def train(x_train, y_train, x_test, y_test):
    x_train = np.asarray(x_train, dtype=np.float)
    y_train = np.asarray(y_train, dtype=np.int64)
    # x_test = np.asarray(x_test, dtype=np.float)
    # y_test = np.asarray(y_test, dtype=np.int64)
    x_test = x_train
    y_test = y_train

    from pystruct.learners import NSlackSSVM, OneSlackSSVM, SubgradientSSVM, LatentSSVM, SubgradientLatentSSVM, PrimalDSStructuredSVM
    from pystruct.models import MultiLabelClf, MultiClassClf

    clf = OneSlackSSVM(MultiLabelClf(),
                       C=1,
                       show_loss_every=1,
                       verbose=1,
                       max_iter=1000)
    # print(x_train, y_train)
    # input()
    clf.fit(x_train, y_train)
    result = clf.predict(x_test)
    print('Result: \n', result)
    print('True label:\n', y_test)
    clf.score(x_test, y_test)
    print('\n')

    count = 0
    for i in range(len(result)):
        # print(np.sum(np.square(y_test[i]-result[i])))
        if np.sum(np.square(y_test[i] - result[i])) != 0:
            print('True label: ', y_test[i], 'Predict:  ', result[i])
            count += 1
    print(count)

    translate_vector(x_test, y_test)
Exemplo n.º 2
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def ssvm_classifier() :

	x_train,y_train,x_test,y_test = load_data1()

	print "Data Loaded"
	

	pca = PCA(n_components= 1000)
	x_train_reduced = pca.fit_transform(x_train)
	x_test_reduced = pca.fit_transform(x_test)

	print "PCA finished"

	print "Learning the model"

	n_labels = y_train.shape[1]

	full = np.vstack([x for x in itertools.combinations(range(n_labels), 2)])
	tree = chow_liu_tree(y_train)

	
	independent_model = MultiLabelClf(inference_method='unary')


	independent_ssvm = OneSlackSSVM(independent_model, C=.1, tol=0.01)
	independent_ssvm.fit(x_train_reduced, y_train)
	print "saving model ..."
	with open("data/independent_ssvm.pkl","wb+") as f :
		cp.dump(independent_ssvm,f)
	#print "Calculatin the cross-validation scores"
	#scores = model_selection.cross_val_score(independent_ssvm,x_train_reduced,y_train,cv=3)

	print independent_ssvm.score(x_test_reduced,y_test)
Exemplo n.º 3
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def syntetic():
    # train model on a single set
    models_basedir = 'models/syntetic/'
    crf = EdgeCRF(n_states=10, n_features=10, n_edge_features=2,
                  inference_method='gco')
    clf = OneSlackSSVM(crf, max_iter=10000, C=0.01, verbose=2,
                       tol=0.1, n_jobs=4, inference_cache=100)

    X, Y = load_syntetic(1)

    x_train, x_test, y_train, y_test = train_test_split(X, Y,
                                                        train_size=100,
                                                        random_state=179)

    start = time()
    clf.fit(x_train, y_train)
    stop = time()

    np.savetxt(models_basedir + 'syntetic_full.csv', clf.w)
    with open(models_basedir + 'syntetic_full' + '.pickle', 'w') as f:
        cPickle.dump(clf, f)

    y_pred = clf.predict(x_test)

    print 'Error on test set: %f' % compute_error(y_test, y_pred)
    print 'Score on test set: %f' % clf.score(x_test, y_test)
    print 'Score on train set: %f' % clf.score(x_train, y_train)
    print 'Norm of weight vector: |w|=%f' % np.linalg.norm(clf.w)
    print 'Elapsed time: %f s' % (stop - start)

    return clf
Exemplo n.º 4
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def test_constraint_removal():
    digits = load_digits()
    X, y = digits.data, digits.target
    y = 2 * (y % 2) - 1  # even vs odd as +1 vs -1
    X = X / 16.
    pbl = BinaryClf(n_features=X.shape[1])
    clf_no_removal = OneSlackSSVM(model=pbl, max_iter=500, C=1,
                                  inactive_window=0, tol=0.01)
    clf_no_removal.fit(X, y)
    clf = OneSlackSSVM(model=pbl, max_iter=500, C=1, tol=0.01,
                       inactive_threshold=1e-8)
    clf.fit(X, y)
    # check that we learned something
    assert_greater(clf.score(X, y), .92)

    # results are mostly equal
    # if we decrease tol, they will get more similar
    assert_less(np.mean(clf.predict(X) != clf_no_removal.predict(X)), 0.02)

    # without removal, have as many constraints as iterations
    assert_equal(len(clf_no_removal.objective_curve_),
                 len(clf_no_removal.constraints_))

    # with removal, there are less constraints than iterations
    assert_less(len(clf.constraints_),
                len(clf.objective_curve_))
Exemplo n.º 5
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def msrc():
    models_basedir = 'models/msrc/'
    crf = EdgeCRF(n_states=24, n_features=2028, n_edge_features=4,
                  inference_method='gco')
    clf = OneSlackSSVM(crf, max_iter=10000, C=0.01, verbose=2,
                       tol=0.1, n_jobs=4,
                       inference_cache=100)

    X, Y = load_msrc('train')
    Y = remove_areas(Y)

    start = time()
    clf.fit(X, Y)
    stop = time()

    np.savetxt(models_basedir + 'msrc_full.csv', clf.w)
    with open(models_basedir + 'msrc_full' + '.pickle', 'w') as f:
        pickle.dump(clf, f)

    X, Y = load_msrc('test')
    Y = remove_areas(Y)

    Y_pred = clf.predict(X)

    print('Error on test set: %f' % compute_error(Y, Y_pred))
    print('Score on test set: %f' % clf.score(X, Y))
    print('Norm of weight vector: |w|=%f' % np.linalg.norm(clf.w))
    print('Elapsed time: %f s' % (stop - start))

    return clf
Exemplo n.º 6
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def msrc():
    models_basedir = 'models/msrc/'
    crf = EdgeCRF(n_states=24, n_features=2028, n_edge_features=4,
                  inference_method='gco')
    clf = OneSlackSSVM(crf, max_iter=10000, C=0.01, verbose=2,
                       tol=0.1, n_jobs=4,
                       inference_cache=100)

    X, Y = load_msrc('train')
    Y = remove_areas(Y)

    start = time()
    clf.fit(X, Y)
    stop = time()

    np.savetxt(models_basedir + 'msrc_full.csv', clf.w)
    with open(models_basedir + 'msrc_full' + '.pickle', 'w') as f:
        cPickle.dump(clf, f)

    X, Y = load_msrc('test')
    Y = remove_areas(Y)

    Y_pred = clf.predict(X)

    print 'Error on test set: %f' % compute_error(Y, Y_pred)
    print 'Score on test set: %f' % clf.score(X, Y)
    print 'Norm of weight vector: |w|=%f' % np.linalg.norm(clf.w)
    print 'Elapsed time: %f s' % (stop - start)

    return clf
Exemplo n.º 7
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def test_constraint_removal():
    digits = load_digits()
    X, y = digits.data, digits.target
    y = 2 * (y % 2) - 1  # even vs odd as +1 vs -1
    X = X / 16.
    pbl = BinaryClf(n_features=X.shape[1])
    clf_no_removal = OneSlackSSVM(model=pbl, max_iter=500, C=1,
                                  inactive_window=0, tol=0.01)
    clf_no_removal.fit(X, y)
    clf = OneSlackSSVM(model=pbl, max_iter=500, C=1, tol=0.01,
                       inactive_threshold=1e-8)
    clf.fit(X, y)
    # check that we learned something
    assert_greater(clf.score(X, y), .92)

    # results are mostly equal
    # if we decrease tol, they will get more similar
    assert_less(np.mean(clf.predict(X) != clf_no_removal.predict(X)), 0.02)

    # without removal, have as many constraints as iterations
    assert_equal(len(clf_no_removal.objective_curve_),
                 len(clf_no_removal.constraints_))

    # with removal, there are less constraints than iterations
    assert_less(len(clf.constraints_),
                len(clf.objective_curve_))
Exemplo n.º 8
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def test_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="unary")
    logger = SaveLogger(file_name)
    svm = OneSlackSSVM(pbl, check_constraints=True, C=1, n_jobs=1, logger=logger)
    svm.fit(X_train, y_train)

    assert_less(0.97, svm.score(X_test, y_test))
    assert_less(0.97, logger.load().score(X_test, y_test))
Exemplo n.º 9
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def test_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='unary')
    logger = SaveLogger(file_name)
    svm = OneSlackSSVM(pbl, check_constraints=True, C=1, 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))
Exemplo n.º 10
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def test_one_slack_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 = OneSlackSSVM(model=crf, max_iter=10, C=0.01, 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 = OneSlackSSVM(
        model=crf, max_iter=10, C=0.01, check_constraints=True, negativity_constraint=[4, 5, 6]
    )
    submodular_clf.fit(X, Y)
    Y_pred = submodular_clf.predict(X)
    assert_less(submodular_clf.score(X, Y), 0.99)
    # 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])
Exemplo n.º 11
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def test_one_slack_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 = OneSlackSSVM(model=crf, max_iter=10, C=.01,
                       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 = OneSlackSSVM(model=crf, max_iter=10, C=.01,
                                  check_constraints=True,
                                  negativity_constraint=[4, 5, 6])
    submodular_clf.fit(X, Y)
    Y_pred = submodular_clf.predict(X)
    assert_less(submodular_clf.score(X, Y), .99)
    # 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])
Exemplo n.º 12
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from sklearn.utils import shuffle

