Esempio n. 1
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def test_binary_blocks_cutting_plane():
    #testing cutting plane ssvm on easy binary dataset
    # generate graphs explicitly for each example
    for inference_method in get_installed(["lp", "qpbo", "ad3", 'ogm']):
        X, Y = generate_blocks(n_samples=3)
        crf = GraphCRF(inference_method=inference_method)
        clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True,
                         break_on_bad=False, n_jobs=1)
        x1, x2, x3 = X
        y1, y2, y3 = Y
        n_states = len(np.unique(Y))
        # delete some rows to make it more fun
        x1, y1 = x1[:, :-1], y1[:, :-1]
        x2, y2 = x2[:-1], y2[:-1]
        # generate graphs
        X_ = [x1, x2, x3]
        G = [make_grid_edges(x) for x in X_]

        # reshape / flatten x and y
        X_ = [x.reshape(-1, n_states) for x in X_]
        Y = [y.ravel() for y in [y1, y2, y3]]

        X = list(zip(X_, G))

        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        for y, y_pred in zip(Y, Y_pred):
            assert_array_equal(y, y_pred)
def test_logging():
    iris = load_iris()
    X, y = iris.data, iris.target

    X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
    Y = y.reshape(-1, 1)

    X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
    _, file_name = mkstemp()

    pbl = GraphCRF(n_features=4, n_states=3, inference_method=inference_method)
    logger = SaveLogger(file_name)
    svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger)
    svm.fit(X_train, y_train)

    score_current = svm.score(X_test, y_test)
    score_auto_saved = logger.load().score(X_test, y_test)

    alt_file_name = file_name + "alt"
    logger.save(svm, alt_file_name)
    logger.file_name = alt_file_name
    logger.load()
    score_manual_saved = logger.load().score(X_test, y_test)

    assert_less(.97, score_current)
    assert_less(.97, score_auto_saved)
    assert_less(.97, score_manual_saved)
    assert_almost_equal(score_auto_saved, score_manual_saved)
Esempio n. 3
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def test_binary_blocks_cutting_plane():
    #testing cutting plane ssvm on easy binary dataset
    # generate graphs explicitly for each example
    for inference_method in get_installed(["dai", "lp", "qpbo", "ad3", 'ogm']):
        print("testing %s" % inference_method)
        X, Y = generate_blocks(n_samples=3)
        crf = GraphCRF(inference_method=inference_method)
        clf = NSlackSSVM(model=crf,
                         max_iter=20,
                         C=100,
                         check_constraints=True,
                         break_on_bad=False,
                         n_jobs=1)
        x1, x2, x3 = X
        y1, y2, y3 = Y
        n_states = len(np.unique(Y))
        # delete some rows to make it more fun
        x1, y1 = x1[:, :-1], y1[:, :-1]
        x2, y2 = x2[:-1], y2[:-1]
        # generate graphs
        X_ = [x1, x2, x3]
        G = [make_grid_edges(x) for x in X_]

        # reshape / flatten x and y
        X_ = [x.reshape(-1, n_states) for x in X_]
        Y = [y.ravel() for y in [y1, y2, y3]]

        X = zip(X_, G)

        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        for y, y_pred in zip(Y, Y_pred):
            assert_array_equal(y, y_pred)
Esempio n. 4
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def test_binary_blocks_batches_n_slack():
    #testing cutting plane ssvm on easy binary dataset
    X, Y = generate_blocks(n_samples=5)
    crf = GridCRF(inference_method=inference_method)
    clf = NSlackSSVM(model=crf, max_iter=20, batch_size=1, C=100)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
Esempio n. 5
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def test_multinomial_checker_cutting_plane():
    X, Y = generate_checker_multinomial(n_samples=10, noise=.1)
    n_labels = len(np.unique(Y))
    crf = GridCRF(n_states=n_labels, inference_method=inference_method)
    clf = NSlackSSVM(model=crf, max_iter=20, C=100000, check_constraints=True)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
def test_binary_blocks_batches_n_slack():
    #testing cutting plane ssvm on easy binary dataset
    X, Y = toy.generate_blocks(n_samples=5)
    crf = GridCRF()
    clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True,
                     break_on_bad=False, n_jobs=1, batch_size=1)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
Esempio n. 7
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def test_binary_blocks_cutting_plane():
    #testing cutting plane ssvm on easy binary dataset
    X, Y = generate_blocks(n_samples=5)
    crf = GridCRF(inference_method=inference_method)
    clf = NSlackSSVM(model=crf, max_iter=20, C=100,
                     check_constraints=True, break_on_bad=False)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
Esempio n. 8
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def crammer_singer_classifier(X_train_bias, y_train, num_classes, n_jobs=2, C=1):
    model = MultiClassClf(n_features=X_train_bias.shape[1], n_classes=num_classes)
    # n-slack cutting plane ssvm
    n_slack_svm = NSlackSSVM(model, n_jobs=n_jobs, verbose=0, 
                            check_constraints=False, C=C,
                            batch_size=100, tol=1e-2)

