def incomplete_beta_ps(a, b, value):
"""Power series for incomplete beta
Use when b*x is small and value not too close to 1.
Based on Cephes library by Steve Moshier (incbet.c)
"""
one = aet.constant(1, dtype="float64")
ai = one / a
u = (one - b) * value
t1 = u / (a + one)
t = u
threshold = np.MachAr().eps * ai
s = aet.constant(0, dtype="float64")
def _step(i, t, s):
t *= (i - b) * value / i
step = t / (a + i)
s += step
return ((t, s), until(aet.abs_(step) < threshold))
(t, s), _ = scan(_step,
sequences=[aet.arange(2, 302)],
outputs_info=[e for e in aet.cast((t, s), "float64")])
s = s[-1] + t1 + ai
t = gammaln(a +
b) - gammaln(a) - gammaln(b) + a * aet.log(value) + aet.log(s)
return aet.exp(t)
def symbolic_logq_not_scaled(self):
z0 = self.symbolic_initial
std = rho2sigma(self.rho)
logdet = at.log(std)
quaddist = -0.5 * z0**2 - at.log((2 * np.pi)**0.5)
logq = quaddist - logdet
return logq.sum(range(1, logq.ndim))
def check_jacobian_det(
transform,
domain,
constructor=at.dscalar,
test=0,
make_comparable=None,
elemwise=False,
rv_var=None,
):
y = constructor("y")
y.tag.test_value = test
if rv_var is None:
rv_var = y
rv_inputs = rv_var.owner.inputs if rv_var.owner else []
x = transform.backward(y, *rv_inputs)
if make_comparable:
x = make_comparable(x)
if not elemwise:
jac = at.log(at.nlinalg.det(jacobian(x, [y])))
else:
jac = at.log(at.abs_(at.diag(jacobian(x, [y]))))
# ljd = log jacobian det
actual_ljd = aesara.function([y], jac)
computed_ljd = aesara.function(
[y], at.as_tensor_variable(transform.log_jac_det(y, *rv_inputs)), on_unused_input="ignore"
)
for yval in domain.vals:
close_to(actual_ljd(yval), computed_ljd(yval), tol)
def logdet(self):
z = self.z0 # sxd
u = self.u_ # d
w = self.w_ # d
b = self.b # .
deriv = self.h.deriv # f'
if not self.batched:
# f'(sxd \dot d + .) * -xd = sxd
phi = deriv(z.dot(w) + b).dimshuffle(0, "x") * w.dimshuffle("x", 0)
# \abs(. + sxd \dot d) = s
det = aet.abs_(1.0 + phi.dot(u))
return aet.log(det)
else:
z = z.swapaxes(0, 1)
b = b.dimshuffle(0, "x")
# z bxsxd
# u bxd
# w bxd
# b bx-x-
# f'(bxsxd \bdot bxd + bx-x-) * bx-xd = bxsxd
phi = deriv(aet.batched_dot(z, w) + b).dimshuffle(
0, 1, "x") * w.dimshuffle(0, "x", 1)
# \abs(. + bxsxd \bdot bxd) = bxs
det = aet.abs_(1.0 + aet.batched_dot(phi, u)) # bxs
return aet.log(det).sum(0) # s
def _build_marginal_likelihood_logp(self, y, X, Xu, sigma):
sigma2 = at.square(sigma)
Kuu = self.cov_func(Xu)
Kuf = self.cov_func(Xu, X)
Luu = cholesky(stabilize(Kuu))
A = solve_lower(Luu, Kuf)
Qffd = at.sum(A * A, 0)
if self.approx == "FITC":
Kffd = self.cov_func(X, diag=True)
Lamd = at.clip(Kffd - Qffd, 0.0, np.inf) + sigma2
trace = 0.0
elif self.approx == "VFE":
Lamd = at.ones_like(Qffd) * sigma2
trace = (1.0 /
(2.0 * sigma2)) * (at.sum(self.cov_func(X, diag=True)) -
at.sum(at.sum(A * A, 0)))
else: # DTC
