Python hinge_loss Examples

Example #1

def create(**kwargs):
    A, b = problem_util.create_classification(**kwargs)
    lam = 1

    x = cp.Variable(A.shape[1])
    f = ep.hinge_loss(x, A, b) + lam * cp.sum_squares(x)
    return cp.Problem(cp.Minimize(f))

Example #2

File: hinge_l2.py Project: JeroenSoeters/epsilon

def create(**kwargs):
    A, b = problem_util.create_classification(**kwargs)
    lam = 1

    x = cp.Variable(A.shape[1])
    f = ep.hinge_loss(x, A, b) + lam*cp.sum_squares(x)
    return cp.Problem(cp.Minimize(f))

Example #3

def create(**kwargs):
    A, b = problem_util.create_classification(**kwargs)
    m = kwargs["m"]
    n = kwargs["n"]
    sigma = 0.05
    mu = kwargs.get("mu", 1)
    lam = 0.5 * sigma * np.sqrt(m * np.log(mu * n))

    x = cp.Variable(A.shape[1])
    f = ep.hinge_loss(x, A, b) + lam * cp.norm1(x)
    return cp.Problem(cp.Minimize(f))

Example #4

File: hinge_l1.py Project: JeroenSoeters/epsilon

def create(**kwargs):
    A, b = problem_util.create_classification(**kwargs)
    m = kwargs["m"]
    n = kwargs["n"]
    sigma = 0.05
    mu = kwargs.get("mu", 1)
    lam = 0.5*sigma*np.sqrt(m*np.log(mu*n))

    x = cp.Variable(A.shape[1])
    f =  ep.hinge_loss(x, A, b) + lam*cp.norm1(x)
    return cp.Problem(cp.Minimize(f))

Example #5

File: functions_test.py Project: JeroenSoeters/epsilon

k = 3
theta = cp.Variable(n)
Theta = cp.Variable(n,k)
alphas = np.linspace(1./(k+1), 1-1./(k+1), k)

np.random.seed(0)
X = np.random.randn(m,n)
y_binary = np.random.randint(2, size=(m,))*2-1
y_multi = np.random.randint(k, size=(m,))

# TODO(mwytock): Need to handle axis=1 parameter
# lambda: ep.softmax_loss(Theta, X, y_multi),
# lambda: ep.multiclass_hinge_loss(Theta, X, y_multi),

FUNCTION_TESTS = [
    lambda: ep.hinge_loss(theta, X, y_binary),
    lambda: ep.logistic_loss(theta, X, y_binary),
    lambda: ep.poisson_loss(theta, X, y_multi),
]

def run_function(f):
    prob = cp.Problem(cp.Minimize(f()))
    obj_val0 = prob.solve()

    status, obj_val1 = ep.solve(prob)
    tol = 1e-2
    assert_equal(status, OPTIMAL)
    assert_less(abs(obj_val1-obj_val0)/(1+abs(obj_val0)), tol)

def test_functions():
    for f in FUNCTION_TESTS:

Example #6

File: functions_test.py Project: mfouda/epsilon

k = 3
theta = cp.Variable(n)
Theta = cp.Variable(n, k)
alphas = np.linspace(1. / (k + 1), 1 - 1. / (k + 1), k)

np.random.seed(0)
X = np.random.randn(m, n)
y_binary = np.random.randint(2, size=(m, )) * 2 - 1
y_multi = np.random.randint(k, size=(m, ))

# TODO(mwytock): Need to handle axis=1 parameter
# lambda: ep.softmax_loss(Theta, X, y_multi),
# lambda: ep.multiclass_hinge_loss(Theta, X, y_multi),

FUNCTION_TESTS = [
    lambda: ep.hinge_loss(theta, X, y_binary),
    lambda: ep.logistic_loss(theta, X, y_binary),
    lambda: ep.poisson_loss(theta, X, y_multi),
]


def run_function(f):
    prob = cp.Problem(cp.Minimize(f()))
    obj_val0 = prob.solve()

    status, obj_val1 = ep.solve(prob)
    tol = 1e-2
    assert_equal(status, OPTIMAL)
    assert_less(abs(obj_val1 - obj_val0) / (1 + abs(obj_val0)), tol)