def test_latent_node_boxes_edge_features():
# learn the "easy" 2x2 boxes dataset.
# smoketest using a single constant edge feature
X, Y = make_simple_2x2(seed=1, n_samples=40)
latent_crf = EdgeFeatureLatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
base_svm = OneSlackSSVM(latent_crf)
base_svm.C = 10
latent_svm = LatentSSVM(base_svm,
latent_iter=10)
G = [make_grid_edges(x) for x in X]
# make edges for hidden states:
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
#X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
# add edge features
X_ = [(x, g, np.ones((len(g), 1)), 4) for x, g in zip(X_flat, G)]
latent_svm.fit(X_[:20], Y_flat[:20])
assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20])
assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1)
# test that score is not always 1
assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1)
def test_latent_node_boxes_standard_latent():
# learn the "easy" 2x2 boxes dataset.
# a 2x2 box is placed randomly in a 4x4 grid
# we add a latent variable for each 2x2 patch
# that should make the model fairly simple
X, Y = make_simple_2x2(seed=1, n_samples=40)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
one_slack = OneSlackSSVM(latent_crf)
n_slack = NSlackSSVM(latent_crf)
subgradient = SubgradientSSVM(latent_crf, max_iter=100)
for base_svm in [one_slack, n_slack, subgradient]:
base_svm.C = 10
latent_svm = LatentSSVM(base_svm,
latent_iter=10)
G = [make_grid_edges(x) for x in X]
# make edges for hidden states:
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat]))
latent_svm.fit(X_[:20], Y_flat[:20])
assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20])
assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1)
# test that score is not always 1
assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1)
def test_latent_node_boxes_standard_latent():
# learn the "easy" 2x2 boxes dataset.
# a 2x2 box is placed randomly in a 4x4 grid
# we add a latent variable for each 2x2 patch
# that should make the model fairly simple
X, Y = make_simple_2x2(seed=1, n_samples=40)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
one_slack = OneSlackSSVM(latent_crf)
n_slack = NSlackSSVM(latent_crf)
subgradient = SubgradientSSVM(latent_crf, max_iter=100)
for base_svm in [one_slack, n_slack, subgradient]:
base_svm.C = 10
latent_svm = LatentSSVM(base_svm, latent_iter=10)
G = [make_grid_edges(x) for x in X]
# make edges for hidden states:
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
latent_svm.fit(X_[:20], Y_flat[:20])
assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20])
assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1)
# test that score is not always 1
assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1)
def test_latent_node_boxes_edge_features():
# learn the "easy" 2x2 boxes dataset.
# smoketest using a single constant edge feature
X, Y = make_simple_2x2(seed=1, n_samples=40)
latent_crf = EdgeFeatureLatentNodeCRF(n_labels=2,
n_hidden_states=2,
n_features=1)
base_svm = OneSlackSSVM(latent_crf)
base_svm.C = 10
latent_svm = LatentSSVM(base_svm, latent_iter=10)
G = [make_grid_edges(x) for x in X]
# make edges for hidden states:
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
#X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
# add edge features
X_ = [(x, g, np.ones((len(g), 1)), 4) for x, g in zip(X_flat, G)]
latent_svm.fit(X_[:20], Y_flat[:20])
assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20])
assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1)
# test that score is not always 1
assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1)
def test_latent_node_boxes_standard_latent_features():
# learn the "easy" 2x2 boxes dataset.
# we make it even easier now by adding features that encode the correct
# latent state. This basically tests that the features are actually used
X, Y = make_simple_2x2(seed=1, n_samples=20, n_flips=6)
latent_crf = LatentNodeCRF(n_labels=2,
n_hidden_states=2,
n_features=1,
latent_node_features=True)
one_slack = OneSlackSSVM(latent_crf)
n_slack = NSlackSSVM(latent_crf)
subgradient = SubgradientSSVM(latent_crf,
max_iter=100,
learning_rate=0.01,
momentum=0)
for base_svm in [one_slack, n_slack, subgradient]:
base_svm.C = 10
latent_svm = LatentSSVM(base_svm, latent_iter=10)
G = [make_grid_edges(x) for x in X]
# make edges for hidden states:
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
# augment X with the features for hidden units
X_flat = [
np.vstack([x, y[::2, ::2].reshape(-1, 1)])
for x, y in zip(X_flat, Y)
]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
latent_svm.fit(X_[:10], Y_flat[:10])
assert_array_equal(latent_svm.predict(X_[:10]), Y_flat[:10])
assert_equal(latent_svm.score(X_[:10], Y_flat[:10]), 1)
# we actually become prefect ^^
assert_true(.98 < latent_svm.score(X_[10:], Y_flat[10:]) <= 1)
def test_latent_node_boxes_latent_subgradient():
# same as above, now with elementary subgradients
X, Y = make_simple_2x2(seed=1)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
latent_svm = SubgradientLatentSSVM(model=latent_crf, max_iter=50, C=10)
G = [make_grid_edges(x) for x in X]
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = list(zip(X_flat, G, [4 * 4 for x in X_flat]))
latent_svm.fit(X_, Y_flat)
assert_equal(latent_svm.score(X_, Y_flat), 1)
def test_latent_node_boxes_latent_subgradient():
# same as above, now with elementary subgradients
X, Y = make_simple_2x2(seed=1)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
latent_svm = SubgradientLatentSSVM(model=latent_crf, max_iter=50, C=10)
G = [make_grid_edges(x) for x in X]
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [4 * 4 for x in X_flat])
latent_svm.fit(X_, Y_flat)
assert_equal(latent_svm.score(X_, Y_flat), 1)
def test_latent_node_boxes_standard_latent_features():
