def test_latent_node_boxes_latent_subgradient():
    # same as above, now with elementary subgradients

    # 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 = toy.make_simple_2x2(seed=1)
    latent_crf = LatentNodeCRF(n_labels=2, inference_method='lp',
                               n_hidden_states=2, n_features=1)
    latent_svm = LatentSubgradientSSVM(model=latent_crf, max_iter=250, C=10,
                                       verbose=10, learning_rate=0.1,
                                       momentum=0)

    G = [make_grid_edges(x) for x in X]

    # make edges for hidden states:
    edges = []
    node_indices = np.arange(4 * 4).reshape(4, 4)
    for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)):
        for j in xrange(x, x + 2):
            for k in xrange(y, y + 2):
                edges.append([i + 4 * 4, node_indices[j, k]])

    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_latent_subgradient():
    # same as above, now with elementary subgradients

    X, Y = toy.make_simple_2x2(seed=1)
    latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
    latent_svm = LatentSubgradientSSVM(model=latent_crf, max_iter=250, C=10,
                                       learning_rate=0.1, momentum=0)

    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 = toy.make_simple_2x2(seed=1, n_samples=40, 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_[: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)

        # we actually become prefect ^^
        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 = toy.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, 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]
        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)
Ejemplo n.º 5
0
            a.matshow(x[:size * size].reshape(size, size))
    else:
        # have hidden states
        fig, ax = plt.subplots(2, len(boxes), figsize=(20, 10))
        for a, x in zip(ax[0], boxes):
            a.matshow(x[size * size:].reshape(size / 2, size / 2))
        for a, x in zip(ax[1], boxes):
            a.matshow(x[:size * size].reshape(size, size))


# 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 = toy.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(n_states=2, n_features=1, inference_method='lp')
svm = StructuredSVM(model=crf, max_iter=200, C=1, verbose=0,
                    check_constraints=True, break_on_bad=False, n_jobs=1)

# make dataset from X and graph without edges
#G_ = [np.zeros((0, 2), dtype=np.int) for x in X]
G = [make_grid_edges(x) for x in X]