def test_multinomial_checker_cutting_plane():
X, Y = toy.generate_checker_multinomial(n_samples=10, noise=0.0)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels)
clf = StructuredSVM(problem=crf, max_iter=20, C=100000, verbose=20,
check_constraints=True)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_ssvm_attractive_potentials():
# test that submodular SSVM can learn the block dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
submodular_clf = StructuredSVM(problem=crf, max_iter=200, C=100,
verbose=1, check_constraints=True,
positive_constraint=[3])
submodular_clf.fit(X, Y)
Y_pred = submodular_clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_multinomial_blocks_cutting_plane():
#testing cutting plane ssvm on easy multinomial dataset
X, Y = toy.generate_blocks_multinomial(n_samples=10, noise=0.3,
seed=0)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels)
clf = StructuredSVM(problem=crf, max_iter=10, C=100, verbose=0,
check_constraints=False)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_cutting_plane():
#testing cutting plane ssvm on easy binary dataset
#for inference_method in ["dai", "lp", "qpbo", "ad3"]:
for inference_method in ["dai", "lp", "qpbo"]:
X, Y = toy.generate_blocks(n_samples=5)
crf = GridCRF(inference_method=inference_method)
clf = StructuredSVM(problem=crf, max_iter=20, C=100, verbose=0,
check_constraints=True, break_on_bad=False,
n_jobs=-1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_ssvm_repellent_potentials():
# test non-submodular learning with and without positivity constraint
# dataset is checkerboard
X, Y = toy.generate_checker()
crf = GridCRF()
clf = StructuredSVM(problem=crf, max_iter=200, C=100,
verbose=0, check_constraints=True)
clf.fit(X, Y)
Y_pred = clf.predict(X)
# standard crf can predict perfectly
assert_array_equal(Y, Y_pred)
submodular_clf = StructuredSVM(problem=crf, max_iter=200, C=100,
verbose=0, check_constraints=True,
positive_constraint=[2, 3, 4])
submodular_clf.fit(X, Y)
Y_pred = submodular_clf.predict(X)
# submodular crf can not do better than unaries
for i, x in enumerate(X):
y_pred_unaries = crf.inference(x, np.array([1, 1, 0, 0, 0]))
assert_array_equal(y_pred_unaries, Y_pred[i])
