예제 #1
0
def test_testers():
    # Smoke test

    np.random.seed(2432)

    n = 200
    p = 50

    y = np.random.normal(size=n)
    x = np.random.normal(size=(n, p))

    testers = [
        kr.CorrelationEffects(),
        kr.ForwardEffects(pursuit=False),
        kr.ForwardEffects(pursuit=True),
        kr.OLSEffects()
    ]

    for method in "equi", "sdp":

        if method == "sdp" and not has_cvxopt:
            continue

        for tv in testers:
            RegressionFDR(y, x, tv, design_method=method)
예제 #2
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def test_sim():
    # This function assesses the performance of the knockoff approach
    # relative to its theoretical claims.

    np.random.seed(43234)
    npos = 30
    target_fdr = 0.2
    nrep = 10

    testers = [[kr.CorrelationEffects(), 300, 100, 6],
               [kr.ForwardEffects(pursuit=False), 300, 100, 3.5],
               [kr.ForwardEffects(pursuit=True), 300, 100, 3.5],
               [kr.OLSEffects(), 3000, 200, 3.5]]

    for method in "equi", "sdp":

        if method == "sdp" and not has_cvxopt:
            continue

        for tester_info in testers:

            fdr = 0
            power = 0
            tester = tester_info[0]
            n = tester_info[1]
            p = tester_info[2]
            es = tester_info[3]

            for k in range(nrep):

                x = np.random.normal(size=(n, p))
                x /= np.sqrt(np.sum(x * x, 0))

                coeff = es * (-1)**np.arange(npos)
                y = np.dot(x[:, 0:npos], coeff) + np.random.normal(size=n)

                kn = RegressionFDR(y, x, tester)

                tr = kn.threshold(target_fdr)
                cp = np.sum(kn.stats >= tr)
                cp = max(cp, 1)
                fp = np.sum(kn.stats[npos:] >= tr)
                fdr += fp / cp
                power += np.mean(kn.stats[0:npos] >= tr)

                estimated_fdr = (np.sum(kn.stats <= -tr) /
                                 (1 + np.sum(kn.stats >= tr)))
                assert_array_equal(estimated_fdr < target_fdr, True)

            power /= nrep
            fdr /= nrep

            assert_array_equal(power > 0.6, True)
            assert_array_equal(fdr < target_fdr + 0.05, True)
예제 #3
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    gmat = np.dot(exoga.T, exoga)

    cm1 = gmat[0:4, 0:4]
    cm2 = gmat[4:, 4:]
    cm3 = gmat[0:4, 4:]

    assert_allclose(cm1, cm2, rtol=1e-4, atol=1e-4)
    assert_allclose(cm1 - cm3, np.diag(sl * np.ones(4)), rtol=1e-5, atol=1e-5)


@pytest.mark.parametrize("p", [49, 50])
@pytest.mark.parametrize("tester", [
    kr.CorrelationEffects(),
    kr.ForwardEffects(pursuit=False),
    kr.ForwardEffects(pursuit=True),
    kr.OLSEffects(),
    kr.RegModelEffects(sm.OLS),
    kr.RegModelEffects(sm.OLS, True, fit_kws={
        "L1_wt": 0,
        "alpha": 1
    }),
])
@pytest.mark.parametrize("method", ["equi", "sdp"])
def test_testers(p, tester, method):

    if method == "sdp" and not has_cvxopt:
        return

    np.random.seed(2432)
    n = 200