from pystruct.problems import CrammerSingerSVMProblem
#from pystruct.learners import SubgradientStructuredSVM
#from pystruct.learners import StructuredSVM
from pystruct.learners import OneSlackSSVM

mnist = fetch_mldata("MNIST original")

X, y = mnist.data, mnist.target
X = X / 255.

X_train, y_train = X[:60000], y[:60000]
X_test, y_test = X[60000:], y[60000:]

X_train, y_train = shuffle(X_train, y_train)

pblm = CrammerSingerSVMProblem(n_classes=10, n_features=28**2)
#svm = SubgradientStructuredSVM(pblm, verbose=10, n_jobs=1, plot=True,
#max_iter=10, batch=False, learning_rate=0.0001,
#momentum=0)
#svm = SubgradientStructuredSVM(pblm, verbose=10, n_jobs=1, plot=True,
#max_iter=2, batch=False, momentum=.9,
#learning_rate=0.001, show_loss='true', C=1000)
svm = OneSlackSSVM(pblm, verbose=2, n_jobs=1, plot=True, max_iter=2, C=1000)
#svm = StructuredSVM(pblm, verbose=50, n_jobs=1, plot=True, max_iter=10,
#C=1000)
svm.fit(X_train, y_train)
print(svm.score(X_train, y_train))
print(svm.score(X_test, y_test))
Exemplo n.º 13
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# [Note: if you get an error on the below line, it may be because you need to upgrade scikit-learn]
encoder = OneHotEncoder(n_values=[1, 2, 2, 201, 201],
                        sparse=False).fit(np.vstack(X))
# Represent features using one-of-K scheme: If a feature can take value in
X_encoded = [
    encoder.transform(x) for x in X
]  # {0,...,K}, then introduce K binary features such that the value of only
# the i^th binary feature is non-zero when the feature takes value 'i'.
# n_values specifies the number of states each feature can take.

X_small, y_small = X_encoded[:
                             100], y[:
                                     100]  # Pick the first 100 samples from the encoded training set.

# See: http://pystruct.github.io/generated/pystruct.learners.OneSlackSSVM.html
# See: http://pystruct.github.io/generated/pystruct.models.ChainCRF.html
# Rest of documentation can be found here: http://pystruct.github.io/references.html
ssvm = OneSlackSSVM(ChainCRF(n_states=10,
                             inference_method='max-product',
                             directed=True),
                    max_iter=200,
                    C=1)
# Construct a directed ChainCRF with 10 states for each variable,
# and pass this CRF to OneSlackSSVM constructor to create an object 'ssvm'
ssvm.fit(X_small, y_small)  # Learn Structured SVM using X_small and y_small
weights = ssvm.w  # Store learnt weights in 'weights'
print ssvm.score(X_small,
                 y_small)  # Evaluate training accuracy on X_small, y_small
print ssvm.predict(
    X_small)  # Get predicted labels on X_small using the learnt model
Exemplo n.º 14
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"""
================================
Sequence classifcation benchmark
================================
This is a stripped-down version of the "plot_letters.py" example
targetted to benchmark inference and learning algorithms on chains.
"""
import numpy as np

from pystruct.datasets import load_letters
from pystruct.models import ChainCRF
from pystruct.learners import OneSlackSSVM

abc = "abcdefghijklmnopqrstuvwxyz"

letters = load_letters()
X, y, folds = letters['data'], letters['labels'], letters['folds']
# we convert the lists to object arrays, as that makes slicing much more
# convenient
X, y = np.array(X), np.array(y)
X_train, X_test = X[folds == 1], X[folds != 1]
y_train, y_test = y[folds == 1], y[folds != 1]

# Train linear chain CRF
model = ChainCRF()
ssvm = OneSlackSSVM(model=model, C=.1, tol=0.1, verbose=3, max_iter=20)
ssvm.fit(X_train, y_train)

print("Test score with chain CRF: %f" % ssvm.score(X_test, y_test))
Exemplo n.º 15
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                            sparse=False).fit(np.vstack(X))
    # FROM SAMPLE #Represent features using one-of-K scheme: If a feature can take value in
    X_encoded = [
        encoder.transform(x) for x in X
    ]  # FROM SAMPLE #{0,...,K}, then introduce K binary features such that the value of only
    return X_encoded, y, sentences  # FROM SAMPLE #the i^th binary feature is non-zero when the feature takes value 'i'.


