def test_gp_nonfinite_phenotype():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = zeros(N)
y[0] = nan
with pytest.raises(ValueError):
GP(y, mean, cov)
y[0] = -inf
with pytest.raises(ValueError):
GP(y, mean, cov)
y[0] = +inf
with pytest.raises(ValueError):
GP(y, mean, cov)
def offset_mean():
from glimix_core.mean import OffsetMean
mean = OffsetMean(_nsamples())
mean.offset = 0.5
return mean
def _get_data():
random = RandomState(0)
N = 10
X = random.randn(N, N + 1)
X -= X.mean(0)
X /= X.std(0)
X /= sqrt(X.shape[1])
offset = 1.0
mean = OffsetMean(N)
mean.offset = offset
cov_left = LinearCov(X)
cov_left.scale = 1.5
cov_right = EyeCov(N)
cov_right.scale = 1.5
cov = SumCov([cov_left, cov_right])
lik = BernoulliProdLik(LogitLink())
y = GGPSampler(lik, mean, cov).sample(random)
return dict(mean=mean,
cov=cov,
lik=lik,
y=y,
cov_left=cov_left,
cov_right=cov_right)
def append_offset(self):
from glimix_core.mean import OffsetMean
mean = OffsetMean()
mean.set_data(self._sample_idx)
self._fixed_effects["impl"].append(mean)
self._fixed_effects["user"].append(user_mean.OffsetMean(mean))
self._fixed_effects["user"][-1].name = "offset"
self._mean = None
def test_mean_sum():
X = [[5.1, 1.0], [2.1, -0.2]]
mean0 = LinearMean(X)
mean0.effsizes = [-1.0, 0.5]
mean1 = OffsetMean(2)
mean1.offset = 2.0
mean = SumMean([mean0, mean1])
assert_allclose(mean.value(), [-2.6, -0.2])
assert_allclose(mean._check_grad(), 0, atol=1e-5)
def _outcome_sample(random, offset, X):
n = X.shape[0]
mean = OffsetMean(n)
mean.offset = offset
cov_left = LinearCov(X)
cov_left.scale = 1.5
cov_right = EyeCov(n)
cov_right.scale = 1.5
cov = SumCov([cov_left, cov_right])
lik = DeltaProdLik()
return GGPSampler(lik, mean, cov).sample(random)
def test_gp_value_1():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = random.randn(N)
gp = GP(y, mean, cov)
assert_allclose(gp.value(), -153.623791551399108)
def test_gp_gradient():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = random.randn(N)
gp = GP(y, mean, cov)
assert_allclose(gp._check_grad(), 0, atol=1e-5)
def test_gp_maximize():
random = RandomState(94584)
N = 50
X = random.randn(N, 100)
offset = 0.5
mean = OffsetMean(N)
mean.offset = offset
mean.fix_offset()
cov = LinearCov(X)
cov.scale = 1.0
y = random.randn(N)
gp = GP(y, mean, cov)
assert_allclose(gp.value(), -153.623791551)
gp.fit(verbose=False)
assert_allclose(gp.value(), -79.8992122415)
def test_lmm_predict():
random = RandomState(9458)
n = 30
X = random.randn(n, n + 1)
X -= X.mean(0)
X /= X.std(0)
X /= sqrt(X.shape[1])
offset = 1.0
mean = OffsetMean(n)
mean.offset = offset
cov_left = LinearCov(X)
cov_left.scale = 1.5
cov_right = EyeCov(n)
cov_right.scale = 1.5
cov = SumCov([cov_left, cov_right])
lik = DeltaProdLik()
y = GGPSampler(lik, mean, cov).sample(random)
QS = economic_qs_linear(X)
lmm = LMM(y, ones((n, 1)), QS)
lmm.fit(verbose=False)
plmm = LMMPredict(y, lmm.beta, lmm.v0, lmm.v1, lmm.mean(),
lmm.covariance())
K = dot(X, X.T)
pm = plmm.predictive_mean(ones((n, 1)), K, K.diagonal())
assert_allclose(corrcoef(y, pm)[0, 1], 0.8358820971891354)
def test_ggp_expfam_tobi():
random = RandomState(2)
n = 30
ntrials = random.randint(30, size=n)
K = random.randn(n, n)
K = matmul(K, K.T)
lik = BinomialProdLik(ntrials=ntrials, link=LogitLink())
mean = OffsetMean(n)
cov2 = EyeCov(n)
y = GGPSampler(lik, mean, cov2).sample(random)
ggp = ExpFamGP(y, ("binomial", ntrials), mean, cov2)
assert_allclose(ggp.lml(), -67.84095700542488)
ggp.fit(verbose=False)
assert_allclose(ggp.lml(), -64.26701904994792)
def test_mean_offset():
mean = OffsetMean(2)
mean.offset = 1.5
assert_allclose(mean.value(), [1.5, 1.5])
assert_allclose(mean.offset, 1.5)
assert_allclose(mean._check_grad(), 0, atol=1e-5)