    n_slack_svm.fit(X_train_bias, y_train)
    return n_slack_svm
Esempio n. 9
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def train_cue_learner(sentence_dicts, C_value):
    cue_lexicon, affixal_cue_lexicon = get_cue_lexicon(sentence_dicts)
    cue_sentence_dicts, cue_instances, cue_labels = extract_features_cue(
        sentence_dicts, cue_lexicon, affixal_cue_lexicon, 'training')
    vectorizer = DictVectorizer()
    fvs = vectorizer.fit_transform(cue_instances).toarray()
    model = BinaryClf()
    cue_ssvm = NSlackSSVM(model, C=C_value, batch_size=-1)
    cue_ssvm.fit(fvs, np.asarray(cue_labels))
    return cue_ssvm, vectorizer, cue_lexicon, affixal_cue_lexicon
 def fit_ssvm(self, X, Y):
     self.inference_calls = 0
     self.size_joint_feature = self.n_parameters
     ssvm_learner = NSlackSSVM(self,
                               C=1.0 / self.lambda_0,
                               max_iter=self.max_iter,
                               verbose=self.verbose)
     Y = [self.vectorize_label(y) for y in Y]
     ssvm_learner.fit(X, Y)
     self.set_weights(ssvm_learner.w)
Esempio n. 11
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def test_multinomial_blocks_cutting_plane():
    #testing cutting plane ssvm on easy multinomial dataset
    X, Y = generate_blocks_multinomial(n_samples=40, noise=0.5, seed=0)
    n_labels = len(np.unique(Y))
    crf = GridCRF(n_states=n_labels, inference_method=inference_method)
    clf = NSlackSSVM(model=crf, max_iter=100, C=100, check_constraints=False,
                     batch_size=1)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
def test_simple_1d_dataset_cutting_plane():
    # 10 1d datapoints between 0 and 1
    X = np.random.uniform(size=(30, 1))
    Y = (X.ravel() > 0.5).astype(np.int)
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])
    pbl = MultiClassClf(n_features=2)
    svm = NSlackSSVM(pbl, check_constraints=True, C=10000)
    svm.fit(X, Y)
    assert_array_equal(Y, np.hstack(svm.predict(X)))
Esempio n. 13
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def test_simple_1d_dataset_cutting_plane():
    # 10 1d datapoints between 0 and 1
    X = np.random.uniform(size=(30, 1))
    Y = (X.ravel() > 0.5).astype(np.int)
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])
    pbl = MultiClassClf(n_features=2)
    svm = NSlackSSVM(pbl, check_constraints=True, C=10000)
    svm.fit(X, Y)
    assert_array_equal(Y, np.hstack(svm.predict(X)))
Esempio n. 14
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def test_switch_to_ad3():
    # test if switching between qpbo and ad3 works

    if not get_installed(['qpbo']) or not get_installed(['ad3']):
        return
    X, Y = toy.generate_blocks_multinomial(n_samples=5, noise=1.5,
                                           seed=0)
    crf = GridCRF(n_states=3, inference_method='qpbo')

    ssvm = NSlackSSVM(crf, max_iter=10000)

    ssvm_with_switch = NSlackSSVM(crf, max_iter=10000, switch_to=('ad3'))
    ssvm.fit(X, Y)
    ssvm_with_switch.fit(X, Y)
    assert_equal(ssvm_with_switch.model.inference_method, 'ad3')
    # we check that the dual is higher with ad3 inference
    # as it might use the relaxation, that is pretty much guraranteed
    assert_greater(ssvm_with_switch.objective_curve_[-1],
                   ssvm.objective_curve_[-1])
    print(ssvm_with_switch.objective_curve_[-1], ssvm.objective_curve_[-1])

