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_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_best_value():
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],
'MSE': [16, 20, 18, 17]
}
assert linesearch.best_value('BIC') == 5
assert linesearch.best_value('AIC') == 1
assert linesearch.best_value('loglikelihood') == 2
assert linesearch.best_value('MSE') == 1
with pytest.raises(AssertionError) as error:
linesearch.all_values('FAIL')
assert str(error.value) == "Unrecognised metric name: FAIL."
classifier = bnmf_vb_optimised
# Load in data
(_, X_min, M, _, _, _, _) = load_gdsc(standardised=standardised)
folds_test = compute_folds(I, J, no_folds, M)
folds_training = compute_Ms(folds_test)
(M_train, M_test) = (folds_training[0], folds_test[0])
# Run the line search
priors = {'alpha': alpha, 'beta': beta, 'lambdaU': lambdaU, 'lambdaV': lambdaV}
line_search = LineSearch(classifier,
values_K,
X_min,
M,
priors,
initUV,
iterations,
restarts=restarts)
line_search.search()
# Plot the performances of all four metrics
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: