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) > .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_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_perfect_init():
# very simple dataset. k-means init is perfect
for n_states_per_label in [2, [1, 2]]:
# test with 2 states for both foreground and background,
# as well as with single background state
X, Y = 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=n_states_per_label)
clf = LatentSSVM(
OneSlackSSVM(model=crf, max_iter=500, C=10, check_constraints=False, break_on_bad=False, inference_cache=50)
)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
assert_equal(clf.score(X, Y), 1)
def test_with_crosses_base_svms():
# very simple dataset. k-means init is perfect
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2])
one_slack = OneSlackSSVM(crf, inference_cache=50)
n_slack = NSlackSSVM(crf)
subgradient = SubgradientSSVM(crf, max_iter=400, learning_rate=0.01, decay_exponent=0, decay_t0=10)
X, Y = generate_crosses(n_samples=10, noise=5, n_crosses=1, total_size=8)
for base_ssvm in [one_slack, n_slack, subgradient]:
base_ssvm.C = 100.0
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
assert_equal(clf.score(X, Y), 1)
def test_with_crosses_base_svms():
# very simple dataset. k-means init is perfect
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2])
one_slack = OneSlackSSVM(crf, inference_cache=50)
n_slack = NSlackSSVM(crf)
subgradient = SubgradientSSVM(crf,
max_iter=400,
learning_rate=.01,
decay_exponent=0,
decay_t0=10)
X, Y = generate_crosses(n_samples=10, noise=5, n_crosses=1, total_size=8)
for base_ssvm in [one_slack, n_slack, subgradient]:
base_ssvm.C = 100.
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
assert_equal(clf.score(X, Y), 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_with_crosses_perfect_init():
# very simple dataset. k-means init is perfect
for n_states_per_label in [2, [1, 2]]:
# test with 2 states for both foreground and background,
# as well as with single background state
X, Y = 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=n_states_per_label)
clf = LatentSSVM(
OneSlackSSVM(model=crf,
max_iter=500,
C=10,
check_constraints=False,
break_on_bad=False,
inference_cache=50))
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
assert_equal(clf.score(X, Y), 1)
an additional state with different interactions, that maps to the same
state (the cross) in the ground truth.
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
from pystruct.models import LatentGridCRF
from pystruct.learners import LatentSSVM, OneSlackSSVM
from pystruct.datasets import generate_crosses
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5,
allow_nd=True)
crf = LatentGridCRF(n_states_per_label=[1, 2])
base_ssvm = OneSlackSSVM(model=crf, C=10., n_jobs=-1, inference_cache=20,
tol=.1)
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X_train, Y_train)
print("loss training set: %f" % clf.score(X_train, Y_train))
print("loss test set: %f" % clf.score(X_test, Y_test))
Y_pred = clf.predict(X_test)
x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
fig, ax = plt.subplots(3, 2)
We can still solve it by introducing latent dynamics. In essence we allow an
additional state with different interactions, that maps to the same state (the
cross) in the ground truth.
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
from pystruct.models import LatentGridCRF
from pystruct.learners import LatentSSVM, OneSlackSSVM
from pystruct.datasets import generate_crosses
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
crf = LatentGridCRF(n_states_per_label=[1, 2])
base_ssvm = OneSlackSSVM(model=crf,
C=10.,
n_jobs=-1,
inference_cache=20,
tol=.1)
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X_train, Y_train)
print("Score training set: %f" % clf.score(X_train, Y_train))
print("Score test set: %f" % clf.score(X_test, Y_test))
Y_pred = clf.predict(X_test)