def test_batched_init_dist(self):
ar_order, steps, batch_size = 3, 100, 5
beta_tp = aesara.shared(np.random.randn(ar_order), shape=(3, ))
y_tp = np.random.randn(batch_size, steps)
with Model() as t0:
init_dist = Normal.dist(0.0, 0.01, size=(batch_size, ar_order))
AR("y",
beta_tp,
sigma=0.01,
init_dist=init_dist,
steps=steps,
initval=y_tp)
with Model() as t1:
for i in range(batch_size):
AR(f"y_{i}", beta_tp, sigma=0.01, shape=steps, initval=y_tp[i])
np.testing.assert_allclose(
t0.compile_logp()(t0.initial_point()),
t1.compile_logp()(t1.initial_point()),
)
# Next values should keep close to previous ones
beta_tp.set_value(np.full((ar_order, ), 1 / ar_order))
# Init dist is cloned when creating the AR, so the original variable is not
# part of the AR graph. We retrieve the one actually used manually
init_dist = t0["y"].owner.inputs[2]
init_dist_tp = np.full((batch_size, ar_order),
(np.arange(batch_size) * 100)[:, None])
y_eval = t0["y"].eval({init_dist: init_dist_tp})
assert y_eval.shape == (batch_size, steps + ar_order)
assert np.allclose(y_eval[:, -10:].mean(-1),
np.arange(batch_size) * 100,
rtol=0.1,
atol=0.5)
def test_batched_size(self, constant):
ar_order, steps, batch_size = 3, 100, 5
beta_tp = np.random.randn(batch_size, ar_order + int(constant))
y_tp = np.random.randn(batch_size, steps)
with Model() as t0:
y = AR("y",
beta_tp,
shape=(batch_size, steps),
initval=y_tp,
constant=constant)
with Model() as t1:
for i in range(batch_size):
AR(f"y_{i}",
beta_tp[i],
sigma=1.0,
shape=steps,
initval=y_tp[i],
constant=constant)
assert y.owner.op.ar_order == ar_order
np.testing.assert_allclose(
t0.compile_logp()(t0.initial_point()),
t1.compile_logp()(t1.initial_point()),
)
y_eval = draw(y, draws=2)
assert y_eval[0].shape == (batch_size, steps)
assert not np.any(np.isclose(y_eval[0], y_eval[1]))
def test_batched_rhos(self):
ar_order, steps, batch_size = 3, 100, 5
beta_tp = np.random.randn(batch_size, ar_order)
y_tp = np.random.randn(batch_size, steps)
with Model() as t0:
beta = Normal("beta",
0.0,
1.0,
shape=(batch_size, ar_order),
initval=beta_tp)
AR("y", beta, sigma=1.0, shape=(batch_size, steps), initval=y_tp)
with Model() as t1:
beta = Normal("beta",
0.0,
1.0,
shape=(batch_size, ar_order),
initval=beta_tp)
for i in range(batch_size):
AR(f"y_{i}", beta[i], sigma=1.0, shape=steps, initval=y_tp[i])
np.testing.assert_allclose(
t0.compile_logp()(t0.initial_point()),
t1.compile_logp()(t1.initial_point()),
)
beta_tp[1] = 0 # Should always be close to zero
y_eval = t0["y"].eval({t0["beta"]: beta_tp})
assert y_eval.shape == (batch_size, steps)
assert np.all(abs(y_eval[1]) < 5)
def test_order1_logp(self):
data = np.array([0.3, 1, 2, 3, 4])
phi = np.array([0.99])
with Model() as t:
y = AR("y", phi, sigma=1, init_dist=Flat.dist(), shape=len(data))
z = Normal("z", mu=phi * data[:-1], sigma=1, shape=len(data) - 1)
ar_like = t.compile_logp(y)({"y": data})
reg_like = t.compile_logp(z)({"z": data[1:]})
np.testing.assert_allclose(ar_like, reg_like)
with Model() as t_constant:
y = AR(
"y",
np.hstack((0.3, phi)),
sigma=1,
init_dist=Flat.dist(),
shape=len(data),
constant=True,
)
z = Normal("z",
mu=0.3 + phi * data[:-1],
sigma=1,
shape=len(data) - 1)
ar_like = t_constant.compile_logp(y)({"y": data})
reg_like = t_constant.compile_logp(z)({"z": data[1:]})
np.testing.assert_allclose(ar_like, reg_like)
def test_batched_sigma(self):
ar_order, steps, batch_size = 4, 100, (7, 5)