X_train, Y_train, TrainSent = ReadData("train")

best_C = 0.1

crf = ChainCRF(n_states=10, inference_method="max-product", directed=True)
ssvm = OneSlackSSVM(crf, max_iter=200, C=best_C)
ssvm.fit(X_train[:4500], Y_train[:4500])
error = 1 - ssvm.score(X_train[-500:], Y_train[-500:])

tag = np.array([
    'verb', 'noun', 'adjective', 'adverb', 'preposition', 'pronoun',
    'determiner', 'number', 'punctuation', 'other'
])
cl = random.sample(range(10), 3)
print('Chosen classes: ', tag[cl])

trans_matrix = np.reshape(ssvm.w[-10 * 10:], (10, 10))
pairs = list(itertools.combinations(cl, 2))
for pair in pairs:
    print(tag[pair[0]], "->", tag[pair[1]], trans_matrix[pair[0]][pair[1]])
    print(tag[pair[1]], "->", tag[pair[0]], trans_matrix[pair[1]][pair[0]])

features = [
Exemplo n.º 16
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Y_train = Y_train[:4500]

crf = ChainCRF(n_states=10, inference_method='max-product', directed=True)
l1 = [10**i for i in range(-4, 3, 1)]
l1.extend([5 * l for l in l1])
Cs = sorted(l1)
error = {}
best_C = {}

Train_Sizes = [100, 200, 500, 1000, 4500]
for b in Train_Sizes:
    score = {}
    for C in Cs:
        ssvm = OneSlackSSVM(crf, max_iter=200, C=C)
        ssvm.fit(X_train[:b], Y_train[:b])
        score[C] = ssvm.score(X_val, Y_val)
        print('b = ', b, 'C = ', C, ' : ', score[C])
    best_C[b] = max(score, key=score.get)
    error['train', b] = 1. - score[best_C[b]]

for b in Train_Sizes:
    ssvm = OneSlackSSVM(crf, max_iter=200, C=best_C[b])
    ssvm.fit(X_train[:b], Y_train[:b])
    error['test', b] = 1. - ssvm.score(X_test, Y_test)

plt.xlabel('Size of the training set')
plt.ylabel('Error')
plt.plot(Train_Sizes, [error['train', b] for b in Train_Sizes], label='train')
plt.plot(Train_Sizes, [error['test', b] for b in Train_Sizes], label='test')
plt.legend()
plt.show()  #
Exemplo n.º 17
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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))

## 1-slack cutting plane ssvm
start = time()
one_slack_svm.fit(X_train_bias, y_train)
time_one_slack_svm = time() - start
acc_one_slack = one_slack_svm.score(X_test_bias, y_test)
print("Score with pystruct 1-slack ssvm: %f (took %f seconds)"
      % (acc_one_slack, time_one_slack_svm))

# online subgradient ssvm
start = time()
subgradient_svm.fit(X_train_bias, y_train)
time_subgradient_svm = time() - start
acc_subgradient = subgradient_svm.score(X_test_bias, y_test)

print("Score with pystruct subgradient ssvm: %f (took %f seconds)"
      % (acc_subgradient, time_subgradient_svm))

libsvm = SVC(kernel='linear', C=10)
start = time()
libsvm.fit(X_train, y_train)
    print('test index {}'.format(test_index))
    print('{} jackets for training, {} for testing'. \
          format(len(train_index), len(test_index)))
    X_train = X[train_index]
    Y_train = Y[train_index]
    X_test = X[test_index]
    Y_test = Y[test_index]

    start = time.time()
    """ YOUR S-SVM TRAINING CODE HERE """
    ssvm.fit(X_train, Y_train)
    end = time.time()
    print('CRF learning of 1 fold has taken {} seconds'.format(
        (end - start) / 1000.0))