    # test that convergence also results in switch
    ssvm_with_switch = NSlackSSVM(crf, max_iter=10000, switch_to=('ad3'),
                                  tol=10)
    ssvm_with_switch.fit(X, Y)
    assert_equal(ssvm_with_switch.model.inference_method, 'ad3')
Esempio n. 15
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def CRF_oneNode(x_train, x_test, y_train, y_test):
    pbl = GraphCRF(n_states = 4,n_features=20)
    svm = NSlackSSVM(pbl,max_iter=200, C=10,n_jobs=2)
    
    svm.fit(x_train,y_train)
    y_pred = svm.predict(x_test)
    target_names = ['Start','Mid','End','Others']
    #eclf = EnsembleClassifier(clfs=[pipe1, pipe2],voting='soft',weights=[0.5,0.2])
    #eclf.fit(x_train,y_train)
    #y_pred = eclf.predict(x_test)
    print classification_report(y_test, y_pred, target_names=target_names)
Esempio n. 16
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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 = NSlackSSVM(model=crf, max_iter=200, C=100,
                                check_constraints=True,
                                positive_constraint=[5])
    submodular_clf.fit(X, Y)
    Y_pred = submodular_clf.predict(X)
    assert_array_equal(Y, Y_pred)
    assert_true(submodular_clf.w[5] < 0)  # don't ask me about signs
Esempio n. 17
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def test_binary_ssvm_attractive_potentials():
    # test that submodular SSVM can learn the block dataset
    X, Y = generate_blocks(n_samples=10)
    crf = GridCRF(inference_method=inference_method)
    submodular_clf = NSlackSSVM(model=crf, max_iter=200, C=100,
                                check_constraints=True,
                                negativity_constraint=[5])
    submodular_clf.fit(X, Y)
    Y_pred = submodular_clf.predict(X)
    assert_array_equal(Y, Y_pred)
    assert_true(submodular_clf.w[5] < 0)
Esempio n. 18
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def test_multinomial_blocks_directional():
    # testing cutting plane ssvm with directional CRF 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 = DirectionalGridCRF(n_states=n_labels)
    clf = NSlackSSVM(model=crf, max_iter=100, C=100, verbose=0,
                     check_constraints=True, batch_size=1)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
Esempio n. 19
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def test_multinomial_blocks_directional():
    # testing cutting plane ssvm with directional CRF on easy multinomial
    # dataset
    X, Y = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=0)
    n_labels = len(np.unique(Y))
    crf = DirectionalGridCRF(n_states=n_labels,
                             inference_method=inference_method)
    clf = NSlackSSVM(model=crf, max_iter=100, C=100, verbose=0,
                     check_constraints=True, batch_size=1)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
Esempio n. 20
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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))
    for inference_method in get_installed(['lp', 'qpbo', 'ad3']):
        crf = GridCRF(n_states=n_labels, inference_method=inference_method)
        clf = NSlackSSVM(model=crf, max_iter=10, C=100,
                         check_constraints=False)
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(Y, Y_pred)
Esempio n. 21
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def test_simple_1d_dataset_cutting_plane():
    # 10 1d datapoints between 0 and 1
    X = np.random.uniform(size=(30, 1))
    # linearly separable labels
    Y = 1 - 2 * (X.ravel() < .5)
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])

    pbl = BinaryClf(n_features=2)
    svm = NSlackSSVM(pbl, check_constraints=True, C=1000)
    svm.fit(X, Y)
    assert_array_equal(Y, np.hstack(svm.predict(X)))
Esempio n. 22
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def test_simple_1d_dataset_cutting_plane():
    # 10 1d datapoints between 0 and 1
    X = np.random.uniform(size=(30, 1))
    # linearly separable labels
    Y = 1 - 2 * (X.ravel() < .5)
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])

    pbl = BinaryClf(n_features=2)
    svm = NSlackSSVM(pbl, check_constraints=True, C=1000)
    svm.fit(X, Y)
    assert_array_equal(Y, np.hstack(svm.predict(X)))
def cue_trainer(filename, corenlp):
    newfilename = process_data(filename, corenlp)
    sentence_dicts = file_to_sentence_dict(newfilename)
    cue_dict, affix_cue_dict = get_cue_dict(sentence_dicts)
    sentence_dicts, cue_instances, cue_labels = extract_features_cue(
        sentence_dicts, cue_dict, affix_cue_dict, 'training')
    cue_vec = DictVectorizer()
    model = cue_vec.fit_transform(cue_instances).toarray()
    cue_ssvm = NSlackSSVM(BinaryClf(), C=0.2, batch_size=-1)
    #cue_ssvm = SVC(C = 0.2)
    cue_ssvm.fit(model, cue_labels)
    return sentence_dicts, cue_ssvm, cue_vec, cue_dict, affix_cue_dict
    """pickle.dump(cue_ssvm, open("cue_model_%s.pkl" %filename, "wb"))
Esempio n. 24
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def test_blobs_2d_cutting_plane():
    # make two gaussian blobs
    X, Y = make_blobs(n_samples=80, centers=2, random_state=1)
    Y = 2 * Y - 1
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])
    X_train, X_test, Y_train, Y_test = X[:40], X[40:], Y[:40], Y[40:]

    pbl = BinaryClf(n_features=3)
    svm = NSlackSSVM(pbl, check_constraints=True, C=1000)

    svm.fit(X_train, Y_train)
    assert_array_equal(Y_test, np.hstack(svm.predict(X_test)))
Esempio n. 25
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def test_blobs_2d_cutting_plane():
    # make two gaussian blobs
    X, Y = make_blobs(n_samples=80, centers=3, random_state=42)
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])