Lamd = at.ones_like(Qffd) * sigma2
trace = 0.0
A_l = A / Lamd
L_B = cholesky(at.eye(Xu.shape[0]) + at.dot(A_l, at.transpose(A)))
r = y - self.mean_func(X)
r_l = r / Lamd
c = solve_lower(L_B, at.dot(A, r_l))
constant = 0.5 * X.shape[0] * at.log(2.0 * np.pi)
logdet = 0.5 * at.sum(at.log(Lamd)) + at.sum(at.log(at.diag(L_B)))
quadratic = 0.5 * (at.dot(r, r_l) - at.dot(c, c))
return -1.0 * (constant + logdet + quadratic + trace)
def symbolic_logq_not_scaled(self):
z0 = self.symbolic_initial
diag = at.diagonal(self.L, 0, self.L.ndim - 2, self.L.ndim - 1)
logdet = at.log(diag)
quaddist = -0.5 * z0**2 - at.log((2 * np.pi)**0.5)
logq = quaddist - logdet
return logq.sum(range(1, logq.ndim))
def two_gaussians(x):
"""
Mixture of gaussians likelihood
"""
log_like1 = (-0.5 * n * at.log(2 * np.pi) - 0.5 * at.log(dsigma) -
0.5 * (x - mu1).T.dot(isigma).dot(x - mu1))
log_like2 = (-0.5 * n * at.log(2 * np.pi) - 0.5 * at.log(dsigma) -
0.5 * (x - mu2).T.dot(isigma).dot(x - mu2))
return at.log(w1 * at.exp(log_like1) + w2 * at.exp(log_like2))
def forward(self, rv_var, rv_value):
a, b = self.param_extract_fn(rv_var)
if a is not None and b is not None:
return at.log(rv_value - a) - at.log(b - rv_value)
elif a is not None:
return at.log(rv_value - a)
elif b is not None:
return at.log(b - rv_value)
else:
return rv_value
def logp(self, states):
r"""Create a Theano graph that computes the log-likelihood for a discrete Markov chain.
This is the log-likelihood for the joint distribution of states, :math:`S_t`, conditional
on state samples, :math:`s_t`, given by the following:
.. math::
\int_{S_0} P(S_1 = s_1 \mid S_0) dP(S_0) \prod^{T}_{t=2} P(S_t = s_t \mid S_{t-1} = s_{t-1})
The first term (i.e. the integral) simply computes the marginal :math:`P(S_1 = s_1)`, so
another way to express this result is as follows:
.. math::
P(S_1 = s_1) \prod^{T}_{t=2} P(S_t = s_t \mid S_{t-1} = s_{t-1})
""" # noqa: E501
states_tt = at.as_tensor(states)
if states.ndim > 1 or self.Gammas.ndim > 3 or self.gamma_0.ndim > 1:
raise NotImplementedError("Broadcasting not supported.")
Gammas_tt = at_broadcast_to(self.Gammas, (states.shape[0], ) +
tuple(self.Gammas.shape)[-2:])
gamma_0_tt = self.gamma_0
Gamma_1_tt = Gammas_tt[0]
P_S_1_tt = at.dot(gamma_0_tt, Gamma_1_tt)[states_tt[0]]
# def S_logp_fn(S_tm1, S_t, Gamma):
# return at.log(Gamma[..., S_tm1, S_t])
#
# P_S_2T_tt, _ = aesara.scan(
# S_logp_fn,
# sequences=[
# {
# "input": states_tt,
# "taps": [-1, 0],
# },
# Gammas_tt,
# ],
# )
P_S_2T_tt = Gammas_tt[at.arange(1, states.shape[0]), states[:-1],
states[1:]]
log_P_S_1T_tt = at.concatenate(
[at.shape_padright(at.log(P_S_1_tt)),
at.log(P_S_2T_tt)])
res = log_P_S_1T_tt.sum()
res.name = "states_logp"
return res
def two_gaussians(x):
log_like1 = (
-0.5 * n * aet.log(2 * np.pi)
- 0.5 * aet.log(dsigma)
- 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)
)
log_like2 = (
-0.5 * n * aet.log(2 * np.pi)
- 0.5 * aet.log(dsigma)
- 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)
)
return aet.log(w1 * aet.exp(log_like1) + w2 * aet.exp(log_like2))
def test_tt_logdotexp():
np.seterr(over="ignore", under="ignore")
aesara.config.compute_test_value = "warn"
A = np.c_[[1.0, 2.0], [3.0, 4.0], [10.0, 20.0]]
b = np.c_[[0.1], [0.2], [30.0]].T
A_tt = at.as_tensor_variable(A)
b_tt = at.as_tensor_variable(b)
test_res = tt_logdotexp(at.log(A_tt), at.log(b_tt)).eval()
assert test_res.shape == (2, 1)
assert np.allclose(A.dot(b), np.exp(test_res))
b = np.r_[0.1, 0.2, 30.0]
test_res = tt_logdotexp(at.log(A), at.log(b)).eval()
assert test_res.shape == (2,)
assert np.allclose(A.dot(b), np.exp(test_res))
A = np.c_[[1.0, 2.0], [10.0, 20.0]]
b = np.c_[[0.1], [0.2]].T
test_res = tt_logdotexp(at.log(A), at.log(b)).eval()
assert test_res.shape == (2, 1)
assert np.allclose(A.dot(b), np.exp(test_res))
b = np.r_[0.1, 0.2]
test_res = tt_logdotexp(at.log(A), at.log(b)).eval()
assert test_res.shape == (2,)
assert np.allclose(A.dot(b), np.exp(test_res))
def logdet(self):
d = float(self.dim)
a = self.a_ # .
b = self.b_ # .
z_ref = self.z_ref # d
z = self.z0 # sxd
h = self.h # h(a, r)
deriv = self.h.deriv # h'(a, r)
if self.batched:
z = z.swapaxes(0, 1)
a = a.dimshuffle(0, "x", "x")
b = b.dimshuffle(0, "x", "x")
z_ref = z_ref.dimshuffle(0, "x", 1)
# a bx-x-
# b bx-x-
# z bxsxd
# z_ref bx-xd
r = (z - z_ref).norm(2, axis=-1, keepdims=True) # s
har = h(a, r)
dar = deriv(a, r)
logdet = aet.log(
(1.0 + b * har)**(d - 1.0) * (1.0 + b * har + b * dar * r))
if self.batched:
return logdet.sum([0, -1])
else:
return logdet.sum(-1)
def test_detect_nan():
# Test the code snippet example that detects NaN values.
nan_detected = [False]
def detect_nan(fgraph, i, node, fn):
for output in fn.outputs:
if np.isnan(output[0]).any():
print("*** NaN detected ***")
debugprint(node)
print("Inputs : %s" % [input[0] for input in fn.inputs])
print("Outputs: %s" % [output[0] for output in fn.outputs])
nan_detected[0] = True
break
x = dscalar("x")
f = function(
[x],
[log(x) * x],
mode=MonitorMode(post_func=detect_nan),
)
try:
old_stdout = sys.stdout
sys.stdout = StringIO()
f(0) # log(0) * 0 = -inf * 0 = NaN
finally:
sys.stdout = old_stdout
assert nan_detected[0]
def forward(self, x_):
x = x_.T
n = x.shape[0]
lx = aet.log(x)
shift = aet.sum(lx, 0, keepdims=True) / n
y = lx[:-1] - shift
return floatX(y.T)
def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
# This is basic test for GpuCrossentropySoftmaxArgmax1HotWithBias
# We check that we loop when their is too much threads
n_in = 1000
batch_size = 4097
n_out = 1250
if not isinstance(mode_with_gpu, aesara.compile.DebugMode):
n_in = 4098
n_out = 4099
y = tt.lvector("y")
b = tt.fvector("b")
