Python learners.NSlackSSVM类代码示例

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)

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 = 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)

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)

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)

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)))

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_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')

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)

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)

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

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)

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 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)