    X_train, X_test, Y_train, Y_test = X[:40], X[40:], Y[:40], Y[40:]

    pbl = MultiClassClf(n_features=3, n_classes=3)
    svm = NSlackSSVM(pbl, check_constraints=True, C=1000, batch_size=1)

    svm.fit(X_train, Y_train)
    assert_array_equal(Y_test, np.hstack(svm.predict(X_test)))
def test_multinomial_blocks_directional_simple():
    # testing cutting plane ssvm with directional CRF on easy multinomial
    # dataset
    X_, Y_ = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=0)
    G = [make_grid_edges(x, return_lists=True) for x in X_]
    edge_features = [edge_list_to_features(edge_list) for edge_list in G]
    edges = [np.vstack(g) for g in G]
    X = zip([x.reshape(-1, 3) for x in X_], edges, edge_features)
    Y = [y.ravel() for y in Y_]

    crf = EdgeFeatureGraphCRF(n_states=3, n_edge_features=2)
    clf = NSlackSSVM(model=crf, max_iter=10, C=1, check_constraints=False)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
def test_multinomial_blocks_directional_simple():
    # testing cutting plane ssvm with directional CRF on easy multinomial
    # dataset
    X_, Y_ = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=0)
    G = [make_grid_edges(x, return_lists=True) for x in X_]
    edge_features = [edge_list_to_features(edge_list) for edge_list in G]
    edges = [np.vstack(g) for g in G]
    X = list(zip([x.reshape(-1, 3) for x in X_], edges, edge_features))
    Y = [y.ravel() for y in Y_]

    crf = EdgeFeatureGraphCRF(n_states=3, n_edge_features=2)
    clf = NSlackSSVM(model=crf, max_iter=10, C=1, check_constraints=False)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
Esempio n. 28
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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)
Esempio n. 29
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def test_n_slack_svm_as_crf_pickling():
    iris = load_iris()
    X, y = iris.data, iris.target

    X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
    Y = y.reshape(-1, 1)

    X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
    _, file_name = mkstemp()

    pbl = GraphCRF(n_features=4, n_states=3, inference_method='lp')
    logger = SaveLogger(file_name)
    svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger)
    svm.fit(X_train, y_train)

    assert_less(.97, svm.score(X_test, y_test))
    assert_less(.97, logger.load().score(X_test, y_test))
Esempio n. 30
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def multiClf(x_train, x_test, y_train, y_test):
    #lb = preprocessing.LabelBinarizer()
    #y=y_train.reshape((1,y_train.shape[0]))
    #lb.fit(y_train)
    #y=lb.transform(y_train)
    x_train = np.array(x_train)
    y_train = np.array(y_train)
    #full = np.vstack([x for x in itertools.combinations(range(4), 2)])
    clf = pystruct.models.MultiClassClf(n_features=x_train.shape[1],n_classes=4)
    ssvm = NSlackSSVM(clf, C=.1, tol=0.01)
    
    ssvm.fit(x_train,y_train)
    y_pred = clf.predict(np.array(x_test))
    target_names = ['Start','Mid','End','Others']
    #eclf = EnsembleClassifier(clfs=[pipe1, pipe2],voting='soft',weights=[0.5,0.2])
    #eclf.fit(x_train,y_train)
    #y_pred = eclf.predict(x_test)
    print classification_report(y_test, y_pred, target_names=target_names)
Esempio n. 31
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def runIt(train_list):
    X_org = list2features(train_list)
    X = np.array(X_org)
    y = list2labels_sleep(train_list)
    y_org = np.array(y)
    Y = y_org.reshape(-1, 1)

    X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
    X_train, X_test, y_train, y_test = train_test_split(X_, Y, test_size=.5)

    pbl = GraphCRF(inference_method='unary')
    svm = NSlackSSVM(pbl, C=100)

    start = time()
    svm.fit(X_train, y_train)
    time_svm = time() - start
    y_pred = np.vstack(svm.predict(X_test))
    print("Score with pystruct crf svm: %f (took %f seconds)"
          % (np.mean(y_pred == y_test), time_svm))
def test_multinomial_blocks_directional_anti_symmetric():
    # testing cutting plane ssvm with directional CRF on easy multinomial
    # dataset
    X_, Y_ = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=0)
    G = [make_grid_edges(x, return_lists=True) for x in X_]
    edge_features = [edge_list_to_features(edge_list) for edge_list in G]
    edges = [np.vstack(g) for g in G]
    X = list(zip([x.reshape(-1, 3) for x in X_], edges, edge_features))
    Y = [y.ravel() for y in Y_]