# we precompute the dot with big shape before to allow the test of
# GpuCrossentropySoftmax1HotWithBiasDx to don't fail with the error
# (the launch timed out and was terminated) on GPU card not
# powerful enough. We need the big shape to check for corner
# case.
dot_result = tt.fmatrix("dot_result")
# Seed numpy.random with config.unittests.rseed
utt.seed_rng()
xx = np.asarray(np.random.rand(batch_size, n_in), dtype=np.float32)
yy = np.ones((batch_size, ), dtype="int32")
b_values = np.zeros((n_out, ), dtype="float32")
W_values = np.asarray(np.random.rand(n_in, n_out), dtype="float32")
dot_value = np.asarray(np.dot(xx, W_values), dtype="float32")
del W_values
p_y_given_x = tt.nnet.softmax(dot_result + b)
y_pred = tt.argmax(p_y_given_x, axis=-1)
loss = -tt.mean(tt.log(p_y_given_x)[tt.arange(y.shape[0]), y])
dW = tt.grad(loss, dot_result)
classify = aesara.function(inputs=[y, b, dot_result],
outputs=[loss, y_pred, dW],
mode=mode_without_gpu)
classify_gpu = aesara.function(inputs=[y, b, dot_result],
outputs=[loss, y_pred, dW],
mode=mode_with_gpu)
assert any([
isinstance(node.op, tt.nnet.CrossentropySoftmaxArgmax1HotWithBias)
for node in classify.maker.fgraph.toposort()
])
assert any([
isinstance(node.op, GpuCrossentropySoftmaxArgmax1HotWithBias)
for node in classify_gpu.maker.fgraph.toposort()
])
out = classify(yy, b_values, dot_value)
gout = classify_gpu(yy, b_values, dot_value)
assert len(out) == len(gout) == 3
utt.assert_allclose(out[0], gout[0])
utt.assert_allclose(out[2], gout[2], atol=3e-6)
utt.assert_allclose(out[1], gout[1])
def test_optimizer():
# Test that we can remove optimizer
nan_detected = [False]
def detect_nan(fgraph, i, node, fn):
for output in fn.outputs:
if np.isnan(output[0]).any():
print("*** NaN detected ***")
debugprint(node)
print("Inputs : %s" % [input[0] for input in fn.inputs])
print("Outputs: %s" % [output[0] for output in fn.outputs])
nan_detected[0] = True
break
x = dscalar("x")
mode = MonitorMode(post_func=detect_nan)
mode = mode.excluding("fusion")
f = function([x], [log(x) * x], mode=mode)
# Test that the fusion wasn't done
assert len(f.maker.fgraph.apply_nodes) == 2
try:
old_stdout = sys.stdout
sys.stdout = StringIO()
f(0) # log(0) * 0 = -inf * 0 = NaN
finally:
sys.stdout = old_stdout
# Test that we still detect the nan
assert nan_detected[0]
def forward(self, value, *inputs):
"""Inverse operation of softplus.
y = Log(Exp(x) - 1)
= Log(1 - Exp(-x)) + x
"""
return at.log(1.0 - at.exp(-value)) + value
def logsumexp(x, axis=None, keepdims=True):
# Adapted from https://github.com/Theano/Theano/issues/1563
x_max = aet.max(x, axis=axis, keepdims=True)
x_max = aet.switch(aet.isinf(x_max), 0, x_max)
res = aet.log(aet.sum(aet.exp(x - x_max), axis=axis,
keepdims=True)) + x_max
return res if keepdims else res.squeeze()
def __call__(self, X):
XY = X.dot(X.T)
x2 = at.sum(X**2, axis=1).dimshuffle(0, "x")
X2e = at.repeat(x2, X.shape[0], axis=1)
H = X2e + X2e.T - 2.0 * XY
V = at.sort(H.flatten())
length = V.shape[0]
# median distance
m = at.switch(
at.eq((length % 2), 0),
# if even vector
at.mean(V[((length // 2) - 1):((length // 2) + 1)]),
# if odd vector
V[length // 2],
)
h = 0.5 * m / at.log(floatX(H.shape[0]) + floatX(1))
# RBF
Kxy = at.exp(-H / h / 2.0)
# Derivative
dxkxy = -at.dot(Kxy, X)
sumkxy = at.sum(Kxy, axis=-1, keepdims=True)
dxkxy = at.add(dxkxy, at.mul(X, sumkxy)) / h
return Kxy, dxkxy
def forward(self, rv_var, rv_value):
"""Inverse operation of softplus.