# AR order cannot be inferred from beta_tp because it is not fixed.
# We specify it manually below
beta_tp = aesara.shared(np.random.randn(ar_order))
sigma_tp = np.abs(np.random.randn(*batch_size))
y_tp = np.random.randn(*batch_size, steps)
with Model() as t0:
sigma = HalfNormal("sigma",
1.0,
shape=batch_size,
initval=sigma_tp)
AR(
"y",
beta_tp,
sigma=sigma,
init_dist=Normal.dist(0, sigma[..., None]),
size=batch_size,
steps=steps,
initval=y_tp,
ar_order=ar_order,
)
with Model() as t1:
sigma = HalfNormal("beta", 1.0, shape=batch_size, initval=sigma_tp)
for i in range(batch_size[0]):
for j in range(batch_size[1]):
AR(
f"y_{i}{j}",
beta_tp,
sigma=sigma[i][j],
init_dist=Normal.dist(0, sigma[i][j]),
shape=steps,
initval=y_tp[i, j],
ar_order=ar_order,
)
# Check logp shape
sigma_logp, y_logp = t0.compile_logp(sum=False)(t0.initial_point())
assert tuple(y_logp.shape) == batch_size
np.testing.assert_allclose(
sigma_logp.sum() + y_logp.sum(),
t1.compile_logp()(t1.initial_point()),
)
beta_tp.set_value(np.zeros(
(ar_order, ))) # Should always be close to zero
sigma_tp = np.full(batch_size, [0.01, 0.1, 1, 10, 100])
y_eval = t0["y"].eval({t0["sigma"]: sigma_tp})
assert y_eval.shape == (*batch_size, steps + ar_order)
assert np.allclose(y_eval.std(axis=(0, 2)), [0.01, 0.1, 1, 10, 100],
rtol=0.1)
def test_AR_nd():
# AR2 multidimensional
p, T, n = 3, 100, 5
beta_tp = np.random.randn(p, n)
y_tp = np.random.randn(T, n)
with Model() as t0:
beta = Normal("beta", 0.0, 1.0, shape=(p, n), initval=beta_tp)
AR("y", beta, sigma=1.0, shape=(T, n), initval=y_tp)
with Model() as t1:
beta = Normal("beta", 0.0, 1.0, shape=(p, n), initval=beta_tp)
for i in range(n):
AR("y_%d" % i, beta[:, i], sigma=1.0, shape=T, initval=y_tp[:, i])
np.testing.assert_allclose(t0.logp(t0.recompute_initial_point()),
t1.logp(t1.recompute_initial_point()))
def test_constant_random(self):
x = AR.dist(
rho=[100, 0, 0],
sigma=0.1,
init_dist=Normal.dist(-100.0, sigma=0.1),
constant=True,
shape=(6, ),
)
x_eval = x.eval()
assert np.allclose(x_eval[:2], -100, rtol=0.1)
assert np.allclose(x_eval[2:], 100, rtol=0.1)
def test_multivariate_init_dist(self):
init_dist = Dirichlet.dist(a=np.full((5, 2), [1, 10]))
x = AR.dist(rho=[0, 0], init_dist=init_dist, steps=0)
x_eval = x.eval()
assert x_eval.shape == (5, 2)
init_dist_eval = init_dist.eval()
init_dist_logp_eval = logp(init_dist, init_dist_eval).eval()
x_logp_eval = logp(x, init_dist_eval).eval()
assert x_logp_eval.shape == (5, )
assert np.allclose(x_logp_eval, init_dist_logp_eval)
def test_order2_logp(self):
data = np.array([0.3, 1, 2, 3, 4])
phi = np.array([0.84, 0.10])
with Model() as t:
y = AR("y", phi, sigma=1, init_dist=Flat.dist(), shape=len(data))
z = Normal("z",
mu=phi[0] * data[1:-1] + phi[1] * data[:-2],
sigma=1,
shape=len(data) - 2)
ar_like = t.compile_logp(y)({"y": data})
reg_like = t.compile_logp(z)({"z": data[2:]})
np.testing.assert_allclose(ar_like, reg_like)
def test_AR():
# AR1
data = np.array([0.3, 1, 2, 3, 4])
phi = np.array([0.99])
with Model() as t:
y = AR("y", phi, sigma=1, shape=len(data))
z = Normal("z", mu=phi * data[:-1], sigma=1, shape=len(data) - 1)
ar_like = t["y"].logp({"z": data[1:], "y": data})
reg_like = t["z"].logp({"z": data[1:], "y": data})
np.testing.assert_allclose(ar_like, reg_like)
# AR1 and AR(1)
with Model() as t:
rho = Normal("rho", 0.0, 1.0)
y1 = AR1("y1", rho, 1.0, observed=data)
y2 = AR("y2", rho, 1.0, init=Normal.dist(0, 1), observed=data)
initial_point = t.recompute_initial_point()
np.testing.assert_allclose(y1.logp(initial_point), y2.logp(initial_point))
# AR1 + constant
with Model() as t:
y = AR("y",
np.hstack((0.3, phi)),
sigma=1,
shape=len(data),
constant=True)
z = Normal("z", mu=0.3 + phi * data[:-1], sigma=1, shape=len(data) - 1)
ar_like = t["y"].logp({"z": data[1:], "y": data})
reg_like = t["z"].logp({"z": data[1:], "y": data})
np.testing.assert_allclose(ar_like, reg_like)
# AR2
phi = np.array([0.84, 0.10])
with Model() as t:
y = AR("y", phi, sigma=1, shape=len(data))
z = Normal("z",
mu=phi[0] * data[1:-1] + phi[1] * data[:-2],
sigma=1,
shape=len(data) - 2)
ar_like = t["y"].logp({"z": data[2:], "y": data})
reg_like = t["z"].logp({"z": data[2:], "y": data})
np.testing.assert_allclose(ar_like, reg_like)
def test_moment(self, size, expected):
with Model() as model:
init_dist = Constant.dist([[1.0, 2.0], [3.0, 4.0]])
AR("x", rho=[0, 0], init_dist=init_dist, steps=5, size=size)
assert_moment_is_expected(model, expected, check_finite_logp=False)