    crf = EdgeFeatureGraphCRF(symmetric_edge_features=[0], antisymmetric_edge_features=[1])
    clf = NSlackSSVM(model=crf, C=100)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
    pairwise_params = clf.w[-9 * 2 :].reshape(2, 3, 3)
    sym = pairwise_params[0]
    antisym = pairwise_params[1]
    assert_array_equal(sym, sym.T)
    assert_array_equal(antisym, -antisym.T)
def test_binary_blocks_cutting_plane_latent_node():
    #testing cutting plane ssvm on easy binary dataset
    # we use the LatentNodeCRF without latent nodes and check that it does the
    # same as GraphCRF
    X, Y = generate_blocks(n_samples=3)
    crf = GraphCRF()
    clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True,
                     break_on_bad=False, n_jobs=1)
    x1, x2, x3 = X
    y1, y2, y3 = Y
    n_states = len(np.unique(Y))
    # delete some rows to make it more fun
    x1, y1 = x1[:, :-1], y1[:, :-1]
    x2, y2 = x2[:-1], y2[:-1]
    # generate graphs
    X_ = [x1, x2, x3]
    G = [make_grid_edges(x) for x in X_]

    # reshape / flatten x and y
    X_ = [x.reshape(-1, n_states) for x in X_]
    Y = [y.ravel() for y in [y1, y2, y3]]

    X = zip(X_, G)

    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    for y, y_pred in zip(Y, Y_pred):
        assert_array_equal(y, y_pred)

    latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=0)
    latent_svm = LatentSSVM(NSlackSSVM(model=latent_crf, max_iter=20, C=100,
                                       check_constraints=True,
                                       break_on_bad=False, n_jobs=1),
                            latent_iter=3)
    X_latent = zip(X_, G, np.zeros(len(X_)))
    latent_svm.fit(X_latent, Y, H_init=Y)
    Y_pred = latent_svm.predict(X_latent)
    for y, y_pred in zip(Y, Y_pred):
        assert_array_equal(y, y_pred)

    assert_array_almost_equal(latent_svm.w, clf.w)
def test_multinomial_blocks_directional_anti_symmetric():
    # testing cutting plane ssvm with directional CRF on easy multinomial
    # dataset
    X_, Y_ = generate_blocks_multinomial(n_samples=10, noise=0.3, seed=0)
    G = [make_grid_edges(x, return_lists=True) for x in X_]
    edge_features = [edge_list_to_features(edge_list) for edge_list in G]
    edges = [np.vstack(g) for g in G]
    X = zip([x.reshape(-1, 3) for x in X_], edges, edge_features)
    Y = [y.ravel() for y in Y_]

    crf = EdgeFeatureGraphCRF(symmetric_edge_features=[0],
                              antisymmetric_edge_features=[1])
    clf = NSlackSSVM(model=crf, C=100)
    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    assert_array_equal(Y, Y_pred)
    pairwise_params = clf.w[-9 * 2:].reshape(2, 3, 3)
    sym = pairwise_params[0]
    antisym = pairwise_params[1]
    assert_array_equal(sym, sym.T)
    assert_array_equal(antisym, -antisym.T)
Esempio n. 35
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def test_binary_ssvm_repellent_potentials():
    # test non-submodular problem with and without submodularity constraint
    # dataset is checkerboard
    X, Y = generate_checker()
    crf = GridCRF(inference_method=inference_method)
    clf = NSlackSSVM(model=crf, max_iter=10, C=100,
                     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 = NSlackSSVM(model=crf, max_iter=10, C=100,
                                check_constraints=True,
                                negativity_constraint=[4, 5, 6])
    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, 0, 0, 1, 0, 0, 0]))
        assert_array_equal(y_pred_unaries, Y_pred[i])
Esempio n. 36
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def test_states(states, x, y, x_t, y_t, jobs):
    latent_pbl = GraphLDCRF(n_states_per_label=states, inference_method='qpbo')

    base_ssvm = NSlackSSVM(latent_pbl,
                           C=1,
                           tol=.01,
                           inactive_threshold=1e-3,
                           batch_size=10,
                           verbose=0,
                           n_jobs=jobs)
    latent_svm = LatentSSVM(base_ssvm=base_ssvm, latent_iter=3)
    latent_svm.fit(x, y)

    test = latent_svm.score(x_t, y_t)
    train = latent_svm.score(x, y)

