def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
ratio = float(np.sum(b == 1)) / len(b)
lambda_max = np.abs((1 - ratio) * A[b == 1, :].sum(axis=0) +
ratio * A[b == -1, :].sum(axis=0)).max()
lam = 0.5 * lambda_max
x = cp.Variable(A.shape[1])
f = ep.logistic_loss(x, A, b) + lam * cp.norm1(x)
return cp.Problem(cp.Minimize(f))
def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
ratio = float(np.sum(b==1)) / len(b)
lambda_max = np.abs((1-ratio)*A[b==1,:].sum(axis=0) +
ratio*A[b==-1,:].sum(axis=0)).max()
lam = 0.5*lambda_max
x = cp.Variable(A.shape[1])
f = ep.logistic_loss(x, A, b) + lam*cp.norm1(x)
return cp.Problem(cp.Minimize(f))
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:
yield run_function, f
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():