnmf_gibbs_optimised
initUV = 'random'
# Generate data
(_, _, _, _, R) = generate_dataset(I, J, true_K, lambdaU, lambdaV, tau)
M = numpy.ones((I, J))
#M = try_generate_M(I,J,fraction_unknown,attempts_M)
# Run the line search. The priors lambdaU and lambdaV need to be a single value (recall K is unknown)
priors = {
'alpha': alpha,
'beta': beta,
'lambdaU': lambdaU[0, 0] / 10,
'lambdaV': lambdaV[0, 0] / 10
}
line_search = LineSearch(classifier, values_K, R, M, priors, initUV,
iterations, restarts)
line_search.search(burn_in, thinning)
# Plot the performances of all three metrics - but MSE separately
metrics = ['loglikelihood', 'BIC', 'AIC', 'MSE']
for metric in metrics:
plt.figure()
plt.plot(values_K, line_search.all_values(metric), label=metric)
plt.legend(loc=3)
# Also print out all values in a dictionary
all_values = {}
for metric in metrics:
all_values[metric] = line_search.all_values(metric)
print "all_values = %s" % all_values