    print states, 'Test:', test, 'Train:', train
    return test, train
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)
Esempio n. 38
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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_binary_blocks_cutting_plane_latent_node():
    #testing cutting plane ssvm on easy binary dataset
    # we use the LatentNodeCRF without latent nodes and check that it does the
    # same as GraphCRF
    X, Y = generate_blocks(n_samples=3)
    crf = GraphCRF()
    clf = NSlackSSVM(model=crf,
                     max_iter=20,
                     C=100,
                     check_constraints=True,
                     break_on_bad=False,
                     n_jobs=1)
    x1, x2, x3 = X
    y1, y2, y3 = Y
    n_states = len(np.unique(Y))
    # delete some rows to make it more fun
    x1, y1 = x1[:, :-1], y1[:, :-1]
    x2, y2 = x2[:-1], y2[:-1]
    # generate graphs
    X_ = [x1, x2, x3]
    G = [make_grid_edges(x) for x in X_]

    # reshape / flatten x and y
    X_ = [x.reshape(-1, n_states) for x in X_]
    Y = [y.ravel() for y in [y1, y2, y3]]

    X = zip(X_, G)

    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    for y, y_pred in zip(Y, Y_pred):
        assert_array_equal(y, y_pred)

    latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=0)
    latent_svm = LatentSSVM(NSlackSSVM(model=latent_crf,
                                       max_iter=20,
                                       C=100,
                                       check_constraints=True,
                                       break_on_bad=False,
                                       n_jobs=1),
                            latent_iter=3)
    X_latent = zip(X_, G, np.zeros(len(X_)))
    latent_svm.fit(X_latent, Y, H_init=Y)
    Y_pred = latent_svm.predict(X_latent)
    for y, y_pred in zip(Y, Y_pred):
        assert_array_equal(y, y_pred)

    assert_array_almost_equal(latent_svm.w, clf.w)
Esempio n. 40
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                               SubgradientSSVM)

# do a binary digit classification
digits = load_digits()
X, y = digits.data, digits.target

# make binary task by doing odd vs even numers
y = y % 2
# code as +1 and -1
y = 2 * y - 1
X /= X.max()

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

pbl = BinaryClf()
n_slack_svm = NSlackSSVM(pbl, C=10, batch_size=-1)
one_slack_svm = OneSlackSSVM(pbl, C=10, tol=0.1)
subgradient_svm = SubgradientSSVM(pbl, C=10, learning_rate=0.1, max_iter=100,
                                  batch_size=10)

# we add a constant 1 feature for the bias
X_train_bias = np.hstack([X_train, np.ones((X_train.shape[0], 1))])
X_test_bias = np.hstack([X_test, np.ones((X_test.shape[0], 1))])

# n-slack cutting plane ssvm
start = time()
n_slack_svm.fit(X_train_bias, y_train)
time_n_slack_svm = time() - start
acc_n_slack = n_slack_svm.score(X_test_bias, y_test)
print("Score with pystruct n-slack ssvm: %f (took %f seconds)"
      % (acc_n_slack, time_n_slack_svm))
Esempio n. 41
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def test_ssvm_objectives():
    # test that the algorithms provide consistent objective curves.
    # this is not that strong a test now but at least makes sure that
    # the objective function is called.
    X, Y = generate_blocks_multinomial(n_samples=10, noise=1.5, seed=0)
    n_labels = len(np.unique(Y))
    crf = GridCRF(n_states=n_labels, inference_method=inference_method)
    # once for n-slack
    clf = NSlackSSVM(model=crf, max_iter=5, C=1, tol=.1)
    clf.fit(X, Y)
    primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C)
    assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective)

    # once for one-slack
    clf = OneSlackSSVM(model=crf, max_iter=5, C=1, tol=.1)
    clf.fit(X, Y)
    primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C,
                                        variant='one_slack')
    assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective)

    # now subgradient. Should also work in batch-mode.
    clf = SubgradientSSVM(model=crf, max_iter=5, C=1, batch_size=-1)
    clf.fit(X, Y)
    primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C)
    assert_almost_equal(clf.objective_curve_[-1], primal_objective)

    # frank wolfe
    clf = FrankWolfeSSVM(model=crf, max_iter=5, C=1, batch_mode=True)
    clf.fit(X, Y)
    primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C)
    assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective)
    # block-coordinate Frank-Wolfe
    clf = FrankWolfeSSVM(model=crf, max_iter=5, C=1, batch_mode=False)
    clf.fit(X, Y)
    primal_objective = objective_primal(clf.model, clf.w, X, Y, clf.C)
    assert_almost_equal(clf.primal_objective_curve_[-1], primal_objective)
Esempio n. 42
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    mean_cac = np.array(map(np.mean,labels))