# learn the "easy" 2x2 boxes dataset.
# we make it even easier now by adding features that encode the correct
# latent state. This basically tests that the features are actually used
X, Y = make_simple_2x2(seed=1, n_samples=20, n_flips=6)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1,
latent_node_features=True)
one_slack = OneSlackSSVM(latent_crf)
n_slack = NSlackSSVM(latent_crf)
subgradient = SubgradientSSVM(latent_crf, max_iter=100, learning_rate=0.01,
momentum=0)
for base_svm in [one_slack, n_slack, subgradient]:
base_svm.C = 10
latent_svm = LatentSSVM(base_svm,
latent_iter=10)
G = [make_grid_edges(x) for x in X]
# make edges for hidden states:
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
# augment X with the features for hidden units
X_flat = [np.vstack([x, y[::2, ::2].reshape(-1, 1)])
for x, y in zip(X_flat, Y)]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
latent_svm.fit(X_[:10], Y_flat[:10])
assert_array_equal(latent_svm.predict(X_[:10]), Y_flat[:10])
assert_equal(latent_svm.score(X_[:10], Y_flat[:10]), 1)
# we actually become prefect ^^
assert_true(.98 < latent_svm.score(X_[10:], Y_flat[10:]) <= 1)
for a, x in zip(ax[0], boxes):
plot_grid(x[size * size:].reshape(size / 2, size / 2), cmap=cmap,
axes=a, border_color="green")
for a, x in zip(ax[1], boxes):
plot_grid(x[:size * size].reshape(size, size), cmap=cmap, axes=a,
border_color="green")
fig.subplots_adjust(.01, .03, .98, .75, .2, .05)
fig.suptitle(title)
# learn the "easy" 2x2 boxes dataset.
# a 2x2 box is placed randomly in a 4x4 grid
# we add a latent variable for each 2x2 patch
# that should make the model fairly simple
X, Y = make_simple_2x2(seed=1)
# flatten X and Y
X_flat = [x.reshape(-1, 1).astype(np.float) for x in X]
Y_flat = [y.ravel() for y in Y]
# first, use standard graph CRF. Can't do much, high loss.
crf = GraphCRF()
svm = NSlackSSVM(model=crf, max_iter=200, C=1, n_jobs=1)
G = [make_grid_edges(x) for x in X]
X_grid_edges = list(zip(X_flat, G))
svm.fit(X_grid_edges, Y_flat)
plot_boxes(svm.predict(X_grid_edges), title="Non-latent SSVM predictions")
border_color="green")
for a, x in zip(ax[1], boxes):
plot_grid(x[:size * size].reshape(size, size),
cmap=cmap,
axes=a,
border_color="green")
fig.subplots_adjust(.01, .03, .98, .75, .2, .05)
fig.suptitle(title)
# learn the "easy" 2x2 boxes dataset.
# a 2x2 box is placed randomly in a 4x4 grid
# we add a latent variable for each 2x2 patch
# that should make the model fairly simple
X, Y = make_simple_2x2(seed=1)
# flatten X and Y
X_flat = [x.reshape(-1, 1).astype(np.float) for x in X]
Y_flat = [y.ravel() for y in Y]
# first, use standard graph CRF. Can't do much, high loss.
crf = GraphCRF()
svm = NSlackSSVM(model=crf, max_iter=200, C=1, n_jobs=1)
G = [make_grid_edges(x) for x in X]
asdf = zip(X_flat, G)
svm.fit(asdf, Y_flat)
plot_boxes(svm.predict(asdf), title="Non-latent SSVM predictions")
print("Training score binary grid CRF: %f" % svm.score(asdf, Y_flat))