y = Log(Exp(x) - 1)
= Log(1 - Exp(-x)) + x
"""
return at.log(1.0 - at.exp(-rv_value)) + rv_value
def marginal_mixture_logcdf(op, value, rng, weights, *components, **kwargs):
# single component
if len(components) == 1:
# Need to broadcast value across mixture axis
mix_axis = -components[0].owner.op.ndim_supp - 1
components_logcdf = logcdf(components[0],
at.expand_dims(value, mix_axis))
else:
components_logcdf = at.stack(
[logcdf(component, value) for component in components],
axis=-1,
)
mix_logcdf = at.logsumexp(at.log(weights) + components_logcdf, axis=-1)
# Squeeze stack dimension
# There is a Aesara bug in squeeze with negative axis
# https://github.com/aesara-devs/aesara/issues/830
# mix_logp = at.squeeze(mix_logp, axis=-1)
mix_logcdf = at.squeeze(mix_logcdf, axis=mix_logcdf.ndim - 1)
mix_logcdf = check_parameters(
mix_logcdf,
0 <= weights,
weights <= 1,
at.isclose(at.sum(weights, axis=-1), 1),
msg="0 <= weights <= 1, sum(weights) == 1",
)
return mix_logcdf
def logp(self, value):
"""
Calculate log-probability of defined Mixture distribution at specified value.
Parameters
----------
value: numeric
Value(s) for which log-probability is calculated. If the log probabilities for multiple
values are desired the values must be provided in a numpy array or Aesara tensor
Returns
-------
TensorVariable
"""
w = self.w
return bound(
logsumexp(at.log(w) + self._comp_logp(value),
axis=-1,
keepdims=False),
w >= 0,
w <= 1,
at.allclose(w.sum(axis=-1), 1),
broadcast_conditions=False,
)
def forward(self, x):
"""Inverse operation of softplus.
y = Log(Exp(x) - 1)
= Log(1 - Exp(-x)) + x
"""
return aet.log(1.0 - aet.exp(-x)) + x
def marginal_mixture_logcdf(op, value, rng, weights, *components, **kwargs):
# single component
if len(components) == 1:
# Need to broadcast value across mixture axis
mix_axis = -components[0].owner.op.ndim_supp - 1
components_logcdf = logcdf(components[0],
at.expand_dims(value, mix_axis))
else:
components_logcdf = at.stack(
[logcdf(component, value) for component in components],
axis=-1,
)
mix_logcdf = at.logsumexp(at.log(weights) + components_logcdf, axis=-1)
mix_logcdf = check_parameters(
mix_logcdf,
0 <= weights,
weights <= 1,
at.isclose(at.sum(weights, axis=-1), 1),
msg="0 <= weights <= 1, sum(weights) == 1",
)
return mix_logcdf
def local_log_pow(node):
if node.op == tensor.log:
(x, ) = node.inputs
if x.owner and x.owner.op == tensor.pow:
base, exponent = x.owner.inputs
# TODO: reason to be careful with dtypes?
return [exponent * tensor.log(base)]
def normal_lccdf(mu, sigma, x):
z = (x - mu) / sigma
return aet.switch(
aet.gt(z, 1.0),
aet.log(aet.erfcx(z / aet.sqrt(2.0)) / 2.0) - aet.sqr(z) / 2.0,
aet.log1p(-aet.erfc(-z / aet.sqrt(2.0)) / 2.0),
)
def logpow(x, m):
"""
Calculates log(x**m) since m*log(x) will fail when m, x = 0.
"""
# return m * log(x)
return aet.switch(aet.eq(x, 0), aet.switch(aet.eq(m, 0), 0.0, -np.inf),
m * aet.log(x))
def logp(value, n, p):
return check_parameters(
factln(n) - factln(value).sum() + (value * at.log(p)).sum(),
at.all(value >= 0),
at.all(0 <= p),
at.all(p <= 1),
at.isclose(p.sum(), 1),
)
def normal_lcdf(mu, sigma, x):
"""Compute the log of the cumulative density function of the normal."""
z = (x - mu) / sigma
return aet.switch(
aet.lt(z, -1.0),
aet.log(aet.erfcx(-z / aet.sqrt(2.0)) / 2.0) - aet.sqr(z) / 2.0,
aet.log1p(-aet.erfc(z / aet.sqrt(2.0)) / 2.0),
)
def simple_2model_continuous():
mu = -2.1
tau = 1.3
with Model() as model:
x = pm.Normal("x", mu, tau=tau, initval=0.1)
pm.Deterministic("logx", at.log(x))
pm.Beta("y", alpha=1, beta=1, size=2)
return model.initial_point, model
