def test_NestedSamples_importance_sample():
np.random.seed(3)
ns0 = NestedSamples(root='./tests/example_data/pc')
pi0 = ns0.set_beta(0)
NS0 = ns0.ns_output(nsamples=2000)
with pytest.raises(NotImplementedError):
ns0.importance_sample(ns0.logL, action='spam')
ns_masked = ns0.importance_sample(ns0.logL, action='replace')
assert_array_equal(ns0.logL, ns_masked.logL)
assert_array_equal(ns0.logL_birth, ns_masked.logL_birth)
assert_array_equal(ns0.weights, ns_masked.weights)
ns_masked = ns0.importance_sample(np.zeros_like(ns0.logL), action='add')
assert_array_equal(ns0.logL, ns_masked.logL)
assert_array_equal(ns0.logL_birth, ns_masked.logL_birth)
assert_array_equal(ns0.weights, ns_masked.weights)
mask = ((ns0.x0 > -0.3) & (ns0.x2 > 0.2) & (ns0.x4 < 3.5)).to_numpy()
ns_masked = merge_nested_samples((ns0[mask], ))
V_prior = pi0[mask].weights.sum() / pi0.weights.sum()
V_posterior = ns0[mask].weights.sum() / ns0.weights.sum()
ns1 = ns0.importance_sample(mask, action='mask')
assert_array_equal(ns_masked.logL, ns1.logL)
assert_array_equal(ns_masked.logL_birth, ns1.logL_birth)
assert_array_equal(ns_masked.weights, ns1.weights)
logL_new = np.where(mask, 0, -np.inf)
ns1 = ns0.importance_sample(logL_new)
NS1 = ns1.ns_output(nsamples=2000)
assert_array_equal(ns1, ns_masked)
logZ_V = NS0.logZ.mean() + np.log(V_posterior) - np.log(V_prior)
assert abs(NS1.logZ.mean() - logZ_V) < 1.5 * NS1.logZ.std()
logL_new = np.where(mask, 0, -1e30)
ns1 = ns0.importance_sample(logL_new)
NS1 = ns1.ns_output(nsamples=2000)
logZ_V = NS0.logZ.mean() + np.log(V_posterior)
assert abs(NS1.logZ.mean() - logZ_V) < 1.5 * NS1.logZ.std()
ns0.importance_sample(logL_new, inplace=True)
assert type(ns0) is NestedSamples
assert_array_equal(ns0, ns1)
assert ns0.tex == ns1.tex
assert ns0.limits == ns1.limits
assert ns0.root == ns1.root
assert ns0.label == ns1.label
assert ns0.beta == ns1.beta
assert ns0 is not ns1
assert ns0.tex is not ns1.tex
assert ns0.limits is not ns1.limits
def test_logzero_mask_likelihood_level():
np.random.seed(3)
ns0 = NestedSamples(root='./tests/example_data/pc')
NS0 = ns0.ns_output(nsamples=2000)
mask = ((ns0.x0 > -0.3) & (ns0.x2 > 0.2) & (ns0.x4 < 3.5)).to_numpy()
V_posterior = ns0[mask].weights.sum() / ns0.weights.sum()
logZ_V = NS0.logZ.mean() + np.log(V_posterior)
ns1 = NestedSamples(root='./tests/example_data/pc')
ns1.logL = np.where(mask, ns1.logL, -1e30)
ns1 = merge_nested_samples((ns1[ns1.logL > ns1.logL_birth],))
NS1 = ns1.ns_output(nsamples=2000)
assert abs(NS1.logZ.mean() - logZ_V) < 1.5 * NS1.logZ.std()
def test_logzero_mask_prior_level():
np.random.seed(3)
ns0 = NestedSamples(root='./tests/example_data/pc')
pi0 = ns0.set_beta(0)
NS0 = ns0.ns_output(nsamples=2000)
mask = ((ns0.x0 > -0.3) & (ns0.x2 > 0.2) & (ns0.x4 < 3.5)).to_numpy()
V_prior = pi0[mask].weights.sum() / pi0.weights.sum()
V_posterior = ns0[mask].weights.sum() / ns0.weights.sum()
logZ_V = NS0.logZ.mean() + np.log(V_posterior) - np.log(V_prior)
ns1 = merge_nested_samples((ns0[mask],))
NS1 = ns1.ns_output(nsamples=2000)
assert abs(NS1.logZ.mean() - logZ_V) < 1.5 * NS1.logZ.std()
def test_merging():
numpy.random.seed(3)
samples_1 = NestedSamples(root='./tests/example_data/pc')
samples_2 = NestedSamples(root='./tests/example_data/pc_250')
samples = merge_nested_samples([samples_1, samples_2])
nlive_1 = samples_1.nlive.mode()[0]
nlive_2 = samples_2.nlive.mode()[0]
nlive = samples.nlive.mode()[0]
assert nlive_1 == 125
assert nlive_2 == 250
assert nlive == nlive_1 + nlive_2
assert (samples.logZ() < samples_1.logZ()
and samples.logZ() > samples_2.logZ()
or samples.logZ() > samples_1.logZ()
and samples.logZ() < samples_2.logZ())
