def test_sampling(self):
with models.multidimensional_model()[1]:
full_rank = FullRankADVI()
approx = full_rank.fit(20)
trace0 = approx.sample_vp(10000)
histogram = Histogram(trace0)
trace1 = histogram.sample_vp(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_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())
histogram = Histogram(trace)
histogram.randidx(None).eval()
histogram.randidx(1).eval()
histogram.random_fn(no_rand=True)
histogram.random_fn(no_rand=False)
histogram.histogram_logp.eval()
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())
histogram = Histogram(trace)
histogram.randidx(None).eval()
histogram.randidx(1).eval()
histogram.random_fn(no_rand=True)
histogram.random_fn(no_rand=False)
histogram.histogram_logp.eval()
def test_aevb_histogram(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_vp(10000)
histogram = Histogram(trace0, local_rv={x: (mu, rho)})
trace1 = histogram.sample_vp(10000)
histogram.random(no_rand=True)
histogram.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_aevb_histogram(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_vp(10000)
histogram = Histogram(trace0, local_rv={x: (mu, rho)})
trace1 = histogram.sample_vp(10000)
histogram.random(no_rand=True)
histogram.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_init_from_noize(self):
with models.multidimensional_model()[1]:
histogram = Histogram.from_noise(100)
assert histogram.histogram.eval().shape == (100, 6)