def test_syntetic_weak(mode):
# needs refactoring
# Syntetic data
# test latentSSVM on different train set sizes & on different train sets
# mode can be 'heterogenous' or 'latent'
results = np.zeros((18, 6))
full_labeled = np.array([0, 2, 4, 10, 25, 100])
train_size = 400
for dataset in xrange(1, 19):
X, H = load_syntetic(dataset)
H = list(H)
Y = weak_from_hidden(H)
for j, nfull in enumerate(full_labeled):
if mode == 'latent':
crf = LatentCRF(n_states=10, n_features=10, n_edge_features=2,
inference_method='qpbo')
base_clf = OneSlackSSVM(crf, max_iter=100, C=0.01, verbose=0,
tol=0.1, n_jobs=4, inference_cache=100)
clf = LatentSSVM(base_clf, latent_iter=5)
elif mode == 'heterogenous':
crf = HCRF(n_states=10, n_features=10, n_edge_features=2,
inference_method='gco')
base_clf = OneSlackSSVM(crf, max_iter=500, C=0.1, verbose=0,
tol=0.001, n_jobs=4, inference_cache=100)
clf = LatentSSVM(base_clf, latent_iter=5, verbose=0)
x_train = X[:train_size]
y_train = Y[:train_size]
h_train = H[:train_size]
x_test = X[(train_size + 1):]
h_test = H[(train_size + 1):]
for i in xrange(nfull, len(h_train)):
h_train[i] = None
try:
if mode == 'latent':
clf.fit(x_train, y_train, h_train)
elif mode == 'heterogenous':
clf.fit(x_train, y_train, h_train,
pass_labels=True, initialize=True)
h_pred = clf.predict_latent(x_test)
results[dataset - 1, j] = compute_error(h_test, h_pred)
print 'dataset=%d, nfull=%d, error=%f' % (dataset,
nfull,
results[dataset - 1, j])
except ValueError:
# bad QP
print 'dataset=%d, nfull=%d: Failed' % (dataset, nfull)
if mode == 'latent':
np.savetxt('results/weak_labeled.csv', results, delimiter=',')
elif mode == 'heterogenous':
np.savetxt('results/heterogenous.csv', results, delimiter=',')
return results