    # sure there's a more efficient way to do this
    # --- Test/train split --- #
    X_train = [examples[j] for j in train]
    Y_train = [labels[j] for j in train]
    X_test = [examples[j] for j in test]
    Y_test = [labels[j] for j in test]
#    if verbose:
#        print np.mean(map(np.mean,Y_train)), 'pm',np.var(map(np.mean,Y_train))
#        print np.mean(map(np.mean,Y_test)), 'pm',np.var(map(np.mean,Y_test))
    
    # --- Train model --- #
    model = EdgeFeatureGraphCRF(n_states,n_features,n_edge_features)
    ssvm = NSlackSSVM(model=model, C=0.1, tol=0.001, verbose=0,show_loss_every=10)
#    ssvm = OneSlackSSVM(model=model, C=.1, inference_cache=50, tol=0.1, verbose=0,show_loss_every=10)
    ssvm.fit(X_train, Y_train)

    # --- Test with pystruct --- #
#        print("Test score with graph CRF: %f" % ssvm.score(X_test, Y_test))

    # --- Test manually - get contingency tables --- #
    prediction = ssvm.predict(X_test)

    contingency = np.array([0,0,0,0])
    for i in xrange(len(test)):
        pred = prediction[i]
        true = Y_test[i]
        contingency = contingency+get_contingency(pred,true)
Esempio n. 43
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# do a binary digit classification
#digits = fetch_mldata("USPS")
digits = load_digits()
X, y = digits.data, digits.target
#X = X / 255.
X = X / 16.
#y = y.astype(np.int) - 1
X_train, X_test, y_train, y_test = train_test_split(X, y)

# we add a constant 1 feature for the bias
X_train_bias = np.hstack([X_train, np.ones((X_train.shape[0], 1))])
X_test_bias = np.hstack([X_test, np.ones((X_test.shape[0], 1))])

model = MultiClassClf(n_features=X_train_bias.shape[1], n_classes=10)
n_slack_svm = NSlackSSVM(model, verbose=2, check_constraints=False, C=0.1,
                         batch_size=100, tol=1e-2)
one_slack_svm = OneSlackSSVM(model, verbose=2, C=.10, tol=.001)
subgradient_svm = SubgradientSSVM(model, C=0.1, learning_rate=0.000001,
                                  max_iter=1000, verbose=0)

fw_bc_svm = FrankWolfeSSVM(model, C=.1, max_iter=50)
fw_batch_svm = FrankWolfeSSVM(model, C=.1, max_iter=50, batch_mode=True)

# n-slack cutting plane ssvm
start = time()
n_slack_svm.fit(X_train_bias, y_train)
time_n_slack_svm = time() - start
y_pred = np.hstack(n_slack_svm.predict(X_test_bias))
print("Score with pystruct n-slack ssvm: %f (took %f seconds)"
      % (np.mean(y_pred == y_test), time_n_slack_svm))
Esempio n. 44
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from time import time
import numpy as np

from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split

from pystruct.models import GraphCRF
from pystruct.learners import NSlackSSVM

iris = load_iris()
X, y = iris.data, iris.target

# make each example into a tuple of a single feature vector and an empty edge
# list
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape(-1, 1)

X_train, X_test, y_train, y_test = train_test_split(X_, Y)

pbl = GraphCRF(n_features=4, n_states=3, inference_method='lp')
svm = NSlackSSVM(pbl, verbose=1, check_constraints=True, C=100, n_jobs=1)


start = time()
svm.fit(X_train, y_train)
time_svm = time() - start
y_pred = np.vstack(svm.predict(X_test))
print("Score with pystruct crf svm: %f (took %f seconds)"
      % (np.mean(y_pred == y_test), time_svm))
Esempio n. 45
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from pystruct.learners import NSlackSSVM, OneSlackSSVM, LatentSSVM
from sklearn.model_selection import train_test_split

mat_content = h5py.File('feat_train_1.mat')
EEG_feature = np.array(mat_content['feat_train'])
EEG_feature = EEG_feature.transpose()
mat_content = h5py.File('LABEL_train_1.mat')
EEG_label = np.array(mat_content['LABEL_train'])
EEG_label = EEG_label.transpose()

X_train, X_test, y_train, y_test = train_test_split(EEG_feature, EEG_label, test_size=0.4, random_state=0)
X_train = X_train.astype(float)
X_test = X_test.astype(float)
X_train_ = np.expand_dims(X_train, axis=1)
X_test_ = np.expand_dims(X_test, axis=1)

#latent_crf = LatentNodeCRF(n_labels=2, n_features=2140, n_hidden_states=2, inference_method='lp')
#ssvm = OneSlackSSVM(model=latent_crf, max_iter=200, C=100, n_jobs=-1, show_loss_every=10, inference_cache=50)
#latent_svm = LatentSSVM(ssvm)

