def test_with_crosses_bad_init():
# use less perfect initialization
rnd = np.random.RandomState(0)
X, Y = toy.generate_crosses(n_samples=20, noise=5, n_crosses=1,
total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2)
H_init = crf.init_latent(X, Y)
mask = rnd.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
one_slack = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
inference_cache=50, max_iter=10000)
n_slack = NSlackSSVM(crf)
subgradient = SubgradientSSVM(crf, max_iter=150, learning_rate=.01,
momentum=0)
for base_ssvm in [one_slack, n_slack, subgradient]:
base_ssvm.C = 10. ** 2
clf = LatentSSVM(base_ssvm)
clf.fit(X, Y, H_init=H_init)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(.98 < clf.score(X_test, Y_test) < 1)
def test_switch_to_ad3():
# smoketest only
# test if switching between qpbo and ad3 works inside latent svm
# use less perfect initialization
if not get_installed(['qpbo']) or not get_installed(['ad3']):
return
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
crf = LatentGridCRF(n_states_per_label=2,
inference_method='qpbo')
crf.initialize(X, Y)
H_init = crf.init_latent(X, Y)
np.random.seed(0)
mask = np.random.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
base_ssvm = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
inference_cache=50, max_iter=10000,
switch_to=('ad3', {'branch_and_bound': True}),
C=10. ** 3)
clf = LatentSSVM(base_ssvm)
clf.fit(X, Y, H_init=H_init)
assert_equal(clf.model.inference_method[0], 'ad3')
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(.98 < clf.score(X_test, Y_test) < 1)
def test_with_crosses_bad_init():
# use less perfect initialization
X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
total_size=8)
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
inference_method='lp')
H_init = crf.init_latent(X, Y)
mask = np.random.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
one_slack = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
inference_cache=50, max_iter=10000)
n_slack = StructuredSVM(crf)
subgradient = SubgradientSSVM(crf, max_iter=150, learning_rate=5)
for base_ssvm in [one_slack, n_slack, subgradient]:
base_ssvm.C = 10. ** 3
base_ssvm.n_jobs = -1
clf = LatentSSVM(base_ssvm)
clf.fit(X, Y, H_init=H_init)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_with_crosses_bad_init():
# use less perfect initialization
rnd = np.random.RandomState(0)
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
crf = LatentGridCRF(n_states_per_label=2)
crf.initialize(X, Y)
H_init = crf.init_latent(X, Y)
mask = rnd.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
one_slack_ssvm = OneSlackSSVM(crf,
inactive_threshold=1e-8,
cache_tol=.0001,
inference_cache=50,
C=100)
clf = LatentSSVM(one_slack_ssvm)
clf.fit(X, Y, H_init=H_init)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(.98 < clf.score(X_test, Y_test) < 1)
def test_switch_to_ad3():
# smoketest only
# test if switching between qpbo and ad3 works inside latent svm
# use less perfect initialization
if not get_installed(["qpbo"]) or not get_installed(["ad3"]):
return
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
crf = LatentGridCRF(n_states_per_label=2, inference_method="qpbo")
crf.initialize(X, Y)
H_init = crf.init_latent(X, Y)
np.random.seed(0)
mask = np.random.uniform(size=H_init.shape) > 0.7
H_init[mask] = 2 * (H_init[mask] / 2)
base_ssvm = OneSlackSSVM(
crf,
inactive_threshold=1e-8,
cache_tol=0.0001,
inference_cache=50,
max_iter=10000,
switch_to=("ad3", {"branch_and_bound": True}),
C=10.0 ** 3,
)
clf = LatentSSVM(base_ssvm)
# evil hackery to get rid of ad3 output
try:
devnull = open("/dev/null", "w")
oldstdout_fno = os.dup(sys.stdout.fileno())
os.dup2(devnull.fileno(), 1)
replaced_stdout = True
except:
replaced_stdout = False
clf.fit(X, Y, H_init=H_init)
if replaced_stdout:
os.dup2(oldstdout_fno, 1)
assert_equal(clf.model.inference_method[0], "ad3")
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(0.98 < clf.score(X_test, Y_test) < 1)
def test_switch_to_ad3():
# smoketest only
# test if switching between qpbo and ad3 works inside latent svm
# use less perfect initialization
if not get_installed(['qpbo']) or not get_installed(['ad3']):
return
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
crf = LatentGridCRF(n_states_per_label=2, inference_method='qpbo')
crf.initialize(X, Y)
H_init = crf.init_latent(X, Y)
np.random.seed(0)
mask = np.random.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
base_ssvm = OneSlackSSVM(crf,
inactive_threshold=1e-8,
cache_tol=.0001,
inference_cache=50,
max_iter=10000,
switch_to=('ad3', {
'branch_and_bound': True
}),
C=10.**3)
clf = LatentSSVM(base_ssvm)
# evil hackery to get rid of ad3 output
try:
devnull = open('/dev/null', 'w')
oldstdout_fno = os.dup(sys.stdout.fileno())
os.dup2(devnull.fileno(), 1)
replaced_stdout = True
except:
replaced_stdout = False
clf.fit(X, Y, H_init=H_init)
if replaced_stdout:
os.dup2(oldstdout_fno, 1)
assert_equal(clf.model.inference_method[0], 'ad3')
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(.98 < clf.score(X_test, Y_test) < 1)
def test_with_crosses_bad_init():
# use less perfect initialization
rnd = np.random.RandomState(0)
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
crf = LatentGridCRF(n_states_per_label=2)
crf.initialize(X, Y)
H_init = crf.init_latent(X, Y)
mask = rnd.uniform(size=H_init.shape) > 0.7
H_init[mask] = 2 * (H_init[mask] / 2)
one_slack_ssvm = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=0.0001, inference_cache=50, C=100)
clf = LatentSSVM(one_slack_ssvm)
clf.fit(X, Y, H_init=H_init)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(0.98 < clf.score(X_test, Y_test) < 1)
def test_switch_to_ad3():
# smoketest only
# test if switching between qpbo and ad3 works inside latent svm
# use less perfect initialization
if not get_installed(['qpbo']) or not get_installed(['ad3']):
return
X, Y = toy.generate_crosses(n_samples=20, noise=5, n_crosses=1,
total_size=8)
X_test, Y_test = X[10:], Y[10:]
X, Y = X[:10], Y[:10]
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
inference_method='qpbo')
H_init = crf.init_latent(X, Y)
np.random.seed(0)
mask = np.random.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
base_ssvm = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
inference_cache=50, max_iter=10000,
switch_to='ad3bb', C=10. ** 3)
clf = LatentSSVM(base_ssvm)
clf.fit(X, Y, H_init=H_init)
# we actually switch back from ad3bb to the original
assert_equal(clf.model.inference_method, "qpbo")
# unfortunately this test only works with ad3
clf.base_ssvm.model.inference_method = 'ad3bb'
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
# test that score is not always 1
assert_true(.98 < clf.score(X_test, Y_test) < 1)
def test_k_means_initialization_grid_crf():
# with only 1 state per label, nothing happends
X, Y = generate_big_checker(n_samples=10)
crf = LatentGridCRF(n_states_per_label=1, n_features=2, n_labels=2)
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_k_means_initialization_grid_crf():
# with only 1 state per label, nothing happends
X, Y = generate_big_checker(n_samples=10)
crf = LatentGridCRF(n_states_per_label=1, n_features=2, n_labels=2)
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