    scores_crf[fold] = ssvm.score(X_test, Y_test)
    print(np.round(end - start), 'elapsed seconds to train the model')
    print("Test score with chain CRF: %f" % scores_crf[fold])
    """ Label the testing set and print results """
    Y_pred = ssvm.predict(X_test)
    wrong_fold_crf = np.sum(np.ravel(Y_test) - np.ravel(Y_pred) != 0)
    wrong_segments_crf.append(wrong_fold_crf)
    print('{} wrong segments out of {}'. \
          format(wrong_fold_crf, len(test_index) * num_segments_per_jacket))
    """ figure showing the result of classification of segments for
    each jacket in the testing part of present fold """
    if plot_labeling:
        for ti, pred in zip(test_index, Y_pred):
            print(ti)
            print(pred)
            s = segments[ti]
Exemplo n.º 19
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    total_correct = 0
    total = 0
    precision = {0: [0, 0], 1: [0, 0], 2: [0, 0]}
    recall = {0: [0, 0], 1: [0, 0], 2: [0, 0]}
    for fold in range(5):
        print 'fold ' + str(fold) + "--------------------------"
        X = []
        Y = []
        for i in range(5):
            if i == fold:
                continue
            X.extend(folds[i]['X'])
            Y.extend(folds[i]['Y'])
        print "Training Size: ", len(X), len(Y)
        ssvm.fit(X, Y)
        print 'Train Score: ' + str(ssvm.score(X, Y))
        # w = Weight(ssvm.w, node_features, edge_features, dictLength)

        fold_correct = 0
        fold_total = 0
        testX = folds[fold]['X']
        testY = folds[fold]['Y']
        for i in range(len(testX)):
            print "Instance: " + str(i)
            print folds[fold]['threads'][i].id
            Yi = testY[i]
            print list(Yi)
            infY = crf.inference(testX[i], ssvm.w)
            print list(infY)
            for py in range(len(Yi)):
                recall[Yi[py]][1] += 1
Exemplo n.º 20
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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))

## 1-slack cutting plane ssvm
start = time()
one_slack_svm.fit(X_train_bias, y_train)
time_one_slack_svm = time() - start
acc_one_slack = one_slack_svm.score(X_test_bias, y_test)
print("Score with pystruct 1-slack ssvm: %f (took %f seconds)" %
      (acc_one_slack, time_one_slack_svm))

# online subgradient ssvm
start = time()
subgradient_svm.fit(X_train_bias, y_train)
time_subgradient_svm = time() - start
acc_subgradient = subgradient_svm.score(X_test_bias, y_test)

print("Score with pystruct subgradient ssvm: %f (took %f seconds)" %
      (acc_subgradient, time_subgradient_svm))

libsvm = SVC(kernel='linear', C=10)
start = time()
libsvm.fit(X_train, y_train)
Exemplo n.º 21
0
from sklearn.utils import shuffle

from pystruct.problems import CrammerSingerSVMProblem
#from pystruct.learners import SubgradientStructuredSVM
#from pystruct.learners import StructuredSVM
from pystruct.learners import OneSlackSSVM

mnist = fetch_mldata("MNIST original")

X, y = mnist.data, mnist.target
X = X / 255.

X_train, y_train = X[:60000], y[:60000]
X_test, y_test = X[60000:], y[60000:]

X_train, y_train = shuffle(X_train, y_train)

pblm = CrammerSingerSVMProblem(n_classes=10, n_features=28 ** 2)
#svm = SubgradientStructuredSVM(pblm, verbose=10, n_jobs=1, plot=True,
                               #max_iter=10, batch=False, learning_rate=0.0001,
                               #momentum=0)
#svm = SubgradientStructuredSVM(pblm, verbose=10, n_jobs=1, plot=True,
                               #max_iter=2, batch=False, momentum=.9,
                               #learning_rate=0.001, show_loss='true', C=1000)
svm = OneSlackSSVM(pblm, verbose=2, n_jobs=1, plot=True, max_iter=2, C=1000)
#svm = StructuredSVM(pblm, verbose=50, n_jobs=1, plot=True, max_iter=10,
#C=1000)
svm.fit(X_train, y_train)
print(svm.score(X_train, y_train))
print(svm.score(X_test, y_test))
Exemplo n.º 22
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from pystruct.models import ChainCRF
from pystruct.learners import OneSlackSSVM
from util import data_generator

for shift in range(5, 7):
    print(shift)
    x_train, x_test, y_train, y_test = data_generator.baseline_crf(train_percentage=0.6, sft=shift, future=True)
    crf = ChainCRF(n_states=2, n_features=x_train.shape[1])
    x_train = x_train.values
    x_test = x_test.values
    y_train = y_train.values.astype(int)
    y_test = y_test.values.astype(int)
    ssvm = OneSlackSSVM(model=crf, C=.1, max_iter=10)
    ssvm.fit([x_train], [y_train])
    print(ssvm.score([x_test], [y_test]))