# Random initialization
#H_init = 

#latent_svm.fit(X_train, Y_train, H_init)
#print("Training score with latent nodes: %f" % latent_svm.score(X, Y))
#H = latent_svm.predict_latent(X)

crf = ChainCRF()
svm = NSlackSSVM(model=crf, max_iter=200, C=1, n_jobs=1)
svm.fit(X_train, y_train)
ssvm.score(X_test, y_test)
Esempio n. 46
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y = y_org % 2

# Make each example into a tuple of a single feature vector and an empty edge
# list
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape(-1, 1)

X_train_, X_test_, X_train, X_test, y_train, y_test, y_org_train, y_org_test =\
    train_test_split(X_, X, Y, y_org, test_size=.5)

# First, perform the equivalent of the usual SVM.  This is represented as
# a CRF problem with no edges.

pbl = GraphCRF(inference_method='unary')
# We use batch_size=-1 as a binary problem can be solved in one go.
svm = NSlackSSVM(pbl, C=1, batch_size=-1)

svm.fit(X_train_, y_train)

# Now, use a latent-variabile CRF model with SVM training.
# 5 states per label is enough capacity to encode the 5 digit classes.

latent_pbl = LatentGraphCRF(n_states_per_label=5, inference_method='unary')
base_ssvm = NSlackSSVM(latent_pbl,
                       C=1,
                       tol=.01,
                       inactive_threshold=1e-3,
                       batch_size=10)
latent_svm = LatentSSVM(base_ssvm=base_ssvm, latent_iter=2)
latent_svm.fit(X_train_, y_train)
num_iter=20
C=0.1
dist=1
diag=0
inference="ad3"

print "num_train : %d num_test : %d dist : %d diag : %d num_iter : %d"%(n_train,n_test,dist,diag,num_iter)
edgeList = edges((28,28),dist=dist,diag=diag)

G = [edgeList for x in trainDirty[0:n_train]]

X_flat = [getNeighborhoodData(i) for i in trainDirty[0:n_train]]
Y_flat = np.array(trainLabels[0:n_train])

crf = GraphCRF(inference_method=inference)
svm = NSlackSSVM(model=crf,max_iter=num_iter,C=C,n_jobs=-1,verbose=1)

#%%
edgeFeatures=[]

for i in range(len(X_flat)):
    feature=[]
    for j in range(len(edgeList)):
        feature.append( np.append(X_flat[i][edgeList[j][0]] , X_flat[i][edgeList[j][1]])  )
    edgeFeatures.append(feature)
edgeFeatures=np.array(edgeFeatures)      
        
#asdf = zip(X_flat,G,edgeFeatures)        
asdf = zip(X_flat,G)                
#%%
def crf_postprocess(X_train, y_train, X_test, train_examples=2000):
    clf = NSlackSSVM(MultiLabelClf(), verbose=1, n_jobs=-1, show_loss_every=1)
    clf.fit(X_train, y_train)
    pred = clf.predict(X_test)
    pred = np.array(pred)
    return pred
Esempio n. 49
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x_test = []
for i in range(0, valid_data.shape[0]):
    temp = np.zeros((1,2),dtype = int)
    if clf.predict(valid_data[i]) == 0:
        temp[0][0] = 1
    else:
        temp[0][1] = 1
    x_test.append(temp[0])
X_test = [(x, np.empty((0, 2), dtype=np.int)) for x in x_test]
print len(x_test)
for i in range(len(test_labels)):
    test_labels = test_labels.astype(int)
"""
print len(test_labels)
pbl = GraphCRF(inference_method='ad3')
svm = NSlackSSVM(pbl, C=1,n_jobs = 1,verbose = 1)
start = time()
print len(X_valid)
print len(valid_Y)
svm.fit(X_valid, valid_Y)
print "fit finished"
time_svm = time() - start
print X_test[i][0].shape
print svm.score(X_valid,valid_Y)
print svm.score(X_test,test_Y)
y_pred = np.vstack(svm.predict(np.array(X_valid)))
print("Score with pystruct crf svm: %f (took %f seconds)"
      % (np.mean(y_pred == valid_Y), time_svm))
y_predt = np.vstack(svm.predict(np.array(X_test)))
print("Score with pystruct crf svm: %f (took %f seconds)"
      % (np.mean(y_predt == test_Y), time_svm))
Esempio n. 50
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# 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")
print("Training score binary grid CRF: %f" % svm.score(X_grid_edges, Y_flat))

# using one latent variable for each 2x2 rectangle
latent_crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2,
                           inference_method='lp')

ssvm = OneSlackSSVM(model=latent_crf, max_iter=200, C=100,
                    n_jobs=-1, show_loss_every=10, inference_cache=50)
latent_svm = LatentSSVM(ssvm)