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=('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)
Example #2
0
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 = 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)
Example #4
0
def main():
    X, Y = toy.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)
    n_labels = len(np.unique(Y_train))
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
                        inference_method='lp')
    #clf = LatentSSVM(model=crf, max_iter=500, C=1000., verbose=2,
                     #check_constraints=True, n_jobs=-1, break_on_bad=True,
                     #base_svm='1-slack', inference_cache=20, tol=.1)
    clf = LatentSubgradientSSVM(
        model=crf, max_iter=500, C=1000., verbose=2,
        n_jobs=-1, learning_rate=0.1, show_loss_every=10)
    clf.fit(X_train, Y_train)

    #for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
                            #[X_test, Y_test, [None] * len(X_test), "test"]]:
    for X_, Y_, H, name in [[X_train, Y_train, [None] * len(X_test), "train"],
                            [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        i = 0
        loss = 0
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            loss += np.sum(y != y_pred)
            fig, ax = plt.subplots(3, 2)
            ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
            ax[0, 0].set_title("ground truth")
            ax[0, 1].matshow(np.argmax(x, axis=-1),
                             vmin=0, vmax=crf.n_labels - 1)
            ax[0, 1].set_title("unaries only")
            if h_init is None:
                ax[1, 0].set_visible(False)
            else:
                ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                ax[1, 0].set_title("latent initial")
            ax[1, 1].matshow(crf.latent(x, y, clf.w),
                             vmin=0, vmax=crf.n_states - 1)
            ax[1, 1].set_title("latent final")
            ax[2, 0].matshow(crf.inference(x, clf.w),
                             vmin=0, vmax=crf.n_states - 1)
            ax[2, 0].set_title("prediction latent")
            ax[2, 1].matshow(y_pred,
                             vmin=0, vmax=crf.n_labels - 1)
            ax[2, 1].set_title("prediction")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight")
            i += 1
        print("loss %s set: %f" % (name, loss))
    print(clf.w)
Example #5
0
def main():
    X, Y = toy.generate_crosses(n_samples=40, noise=8, n_crosses=2,
                                total_size=10)
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
    n_labels = len(np.unique(Y_train))
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
                        inference_method='lp')
    clf = LatentSSVM(problem=crf, max_iter=50, C=1000., verbose=2,
                     check_constraints=True, n_jobs=-1, break_on_bad=True,
                     plot=True)
    clf.fit(X_train, Y_train)

    for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
                            [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        i = 0
        loss = 0
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            loss += np.sum(y != y_pred / crf.n_states_per_label)
            fig, ax = plt.subplots(3, 2)
            ax[0, 0].matshow(y * crf.n_states_per_label,
                             vmin=0, vmax=crf.n_states - 1)
            ax[0, 0].set_title("ground truth")
            unary_params = np.repeat(np.eye(2), 2, axis=1)
            pairwise_params = np.zeros(10)
            w_unaries_only = np.hstack([unary_params.ravel(),
                                        pairwise_params.ravel()])
            unary_pred = crf.inference(x, w_unaries_only)
            ax[0, 1].matshow(unary_pred, vmin=0, vmax=crf.n_states - 1)
            ax[0, 1].set_title("unaries only")
            if h_init is None:
                ax[1, 0].set_visible(False)
            else:
                ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                ax[1, 0].set_title("latent initial")
            ax[1, 1].matshow(crf.latent(x, y, clf.w),
                             vmin=0, vmax=crf.n_states - 1)
            ax[1, 1].set_title("latent final")
            ax[2, 0].matshow(y_pred, vmin=0, vmax=crf.n_states - 1)
            ax[2, 0].set_title("prediction")
            ax[2, 1].matshow((y_pred // crf.n_states_per_label)
                             * crf.n_states_per_label,
                             vmin=0, vmax=crf.n_states - 1)
            ax[2, 1].set_title("prediction")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight")
            i += 1
        print("loss %s set: %f" % (name, loss))
    print(clf.w)
Example #6
0
def test_with_crosses_base_svms():
    # very simple dataset. k-means init is perfect
    for base_svm in ['1-slack', 'n-slack', 'subgradient']:
        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=[1, 2],
                            inference_method='lp')
        clf = LatentSSVM(problem=crf, max_iter=150, C=10. ** 5, verbose=2,
                         check_constraints=True, n_jobs=-1, break_on_bad=True,
                         base_svm=base_svm, learning_rate=5)
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(np.array(Y_pred), Y)
Example #7
0
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')
    clf = LatentSSVM(problem=crf, max_iter=50, C=10. ** 3, verbose=2,
                     check_constraints=True, n_jobs=-1, break_on_bad=True)
    H_init = crf.init_latent(X, Y)

    mask = np.random.uniform(size=H_init.shape) > .7
    H_init[mask] = 2 * (H_init[mask] / 2)
    clf.fit(X, Y, H_init=H_init)
    Y_pred = clf.predict(X)

