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_approximate(self):
with models.multidimensional_model()[1]:
meth = ADVI()
fit(10, method=meth)
with pytest.raises(KeyError):
fit(10, method='undefined')
with pytest.raises(TypeError):
fit(10, method=1)
def test_from_advi(another_simple_model):
with another_simple_model:
advi = ADVI()
full_rank = FullRankADVI.from_advi(advi)
full_rank.fit(20)
def test_from_mean_field(another_simple_model):
with another_simple_model:
advi = ADVI()
full_rank = FullRankADVI.from_mean_field(advi.approx)
full_rank.fit(20)
def test_from_advi(self):
with models.multidimensional_model()[1]:
advi = ADVI()
full_rank = FullRankADVI.from_advi(advi)
full_rank.fit(20)
def test_from_mean_field(self):
with models.multidimensional_model()[1]:
advi = ADVI()
full_rank = FullRankADVI.from_mean_field(advi.approx)
full_rank.fit(20)
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_init_from_noize(self):
with models.multidimensional_model()[1]:
approx = Empirical.from_noise(100)
assert approx.histogram.eval().shape == (100, 6)
_model = models.simple_model()[1]
with _model:
pm.Potential('pot', tt.ones((10, 10)))
_advi = ADVI()
_fullrank_advi = FullRankADVI()
_svgd = SVGD()
@pytest.mark.parametrize(
['method', 'kwargs', 'error'],
[('undefined', dict(), KeyError), (1, dict(), TypeError),
(_advi, dict(start={}), None), (_fullrank_advi, dict(), None),
(_svgd, dict(), None), ('advi', dict(), None),
('advi->fullrank_advi', dict(frac=.1), None),
('advi->fullrank_advi', dict(frac=1), ValueError),
('fullrank_advi', dict(), None), ('svgd', dict(), None),
('svgd', dict(start={}), None),
('svgd', dict(local_rv={_model.free_RVs[0]: (0, 1)}), ValueError)])
def test_fit(method, kwargs, error):
def test_approximate(self):
with models.multidimensional_model()[1]:
meth = ADVI()
fit(10, method=meth)
self.assertRaises(KeyError, fit, 10, method='undefined')
self.assertRaises(TypeError, fit, 10, method=1)
