def test_sampling(self):
with models.multidimensional_model()[1]:
full_rank = FullRankADVI()
approx = full_rank.fit(20)
trace0 = approx.sample(10000)
approx = Empirical(trace0)
trace1 = approx.sample(100000)
np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.01)
np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.01)
def test_empirical_from_trace(another_simple_model):
with another_simple_model:
step = pm.Metropolis()
trace = pm.sample(100, step=step)
emp = Empirical(trace)
assert emp.histogram.shape[0].eval() == 100
trace = pm.sample(100, step=step, njobs=4)
emp = Empirical(trace)
assert emp.histogram.shape[0].eval() == 400
def test_random_with_transformed(self):
p = .2
trials = (np.random.uniform(size=10) < p).astype('int8')
with pm.Model():
p = pm.Uniform('p')
pm.Bernoulli('trials', p, observed=trials)
trace = pm.sample(1000, step=pm.Metropolis())
approx = Empirical(trace)
approx.randidx(None).eval()
approx.randidx(1).eval()
approx.random_fn(no_rand=True)
approx.random_fn(no_rand=False)
approx.histogram_logp.eval()
def test_aevb_empirical():
_, model, _ = models.exponential_beta(n=2)
x = model.x
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = ADVI(local_rv={x: (mu, rho)})
approx = inference.approx
trace0 = approx.sample(10000)
approx = Empirical(trace0, local_rv={x: (mu, rho)})
trace1 = approx.sample(10000)
np.testing.assert_allclose(trace0['y'].mean(0), trace1['y'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['y'].var(0), trace1['y'].var(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.02)
def test_aevb_empirical(self):
_, model, _ = models.exponential_beta(n=2)
x = model.x
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = ADVI(local_rv={x: (mu, rho)})
approx = inference.approx
trace0 = approx.sample(10000)
approx = Empirical(trace0, local_rv={x: (mu, rho)})
trace1 = approx.sample(10000)
approx.random(no_rand=True)
approx.random_fn(no_rand=True)
np.testing.assert_allclose(trace0['y'].mean(0),
trace1['y'].mean(0),
atol=0.02)
np.testing.assert_allclose(trace0['y'].var(0),
trace1['y'].var(0),
atol=0.02)
np.testing.assert_allclose(trace0['x'].mean(0),
trace1['x'].mean(0),
atol=0.02)
np.testing.assert_allclose(trace0['x'].var(0),
trace1['x'].var(0),
atol=0.02)
def test_from_empirical(another_simple_model):
with another_simple_model:
emp = Empirical.from_noise(1000)
svgd = SVGD.from_empirical(emp)
svgd.fit(20)
def test_init_from_noize(self):
with models.multidimensional_model()[1]:
approx = Empirical.from_noise(100)
assert approx.histogram.eval().shape == (100, 6)
def test_from_empirical(another_simple_model):
with another_simple_model:
emp = Empirical.from_noise(1000)
svgd = SVGD.from_empirical(emp)
svgd.fit(20)