    assert_array_equal(np.array(Y_pred), Y)
Example #8
0
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],
                        inference_method='lp')
    one_slack = OneSlackSSVM(crf)
    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. ** 5
        base_ssvm.n_jobs = -1
        X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
                                    total_size=8)
        clf = LatentSSVM(base_ssvm=base_ssvm)
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(np.array(Y_pred), Y)
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 = 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=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)
Example #10
0
def main():
    X, Y = toy.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)
    n_labels = len(np.unique(Y_train))
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
                        inference_method='lp')
    clf = LatentSSVM(problem=crf, max_iter=50, C=1000., verbose=2,
                     check_constraints=True, n_jobs=-1, break_on_bad=True)
    clf.fit(X_train, Y_train)

    i = 0
    for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
                            [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        score = clf.score(X_, Y_)
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            fig, ax = plt.subplots(4, 1)
            ax[0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
            ax[0].set_title("Ground truth")
            ax[1].matshow(np.argmax(x, axis=-1), vmin=0, vmax=crf.n_labels - 1)
            ax[1].set_title("Unaries only")
            #if h_init is None:
                #ax[1, 0].set_visible(False)
            #else:
                #ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                #ax[1, 0].set_title("latent initial")
            #ax[2].matshow(crf.latent(x, y, clf.w),
                          #vmin=0, vmax=crf.n_states - 1)
            #ax[2].set_title("latent final")
            ax[2].matshow(crf.inference(x, clf.w), vmin=0, vmax=crf.n_states
                          - 1)
            ax[2].set_title("Prediction for h")
            ax[3].matshow(y_pred, vmin=0, vmax=crf.n_labels - 1)
            ax[3].set_title("Prediction for y")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            plt.subplots_adjust(hspace=.5)
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight",
                        dpi=400)
            i += 1
        print("score %s set: %f" % (name, score))
    print(clf.w)
Example #11
0
def test_with_crosses():
    # 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
        #for inference_method in ['ad3', 'qpbo', 'lp']:
        for inference_method in ['lp']:
            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=n_states_per_label,
                                inference_method=inference_method)
            clf = LatentSSVM(problem=crf, max_iter=50, C=10. ** 5, verbose=2,
                             check_constraints=True, n_jobs=-1,
                             break_on_bad=True)
            clf.fit(X, Y)
            Y_pred = clf.predict(X)
            assert_array_equal(np.array(Y_pred), Y)
def test_with_crosses():
    # 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
        #for inference_method in ['ad3', 'qpbo', 'lp']:
        for inference_method in ['lp']:
            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=n_states_per_label,
                                inference_method=inference_method)
            clf = LatentSubgradientSSVM(model=crf, max_iter=250, C=10. ** 5,
                                        verbose=20, learning_rate=0.0001,
                                        show_loss_every=10, momentum=0.98,
                                        decay_exponent=0)
            clf.fit(X, Y)
            Y_pred = clf.predict(X)
            assert_array_equal(np.array(Y_pred), Y)
Example #13
0
def main():
    X, Y = toy.generate_crosses(n_samples=40,
                                noise=8,
                                n_crosses=2,
                                total_size=10)
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
    n_labels = len(np.unique(Y_train))
    crf = LatentGridCRF(n_labels=n_labels,
                        n_states_per_label=2,
                        inference_method='lp')
    clf = LatentSSVM(problem=crf,
                     max_iter=50,
                     C=1000.,
                     verbose=2,
                     check_constraints=True,
                     n_jobs=-1,
                     break_on_bad=True,
                     plot=True)
    clf.fit(X_train, Y_train)

    for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
                            [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        i = 0
        loss = 0
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            loss += np.sum(y != y_pred / crf.n_states_per_label)
            fig, ax = plt.subplots(3, 2)
            ax[0, 0].matshow(y * crf.n_states_per_label,
                             vmin=0,
                             vmax=crf.n_states - 1)
            ax[0, 0].set_title("ground truth")
            unary_params = np.repeat(np.eye(2), 2, axis=1)
            pairwise_params = np.zeros(10)
            w_unaries_only = np.hstack(
                [unary_params.ravel(),
                 pairwise_params.ravel()])
            unary_pred = crf.inference(x, w_unaries_only)
            ax[0, 1].matshow(unary_pred, vmin=0, vmax=crf.n_states - 1)
            ax[0, 1].set_title("unaries only")
            if h_init is None:
                ax[1, 0].set_visible(False)
            else:
                ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                ax[1, 0].set_title("latent initial")
            ax[1, 1].matshow(crf.latent(x, y, clf.w),
                             vmin=0,
                             vmax=crf.n_states - 1)
            ax[1, 1].set_title("latent final")
            ax[2, 0].matshow(y_pred, vmin=0, vmax=crf.n_states - 1)
            ax[2, 0].set_title("prediction")
            ax[2, 1].matshow(
                (y_pred // crf.n_states_per_label) * crf.n_states_per_label,
                vmin=0,
                vmax=crf.n_states - 1)
            ax[2, 1].set_title("prediction")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight")
            i += 1
        print("loss %s set: %f" % (name, loss))
    print(clf.w)
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

import pystruct.toy_datasets as toy


X, Y = toy.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)
n_labels = len(np.unique(Y_train))
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
                    inference_method='lp')
base_ssvm = OneSlackSSVM(model=crf, max_iter=500, C=10., verbose=0,
                         check_constraints=True, n_jobs=-1,
                         break_on_bad=True, 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]