Example #1
0
def test_v_graphnet(X, Xlist, Y, l1=500., l2=2, l3=3.5, tol=1e-4):

    print "v_graphnet", l1, l2, l3
    A, Afull = gen_adj(X.shape[1])

    vec = np.dot(Y, X)
    l = regreg.cwpath(regreg.v_graphnet((vec, A)))
    l.problem.assign_penalty(l1=l1, l2=l2, l3=l3)
    l.fit(tol=tol, max_its=5000)
    beta = l.problem.coefficients

    lv2 = regreg.cwpath(regreg.lin_graphnet((Xlist, Y, A)))
    lv2.problem.assign_penalty(l1=l1, l2=l2, l3=l3)
    lv2.fit(tol=tol, max_its=5000)
    beta2 = lv2.problem.coefficients

    if np.linalg.norm(beta) > 1e-8:
        assert (np.linalg.norm(beta - beta2) / np.linalg.norm(beta) < 1.0e-04)
    else:
        assert (np.linalg.norm(beta2) < 1e-8)

    def f(beta):
        return -np.dot(vec, beta) + np.fabs(
            beta).sum() * l1 + l2 * np.linalg.norm(beta)**2 / 2 + l3 * np.dot(
                beta, np.dot(Afull, beta)) / 2

    v = scipy.optimize.fmin_powell(f,
                                   np.zeros(X.shape[1]),
                                   ftol=1.0e-10,
                                   xtol=1.0e-10,
                                   maxfun=100000)
    v = np.asarray(v)

    #print np.round(100*v)/100,'\n', np.round(100*beta)/100
    if f(v) < f(beta):
        assert (np.fabs(f(v) - f(beta)) / np.fabs(f(v) + f(beta)) < 1.0e-04)
        if np.linalg.norm(v) > 1e-8:
            assert (np.linalg.norm(v - beta) / np.linalg.norm(v) < 1.0e-04)
        else:
            assert (np.linalg.norm(beta) < 1e-8)
Example #2
0
def test_v_graphnet(X, Xlist, Y, l1=500.0, l2=2, l3=3.5, tol=1e-4):

    print "v_graphnet", l1, l2, l3
    A, Afull = gen_adj(X.shape[1])

    vec = np.dot(Y, X)
    l = regreg.cwpath(regreg.v_graphnet((vec, A)))
    l.problem.assign_penalty(l1=l1, l2=l2, l3=l3)
    l.fit(tol=tol, max_its=5000)
    beta = l.problem.coefficients

    lv2 = regreg.cwpath(regreg.lin_graphnet((Xlist, Y, A)))
    lv2.problem.assign_penalty(l1=l1, l2=l2, l3=l3)
    lv2.fit(tol=tol, max_its=5000)
    beta2 = lv2.problem.coefficients

    if np.linalg.norm(beta) > 1e-8:
        assert np.linalg.norm(beta - beta2) / np.linalg.norm(beta) < 1.0e-04
    else:
        assert np.linalg.norm(beta2) < 1e-8

    def f(beta):
        return (
            -np.dot(vec, beta)
            + np.fabs(beta).sum() * l1
            + l2 * np.linalg.norm(beta) ** 2 / 2
            + l3 * np.dot(beta, np.dot(Afull, beta)) / 2
        )

    v = scipy.optimize.fmin_powell(f, np.zeros(X.shape[1]), ftol=1.0e-10, xtol=1.0e-10, maxfun=100000)
    v = np.asarray(v)

    # print np.round(100*v)/100,'\n', np.round(100*beta)/100
    if f(v) < f(beta):
        assert np.fabs(f(v) - f(beta)) / np.fabs(f(v) + f(beta)) < 1.0e-04
        if np.linalg.norm(v) > 1e-8:
            assert np.linalg.norm(v - beta) / np.linalg.norm(v) < 1.0e-04
        else:
            assert np.linalg.norm(beta) < 1e-8
Example #3
0
def test_lin_graphnet(X, Xlist, Y, l1=500.0, l2=2, l3=3.5, tol=1e-4):

    print "lin_graphnet", l1, l2, l3
    A, Afull = gen_adj(X.shape[1])

    orth = np.random.normal(0, 1, X.shape[1])
    eta = np.random.normal(0, 1, 1)[0]

    # l = regreg.regreg((Xlist, Y, A),regreg.lin_graphnet,regreg.cwpath)#, initial_coefs= np.array([7.]*10))
    l = regreg.cwpath(regreg.lin_graphnet((Xlist, Y, A)))
    l.problem.assign_penalty(l1=l1, l2=l2, l3=l3, eta=eta)
    l.problem.orth = orth
    l.fit(tol=tol, max_its=5000)
    beta = l.problem.coefficients

    def f(beta):
        q = np.dot(X, beta)
        return (
            -np.dot(Y, q)
            + np.fabs(beta).sum() * l1
            + l2 * np.linalg.norm(beta) ** 2 / 2
            + l3 * np.dot(beta, np.dot(Afull, beta)) / 2
            + eta * np.dot(beta, orth)
        )

    v = scipy.optimize.fmin_powell(f, np.zeros(X.shape[1]), ftol=1.0e-10, xtol=1.0e-10, maxfun=100000)
    v = np.asarray(v)

    print np.round(100 * v) / 100, "\n", np.round(100 * beta) / 100
    print eta, np.sum(beta), np.sum(v)
    if f(v) < f(beta):
        assert np.fabs(f(v) - f(beta)) / np.fabs(f(v) + f(beta)) < 1.0e-04
        if np.linalg.norm(v) > 1e-8:
            assert np.linalg.norm(v - beta) / np.linalg.norm(v) < 1.0e-04
        else:
            assert np.linalg.norm(beta) < 1e-8
Example #4
0
def test_lin_graphnet(X, Xlist, Y, l1=500., l2=2, l3=3.5, tol=1e-4):

    print "lin_graphnet", l1, l2, l3
    A, Afull = gen_adj(X.shape[1])

    orth = np.random.normal(0, 1, X.shape[1])
    eta = np.random.normal(0, 1, 1)[0]

    #l = regreg.regreg((Xlist, Y, A),regreg.lin_graphnet,regreg.cwpath)#, initial_coefs= np.array([7.]*10))
    l = regreg.cwpath(regreg.lin_graphnet((Xlist, Y, A)))
    l.problem.assign_penalty(l1=l1, l2=l2, l3=l3, eta=eta)
    l.problem.orth = orth
    l.fit(tol=tol, max_its=5000)
    beta = l.problem.coefficients

    def f(beta):
        q = np.dot(X, beta)
        return -np.dot(Y, q) + np.fabs(
            beta).sum() * l1 + l2 * np.linalg.norm(beta)**2 / 2 + l3 * np.dot(
                beta, np.dot(Afull, beta)) / 2 + eta * np.dot(beta, orth)

    v = scipy.optimize.fmin_powell(f,
                                   np.zeros(X.shape[1]),
                                   ftol=1.0e-10,
                                   xtol=1.0e-10,
                                   maxfun=100000)
    v = np.asarray(v)

    print np.round(100 * v) / 100, '\n', np.round(100 * beta) / 100
    print eta, np.sum(beta), np.sum(v)
    if f(v) < f(beta):
        assert (np.fabs(f(v) - f(beta)) / np.fabs(f(v) + f(beta)) < 1.0e-04)
        if np.linalg.norm(v) > 1e-8:
            assert (np.linalg.norm(v - beta) / np.linalg.norm(v) < 1.0e-04)
        else:
            assert (np.linalg.norm(beta) < 1e-8)